Electric wheelchair shock-absorbing suspension control method and device

Abstract

The present application provides a method and device for controlling a shock-absorbing suspension of an electric wheelchair, which relates to the technical field of electric wheelchair control. The key points of the technical solution are: obtaining road surface information collected based on the mechanical response of the front wheel of the electric wheelchair, and predicting the road conditions that the rear wheel will encounter based on the road surface information and the distance between the front and rear wheels of the electric wheelchair; calculating and controlling the optimal position of the movable weight under the electric wheelchair seat and the optimal parameters of the rear wheel shock absorber according to the predicted road conditions, wherein the adjustment of the movable weight is used to optimize the distribution of the center of gravity of the wheelchair, and the adjustment of the rear wheel shock absorber is completed before the rear wheel contacts the uneven road surface; real-time monitoring of the wheelchair posture and user comfort, and dynamically adjusting the position of the movable weight and the parameters of the rear wheel shock absorber according to the monitoring results. The method and device for controlling a shock-absorbing suspension of an electric wheelchair provided in the present application have the advantages of improving the comfort and stability of the electric wheelchair when driving on uneven roads.

Description

Technical Field

[0001] The present application relates to the technical field of electric wheelchair control, and in particular to a method and device for controlling a shock-absorbing suspension of an electric wheelchair. Background Art

[0002] As an important tool for disabled people's daily travel, the comfort and stability of electric wheelchairs directly affect the quality of life of users. However, in actual use, when electric wheelchairs transition from flat roads to uneven roads, they often encounter the problem of poor shock absorption. Take a disabled user driving an electric wheelchair from a flat sidewalk to an uneven park path as an example. When the front wheel of the wheelchair first contacts the uneven road surface, the shock absorption system will immediately respond and adjust the shock absorption parameters of the front wheel. However, due to the distance between the front and rear wheels, the rear wheel has not yet contacted the uneven road surface, resulting in the shock absorption system being unable to adjust the parameters of the rear wheel at the same time. This time difference in the shock absorption response of the front and rear wheels will cause the wheelchair to tilt briefly, and the user will instinctively change his sitting posture in order to maintain balance. The change in sitting posture will cause the center of gravity of the wheelchair to change, which in turn affects the working effect of the shock absorption system, forming a circular reaction.

[0003] For disabled users who lack leg muscle support, this constant vibration and posture adjustment can easily lead to fatigue and discomfort, especially when they need to engage in long-term outdoor activities. This situation not only affects the user's comfort, but may also have potential negative effects on their physical health. For example, long-term vibration may aggravate spinal problems of some disabled users, while frequent posture adjustments may lead to muscle fatigue and increased joint pressure.

[0004] In view of the above problems, the existing technology needs to be improved urgently. Summary of the invention

[0005] The purpose of the present application is to provide a method and device for controlling a shock-absorbing suspension of an electric wheelchair, which has the advantage of improving the comfort and stability of the electric wheelchair when traveling on uneven roads.

[0006] The present application provides a method for controlling a shock-absorbing suspension of an electric wheelchair, and the technical solution is as follows:

[0007] The method includes: obtaining road surface information collected based on the mechanical response of the front wheels of the electric wheelchair, and predicting the road conditions that the rear wheels will encounter based on the road surface information and the distance between the front and rear wheels of the electric wheelchair; calculating and controlling the optimal position of a movable weight under the electric wheelchair seat and the optimal parameters of a rear wheel shock absorber according to the predicted road conditions, wherein the adjustment of the movable weight is used to optimize the distribution of the center of gravity of the wheelchair, and the adjustment of the rear wheel shock absorber is completed before the rear wheel contacts the uneven road surface; real-time monitoring of the wheelchair posture and user comfort, and dynamically adjusting the position of the movable weight and the parameters of the rear wheel shock absorber according to the monitoring results.

[0008] Furthermore, the present application also proposes that the steps of calculating and controlling the optimal position of the movable weight under the electric wheelchair seat and the optimal parameters of the rear wheel shock absorber according to the predicted road conditions include: obtaining road surface unevenness data in the predicted road conditions based on the road surface information collected by the front wheel; obtaining the current driving speed data of the electric wheelchair; calculating the optimal position of the movable weight and the optimal parameters of the rear wheel shock absorber based on the road surface unevenness data and the driving speed data; and also including: dividing the movable weight into multiple units and calculating the moving time interval of each unit; controlling each unit to move to the corresponding optimal position according to the moving time interval; monitoring the wheelchair posture and user comfort in real time during the movement of each unit; when the wheelchair posture or user comfort exceeds the preset range, dynamically adjusting the movement strategy of subsequent units and the rear wheel shock absorber parameters to optimize the wheelchair center of gravity distribution and shock absorption effect.

[0009] Furthermore, the present application also proposes that the steps of dividing the movable heavy object into multiple units and calculating the moving time interval of each unit include: obtaining the current position and target position of each unit; determining the moving priority of each unit according to the degree of influence of the position change of each unit on the center of gravity of the wheelchair; calculating the moving time interval between adjacent units based on the moving priority, the wheelchair driving speed and the predicted road conditions; and also includes: before each unit moves, obtaining the real-time inclination angle and vertical acceleration of the wheelchair; when the inclination angle or vertical acceleration exceeds a preset comfort threshold, extending the moving time interval of the unit; and generating the moving timing control instructions of each unit according to the calculated moving time interval.

[0010] Furthermore, the present application also proposes that the step of controlling each unit to move to the corresponding optimal position according to the moving time interval includes: obtaining the current position and target position of each unit; calculating the moving distance of each unit according to the current position and target position; determining the initial moving speed of each unit based on the moving distance and the current driving speed of the electric wheelchair; during the movement of each unit, obtaining the inclination angle and vertical acceleration data of the wheelchair in real time; when the inclination angle or vertical acceleration data exceeds a preset comfort threshold, calculating the remaining distance for the movable weight to reach the optimal position; dividing the remaining distance into multiple equidistant moving intervals; in each moving interval, controlling the movable weight to complete the position adjustment at a decreasing moving speed, wherein the moving speed of each interval is a preset percentage of the speed of the previous interval.

[0011] Furthermore, the present application also proposes that, within each moving interval, the step of controlling the movable weight to complete position adjustment at a decreasing moving speed includes: obtaining the current inclination angle and vertical acceleration data of the electric wheelchair; calculating the maximum allowable moving speed of the movable weight under the current road conditions based on the inclination angle and vertical acceleration data, combined with a preset comfort threshold; when the remaining distance is divided into three equidistant moving intervals; within the first moving interval, controlling the movement of the movable weight at the maximum allowable moving speed; within the second moving interval, controlling the movement of the movable weight at 70% of the maximum allowable moving speed; within the third moving interval, controlling the movement of the movable weight at 40% of the maximum allowable moving speed; when it is detected that the wheelchair inclination angle or vertical acceleration exceeds the preset comfort threshold, reducing the moving speed of the current moving interval to 50% of the previous level moving speed.

[0012] Furthermore, the present application also proposes that the step of dividing the remaining distance into multiple equidistant moving intervals includes: obtaining the current driving speed and predicted road condition information of the electric wheelchair; calculating the adjustment time required for the movable weight to reach the optimal position based on the current driving speed, predicted road condition information and the remaining distance of the movable weight; determining the number of moving intervals based on the adjustment time and the shock absorption response characteristics of the electric wheelchair; and evenly dividing the remaining distance into the determined number of equidistant moving intervals.

[0013] Furthermore, the present application also proposes that the method also includes: obtaining real-time data of the front wheels, rear wheel shock absorbers and movable weights of the electric wheelchair; calculating the complexity index of the current road conditions based on the real-time data; dynamically adjusting the sampling frequency and data processing priority of the front wheels based on the complexity index; when it is detected that the road condition complexity index exceeds a preset threshold, starting a high-frequency sampling mode to improve data processing accuracy; and also includes: allocating the front wheel data and rear wheel shock absorber parameters to two independent microprocessor units for parallel processing; using a data fusion algorithm to integrate the front wheel data and rear wheel shock absorber parameters to generate a road condition prediction model; and calculating the optimal adjustment strategy for the movable weight based on the road condition prediction model.

[0014] Furthermore, the present application also proposes that the step of calculating the optimal adjustment strategy for the movable weight according to the road condition prediction model includes: obtaining the road surface unevenness data and the wheelchair travel speed data in the road condition prediction model; calculating the expected tilt angle and vertical acceleration of the electric wheelchair based on the road surface unevenness data and the wheelchair travel speed data; determining the adjustment range of the movable weight according to a preset user comfort threshold; calculating the position change of the movable weight within the adjustment range to minimize the expected tilt angle and vertical acceleration while optimizing energy consumption; generating an adjustment instruction sequence including the position change of the movable weight and the execution timing; sending the adjustment instruction sequence to the drive device of the movable weight, and executing it synchronously with the adjustment of the rear wheel shock absorber parameters.

[0015] Furthermore, the present application also proposes that the step of calculating the optimal position of the movable weight and the optimal parameters of the rear wheel shock absorber based on the road surface roughness data and the driving speed data includes: obtaining the system state vector x(k) of the electric wheelchair, including the wheelchair inclination angle x1, the wheelchair center of gravity position x2 and the wheelchair vertical acceleration x3; obtaining the control input vector u(k), including the position of the movable weight and the parameters of the rear wheel shock absorber; obtaining the measurable interference vector d(k), including the predicted road surface roughness and the wheelchair driving speed; and establishing a state space model of the electric wheelchair shock absorption system: x(k+1)= A(k)x(k) + B(k)u(k) + E(k)d(k) + w(k), y(k) = C(k)x(k) + v(k), where y(k) is the observed output vector, including the measured wheelchair tilt angle and the measured wheelchair vertical acceleration; A(k), B(k), C(k) and E(k) are time-varying system matrices; w(k) and v(k) are system noise and observation noise, respectively; construct the predictive control objective function: J = Σ(w1 * x1^2 + w2 * x2^2 + w3 * x3^2 ​​+ w4 * Σ ui^2 + w5 * Σ Δui^2), where w1, w2, w3, w4 and w5 are weight coefficients, Δui is the rate of change of the control input, and ui is the moving distance of each unit of the movable weight; use the adaptive law for parameter estimation: θ(k+1) = θ(k) + γ * Φ(k) * e(k), wherein θ(k) is the parameter vector to be estimated, γ is the learning rate, Φ(k) is the regression vector, and e(k) is the prediction error; according to the state space model, the predictive control objective function and the adaptive law, the optimal position of the movable weight and the optimal parameters of the rear wheel shock absorber are calculated and updated in real time; the calculated optimal position and optimal parameters are applied to the shock absorption system of the electric wheelchair to realize dynamic adjustment of the position of the movable weight and the parameters of the rear wheel shock absorber.

[0016] Furthermore, the present application also proposes an electric wheelchair shock-absorbing suspension control device, which includes: a prediction module, used to obtain road surface information collected based on the mechanical response of the front wheel of the electric wheelchair, and predict the road conditions that the rear wheel will encounter based on the road surface information and the distance between the front and rear wheels of the electric wheelchair; a control module, used to calculate and control the optimal position of a movable weight under the electric wheelchair seat and the optimal parameters of the rear wheel shock absorber according to the predicted road conditions, wherein the adjustment of the movable weight is used to optimize the distribution of the center of gravity of the wheelchair, and the adjustment of the rear wheel shock absorber is completed before the rear wheel contacts the uneven road surface; an adjustment module, used to monitor the wheelchair posture and user comfort in real time, and dynamically adjust the position of the movable weight and the parameters of the rear wheel shock absorber according to the monitoring results.

[0017] From the above, it can be seen that the electric wheelchair shock-absorbing suspension control method and device provided in the present application predicts the rear wheel road condition by acquiring the road surface information collected by the front wheel, and adjusts the position of the movable weight and the rear wheel shock absorber parameters in advance according to the prediction results, while monitoring the wheelchair posture and user comfort in real time for dynamic adjustment, thereby effectively solving the problem of poor shock absorption effect of electric wheelchairs when driving on uneven roads in the prior art, and has the advantage of improving the comfort and stability of electric wheelchairs when driving on uneven roads. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A flow chart of a shock-absorbing suspension control method for an electric wheelchair provided in this application.

[0019] Figure 2 A schematic structural diagram of a shock-absorbing suspension control device for an electric wheelchair provided in this application.

[0020] In the figure: 210, prediction module; 220, control module; 230, adjustment module. DETAILED DESCRIPTION

[0021] The technical solutions in the present application will be clearly and completely described below in conjunction with the drawings in the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. The components of the present application generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the present application for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0022] Electric wheelchairs often face the problem of poor shock absorption when transitioning from flat to uneven roads. Specifically, when the front wheel of the electric wheelchair first contacts the uneven road surface, the shock absorption system will immediately respond and adjust the shock absorption parameters of the front wheel. However, due to the distance between the front and rear wheels, the rear wheel has not yet contacted the uneven road surface, resulting in the shock absorption system being unable to adjust the parameters of the rear wheel at the same time. This time difference in the shock absorption response of the front and rear wheels will cause the wheelchair to tilt briefly, and the user will instinctively change their sitting posture to maintain balance. The change in sitting posture will cause the center of gravity of the wheelchair to change, which in turn affects the working effect of the shock absorption system, forming a circular reaction. This problem directly affects the stability of the electric wheelchair and the comfort of the user, especially for users who lack leg muscle support. Continuous vibration and posture adjustment can easily lead to fatigue and discomfort.

[0023] If this technical problem is not solved, the effectiveness of the shock absorption system will be greatly reduced and its design function will not be fully utilized. Secondly, the dynamic stability of the wheelchair will be significantly affected, increasing the risk of tipping over. In addition, continuous vibration and posture adjustment will accelerate the wear of the wheelchair's mechanical parts and shorten the life of the equipment. From the user experience perspective, long-term discomfort may cause increased user fatigue and even cause health problems. These problems not only affect the single-use experience, but also reduce the overall usability and reliability of the electric wheelchair.

[0024] The problem of poor shock absorption when the electric wheelchair transitions from a flat road to an uneven road is due to the interaction between the time difference in shock absorption response of the front and rear wheels and the change in the user's center of gravity. To solve this problem, this application conducts in-depth technical analysis and solution exploration.

[0025] First, considering the fact that the front wheels contact the uneven road surface before the rear wheels, this application proposes to use the mechanical response of the front wheels to collect road surface information. This method can obtain road condition data in a timely manner and provide a basis for subsequent prediction and control. However, it is not enough to just obtain the front wheel information, and it is also necessary to consider how to use this information to improve the shock absorption effect of the rear wheels.

[0026] The key issue is how to prepare for shock absorption before the rear wheel contacts the uneven road surface. By analyzing the distance relationship between the front and rear wheels, this application proposes a method for predicting the road conditions that the rear wheel will encounter based on the front wheel information. This predictive method can provide the rear wheel shock absorption system with an opportunity to react in advance, thereby overcoming the lag of the traditional passive shock absorption system.

[0027] After predicting the rear wheel road conditions, the next step is to consider how to adjust the shock absorption system to cope with the upcoming road surface changes. This application proposes two key adjustment aspects: one is to control the position of the movable weight under the electric wheelchair seat, and the other is to adjust the parameters of the rear wheel shock absorber.

[0028] The adjustment of the movable weight is designed to optimize the distribution of the center of gravity of the wheelchair. The innovation of this method is to actively adjust the center of gravity of the wheelchair, rather than passively responding to the center of gravity shift caused by changes in the user's posture. By precisely controlling the position of the weight, the wheelchair can be kept in the best balance state under different road conditions, improving overall stability.

[0029] At the same time, the adjustment of the rear wheel shock absorber parameters is directly targeted at the upcoming road surface changes. This application emphasizes that this adjustment should be completed before the rear wheel contacts the uneven road surface, achieving true active shock absorption. This advance adjustment method can significantly reduce the bump problem caused by the response lag of the traditional shock absorption system.

[0030] However, the preset adjustment may not be able to fully adapt to the complex and changing actual situation. To this end, the present application further proposes a method for real-time monitoring of wheelchair posture and user comfort.

[0031] Therefore, refer to Figure 1 , the present application proposes a method for controlling a shock-absorbing suspension of an electric wheelchair, the method comprising:

[0032] S110, acquiring road surface information collected based on the mechanical response of the front wheel of the electric wheelchair, and predicting the road conditions that the rear wheel will encounter based on the road surface information and the distance between the front and rear wheels of the electric wheelchair;

[0033] S120, calculating and controlling the optimal position of the movable weight under the electric wheelchair seat and the optimal parameters of the rear wheel shock absorber according to the predicted road conditions, wherein the adjustment of the movable weight is used to optimize the distribution of the center of gravity of the wheelchair, and the adjustment of the rear wheel shock absorber is completed before the rear wheel contacts the uneven road surface;

[0034] S130, monitor the wheelchair posture and user comfort in real time, and dynamically adjust the position of the movable weight and the parameters of the rear wheel shock absorber according to the monitoring results.

[0035] Among them, the road surface information collected based on the mechanical response of the front wheel of the electric wheelchair refers to the road condition data obtained by the sensor installed on the front wheel, which can be specifically implemented by an acceleration sensor or a displacement sensor.

[0036] Among them, predicting the road conditions that the rear wheels are about to encounter refers to calculating the road conditions that the rear wheels are about to pass through based on the road surface information collected by the front wheels and the distance between the front and rear wheels of the wheelchair. This can be achieved specifically by using an interpolation algorithm or a machine learning model.

[0037] Among them, the movable weight refers to a weight device with a controllable position installed under the electric wheelchair seat, and the position adjustment can be achieved by using an electric slider or a hydraulic cylinder.

[0038] Among them, the optimal parameters of the rear wheel shock absorber refer to the shock absorber settings that can minimize vibrations, which can be achieved by using adjustable damping coefficients or spring stiffnesses.

[0039] Among them, real-time monitoring of wheelchair posture and user comfort refers to continuously obtaining the wheelchair's tilt angle, acceleration and user's physiological response data, which can be achieved by using gyroscopes, accelerometers and pressure sensors.

[0040] The core innovation of this application is to propose a predictive and adaptive electric wheelchair shock absorption control method. This method senses the road conditions through the front wheels and predicts the situation that the rear wheels will encounter, thereby achieving advance adjustment of the rear wheel shock absorption system. At the same time, a movable weight is introduced to dynamically optimize the distribution of the wheelchair's center of gravity, combined with a real-time monitoring and adjustment mechanism to form a closed-loop control system. This method not only solves the problem of delayed response of the traditional shock absorption system, but can also actively adapt to different road conditions and user needs.

[0041] The working principle of this application can be described in detail as follows:

[0042] First, a high-precision acceleration sensor is installed on the front wheel of the electric wheelchair to collect road information in real time to ensure that subtle changes in the road surface can be captured. The collected data is filtered and transmitted to the central control unit.

[0043] After the central control unit receives the front wheel road surface information, it combines the pre-stored wheelchair front and rear wheel distance data (usually 50-70cm) and uses an interpolation algorithm to predict the road conditions that the rear wheel will encounter.

[0044] Based on the predicted road conditions, the control unit calculates the optimal position of the movable weight and the optimal parameters of the rear wheel shock absorber. The movable weight is controlled by a high-precision stepper motor and can be moved in the front-to-back direction under the seat. The rear wheel shock absorber adopts an electronically controlled hydraulic damping system, and its damping coefficient can be continuously adjusted within a preset range.

[0045] Based on the calculation results, the control unit sends instructions to the stepper motor of the movable weight and the electronic control unit of the rear wheel shock absorber before the rear wheel contacts the predicted uneven road surface. This time window can be optimized through experiments to ensure timely adjustment and avoid unnecessary vibrations that may be caused by premature adjustment.

[0046] The gyroscope and accelerometer installed on the wheelchair continuously monitor the posture of the wheelchair. The pressure sensor array on the seat monitors the user's center of gravity changes and pressure distribution in real time to assess the user's comfort.

[0047] The control unit can use fuzzy control algorithms to dynamically adjust the position of the movable weight and the parameters of the rear wheel shock absorber based on real-time monitoring data.

[0048] The system completes pre-adjustment before the rear wheels touch the uneven road surface, and continuously optimizes the shock absorption effect when passing through the uneven road surface. By actively adjusting the center of gravity and shock absorption parameters of the wheelchair, the system can effectively reduce the vibration felt by the user and improve the stability and comfort of the wheelchair.

[0049] For example, this method is used on an electric wheelchair whose front wheels are equipped with acceleration sensors and the distance between the front and rear wheels is 60 cm.

[0050] The movable weight is a 5kg metal block controlled by a stepper motor and can move within a preset range.

[0051] The rear wheel shock absorber uses a magnetorheological fluid damper, which can be continuously adjusted within a preset range.

[0052] The attitude monitoring system can use IMU, and the pressure sensor uses a flexible pressure sensor array.

[0053] The system workflow is as follows:

[0054] When the wheelchair enters an uneven road from a flat road at a speed of 1 m / s, the front wheel detects a sudden change in vertical acceleration, for example, from 0.1g to 0.5g.

[0055] After receiving this information, the control unit immediately starts the prediction algorithm. Based on the 60cm wheelbase and 1m / s speed, the system predicts that the rear wheels will encounter similar road conditions in 0.6 seconds.

[0056] The control unit calculates that the optimal weight position should be moved 3 cm backwards and the rear wheel shock absorber damping coefficient should be increased to 3200 N·s / m.

[0057] Within the predicted 0.6 seconds, the system first controls the stepper motor to move the weight backward by 3 cm, and at the same time gradually increases the damping coefficient of the rear wheel shock absorber to 3200 N·s / m.

[0058] When the rear wheels contact an uneven road surface, the IMU detects that the wheelchair's pitch angle changes by no more than 2°, and the vertical acceleration peak is controlled within 0.3g, which is lower than the 5° and 0.7g of traditional systems.

[0059] When passing through uneven roads, the pressure sensor detected that the user's center of gravity moved slightly forward. The system responded immediately and moved the weight back another 1 cm to further optimize the center of gravity distribution.

[0060] During the entire process, the system continuously monitors the wheelchair posture and user comfort, and makes fine adjustments every 100ms to ensure continuous optimization of the shock absorption effect.

[0061] Through this predictive and adaptive control approach, the electric wheelchair is able to significantly reduce vibrations, improve stability and user comfort when transitioning from flat to uneven surfaces.

[0062] In some of the above-mentioned embodiments, during the implementation of the present application, there is still the problem of how to accurately control the position of the movable weight under the electric wheelchair seat and the parameters of the rear wheel shock absorber according to the predicted road conditions to optimize the wheelchair's center of gravity distribution and shock absorption effect.

[0063] In this regard, the present application further proposes to obtain road surface roughness data in the predicted road conditions based on road surface information collected by the front wheels; obtain the current driving speed data of the electric wheelchair; calculate the optimal position of the movable weight and the optimal parameters of the rear wheel shock absorber based on the road surface roughness data and the driving speed data; divide the movable weight into multiple units, and calculate the moving time interval of each unit; control each unit to move to the corresponding optimal position according to the moving time interval; during the movement of each unit, monitor the wheelchair posture and user comfort in real time; when the wheelchair posture or user comfort exceeds the preset range, dynamically adjust the movement strategy of subsequent units and the parameters of the rear wheel shock absorber to optimize the wheelchair center of gravity distribution and shock absorption effect.

[0064] The technical solution of this application effectively solves the shock absorption problem of electric wheelchairs on uneven roads by combining predictive control with real-time feedback. The innovation of this solution lies in combining the control of movable weight units with real-time monitoring, achieving a more precise and flexible shock absorption effect, and significantly improving the comfort and stability of electric wheelchairs.

[0065] Specifically, the technical solution of this application includes the following key steps:

[0066] First, the road surface information is collected through the front wheels to obtain the road surface unevenness data in the predicted road conditions.

[0067] Secondly, obtain the current driving speed data of the electric wheelchair. This can be achieved through the speed sensor or motor control system of the wheelchair.

[0068] Next, based on the road surface roughness data and driving speed data, the optimal position of the movable weight and the optimal parameters of the rear wheel shock absorber are calculated. Multiple factors can be considered, such as the structural characteristics of the wheelchair, the user's weight distribution, and the expected shock absorption effect.

[0069] The movable weight is divided into multiple units and the movement time interval of each unit is calculated. This design allows for more precise control and avoids sudden changes in the center of gravity that may be caused by overall movement. The division of units can be based on the structural characteristics or weight distribution of the weight. The calculation of the movement time interval needs to take into account the speed of road conditions, the speed of the wheelchair, and the impact of each unit's movement on the overall balance.

[0070] According to the calculated movement time interval, each unit is controlled to move to the corresponding optimal position. This step can use electric push rods or linear motors to ensure that each unit can move according to the predetermined time and position.

[0071] During each unit movement, the wheelchair posture and user comfort are monitored in real time. This can be achieved through posture sensors and pressure sensors installed on the wheelchair. The posture sensor can detect the tilt angle and acceleration of the wheelchair, while the pressure sensor can monitor the pressure distribution of the user's seat to evaluate the user's comfort.

[0072] When the wheelchair posture or user comfort level is detected to be outside the preset range, the system will dynamically adjust the movement strategy of the subsequent units and the parameters of the rear wheel shock absorbers. This real-time adjustment mechanism ensures that the system can respond to emergencies or prediction errors, and further optimize the center of gravity distribution and shock absorption effect of the wheelchair.

[0073] This application combines predictive control with real-time feedback, which can not only respond to changes in road conditions in advance, but also dynamically adjust according to actual conditions, thereby effectively solving the shock absorption problem of electric wheelchairs on uneven roads. The innovation of this solution lies in combining the control of movable weight units with real-time monitoring, achieving a more precise and flexible shock absorption effect, and significantly improving the comfort and stability of electric wheelchairs.

[0074] As a specific embodiment, the electric wheelchair shock-absorbing suspension control method of the present application can be performed according to the following steps:

[0075] The front wheels collect road information: the road surface unevenness data is collected through the mechanical response of the front wheels.

[0076] Obtaining electric wheelchair speed data: The speed data is collected through the Hall sensor installed on the motor shaft.

[0077] Calculate optimal parameters: Use a pre-trained neural network model, input road surface unevenness data and speed data, and output the optimal position coordinates (X, Y, Z) of the movable weight and the optimal damping coefficient of the rear wheel shock absorber.

[0078] Divide the weight unit: Divide the movable weight under the seat (for example, a total weight of 20kg) into four 5kg units. Each unit is controlled by an independent electric push rod.

[0079] Calculate the movement time interval: Based on the speed of the wheelchair and the distance between the front and rear wheels, set the first unit to start moving after 1.5 seconds, and then start the movement of one unit every 0.5 seconds.

[0080] Control unit movement: Use PID control algorithm to accurately control the movement speed and position of each unit.

[0081] Real-time monitoring: A three-axis accelerometer is used to monitor the wheelchair posture. At the same time, a pressure sensor matrix (e.g., 16x16 grid) on the seat is used to monitor the user's pressure distribution.

[0082] Dynamic adjustment: When it is detected that the wheelchair tilt angle exceeds 5 degrees or the vertical acceleration exceeds 0.5g, the movement speed of the subsequent units is immediately adjusted to reduce the original speed by 30%. At the same time, the damping coefficient of the rear wheel shock absorber is adjusted and increased by 20% to provide a stronger shock absorption effect.

[0083] Through this precise control and real-time adjustment, the technical solution of the present application can optimize the center of gravity distribution and shock absorption parameters in advance when the electric wheelchair encounters uneven roads, thereby significantly reducing the impact of vibration on the user.

[0084] In addition, the technical solution of the present application realizes advance response to upcoming road conditions through predictive collection of the front wheels and coordinated control of multiple units of movable weights. This predictive control greatly reduces the short-term bumps caused by reaction lag in traditional systems. At the same time, the innovative design of dividing the movable weight into multiple units for control avoids the discomfort that may be caused by sudden changes in the overall center of gravity, making the shock absorption process smoother and more refined.

[0085] In some of the above-mentioned embodiments, during the implementation of the present application, there is still a technical problem of how to solve the technical problem of accurately controlling the movement timing of the movable weight during the shock-absorbing suspension control of the electric wheelchair to achieve smooth and efficient center of gravity adjustment.

[0086] In this regard, the present application further proposes to obtain the current position and target position of each unit; determine the movement priority of each unit according to the degree of influence of the position change of each unit on the center of gravity of the wheelchair; calculate the movement time interval between adjacent units based on the movement priority, the wheelchair's driving speed and the predicted road conditions; before each unit moves, obtain the real-time inclination angle and vertical acceleration of the wheelchair; when the inclination angle or vertical acceleration exceeds the preset comfort threshold, extend the movement time interval of the unit; and generate the movement timing control instructions of each unit according to the calculated movement time interval.

[0087] The technical solution of this application effectively solves the problems of unstable center of gravity adjustment and low efficiency that may occur in the shock absorption process of electric wheelchairs through refined timing control and dynamic adjustment mechanism. Compared with the traditional one-time adjustment or simple segmented adjustment method, this application can better adapt to complex and changeable road conditions and user needs, and improve the shock absorption effect and user comfort of electric wheelchairs.

[0088] Specifically, the present application first obtains the current position and target position of each unit, which provides basic data for subsequent calculation and control. The position information can be obtained in a variety of ways, such as using a position sensor, an encoder, or through feedback information from a motor control system.

[0089] Next, the present application determines the movement priority of each unit according to the degree of influence of the position change of each unit on the center of gravity of the wheelchair. This step can be achieved by establishing a mathematical model that takes into account the mass, position and influence of each unit's movement on the overall center of gravity. For example, the degree of influence can be calculated using the following formula:

[0090] Impact degree = (unit mass * position change distance) / total mass.

[0091] Based on the calculated degree of influence, the units can be sorted and their movement priorities determined. This ensures that the units with the greatest influence on the wheelchair's center of gravity are adjusted first, thereby more effectively controlling the balance of the wheelchair.

[0092] Then, the application calculates the moving time interval between adjacent units based on the moving priority, wheelchair speed and predicted road conditions. This step takes into account multiple factors to ensure the smoothness and efficiency of the moving process. The following formula can be used to calculate the time interval:

[0093] Time interval = basic interval + speed factor * driving speed + road condition factor * road condition complexity.

[0094] Among them, the basic interval can be set according to the system response time, and the speed factor and road condition factor need to be optimized through experimental data.

[0095] Before each unit moves, the present application obtains the real-time tilt angle and vertical acceleration of the wheelchair. This can be achieved through the gyroscope and acceleration sensor installed on the wheelchair. When the tilt angle or vertical acceleration exceeds the preset comfort threshold, the present application will extend the movement time interval of the unit to avoid additional disturbances by extending the movement time interval of the unit. This dynamic adjustment mechanism ensures that effective center of gravity adjustment can be achieved while ensuring user comfort.

[0096] For example, the tilt angle threshold can be set to 3 degrees and the vertical acceleration threshold can be set to 0.5g. When these thresholds are detected to be exceeded, the time interval can be extended according to the following formula:

[0097] Extension time = Base extension time * (1 + percentage above threshold).

[0098] Among them, the basic extension time is a preset value.

[0099] Finally, the present application generates movement timing control instructions for each unit according to the calculated movement time interval, and these instructions may include information such as the movement start time, movement speed, and movement distance of each unit.

[0100] As a preferred implementation, the present application can be implemented in a control system for an electric wheelchair. The system includes a central processor, multiple sensors (such as position sensors, gyroscopes, acceleration sensors, etc.) and actuators (such as motor drivers). The system workflow is as follows:

[0101] After the system starts, the current position and target position of all movable weight units are obtained first.

[0102] The impact of the position change of each unit on the center of gravity of the wheelchair is calculated, and the movement priority is determined according to the degree of impact.

[0103] Get the current wheelchair speed and predicted road condition information.

[0104] The moving time interval between units is calculated based on moving priority, driving speed and predicted road conditions.

[0105] Before each unit starts moving, check the wheelchair's tilt angle and vertical acceleration.

[0106] If the tilt angle or vertical acceleration is detected to exceed a preset threshold, the movement time interval of the unit is dynamically adjusted.

[0107] Generate control instructions containing movement timing, speed and distance information.

[0108] The control instructions are sent to the corresponding actuators to achieve precise control of the movable weight.

[0109] In this way, the present application can dynamically adjust the movement strategy according to the real-time monitored wheelchair status, rather than simply executing a preset movement sequence. This enables the system to better cope with complex and changeable road conditions. By calculating the degree of influence and movement time interval of each unit, the present application achieves precise control of movable heavy objects and avoids sudden changes in the center of gravity that may occur in traditional methods. The present application effectively ensures the user's riding comfort by monitoring the wheelchair posture and user comfort in real time and extending the movement time interval when necessary.

[0110] In some of the above-mentioned embodiments, during the implementation of the present application, there is still the problem of how to accurately control the position adjustment of the movable weight during the shock absorption process of the electric wheelchair to optimize the center of gravity distribution and shock absorption effect of the wheelchair.

[0111] In this regard, the present application further proposes to obtain the current position and target position of each unit; calculate the moving distance of each unit based on the current position and target position; determine the initial moving speed of each unit based on the moving distance and the current driving speed of the electric wheelchair; during the movement of each unit, obtain the inclination angle and vertical acceleration data of the wheelchair in real time; when the inclination angle or vertical acceleration data exceeds the preset comfort threshold, calculate the remaining distance for the movable weight to reach the optimal position; divide the remaining distance into multiple equidistant moving intervals; in each moving interval, control the movable weight to complete the position adjustment at a decreasing moving speed, wherein the moving speed of each interval is a preset percentage of the speed of the previous interval.

[0112] The technical solution of the present application achieves the optimization of the center of gravity and the improvement of the shock absorption effect during the shock absorption process of the electric wheelchair by accurately controlling the position adjustment of the movable weight. The solution first obtains the position information of each unit, calculates the moving distance, and determines the initial moving speed according to the driving speed of the wheelchair. During the movement process, the wheelchair status is monitored in real time and the movement strategy is dynamically adjusted to ensure the stability and comfort of the shock absorption process.

[0113] Specifically, the technical solution of this application includes the following key features:

[0114] First, the current position and target position of each unit are obtained. This can be achieved in a variety of ways, such as using high-precision position sensors or encoders to track the position of each unit in real time.

[0115] Next, calculate the distance each unit has to move. This can be done with simple math: subtract the current position from the target position.

[0116] Then, determine the initial moving speed of each unit. Here, you can consider a variety of factors, such as the current speed of the wheelchair, road conditions, etc. For example, you can set a basic speed and then dynamically adjust it according to the change of the wheelchair's speed. For example, the faster the wheelchair travels, the faster the initial moving speed of each unit.

[0117] During the movement, the inclination angle and vertical acceleration data of the wheelchair are obtained in real time. This can be achieved by installing a gyroscope and an acceleration sensor on the wheelchair.

[0118] When the tilt angle or vertical acceleration is detected to exceed the preset comfort threshold, the system will recalculate the remaining distance and perform segmented control. This dynamic adjustment strategy can effectively respond to emergencies and improve the adaptability of the system.

[0119] An innovative point of this scheme is to divide the remaining distance into multiple equidistant moving intervals. This partitioning strategy allows the system to use different moving speeds in different intervals, thus achieving smoother and more precise position adjustment.

[0120] In each moving interval, the movable weight is controlled to complete the position adjustment at a decreasing moving speed. This decreasing speed control method can effectively reduce the impact of sudden center of gravity changes on the stability of the wheelchair. For example, the moving speed of each interval can be set to 80% of the speed of the previous interval.

[0121] This adaptive control method not only improves the shock absorption performance of the electric wheelchair, but also effectively improves the riding comfort of the user. By dividing the remaining distance into multiple equidistant moving intervals and using a decreasing moving speed in each interval, the system can more finely control the position adjustment of the heavy object. This method can not only reduce the impact of sudden changes in the center of gravity on the stability of the wheelchair, but also dynamically adjust the control strategy according to the real-time monitoring of the wheelchair status, thereby achieving a smoother and more comfortable shock absorption effect.

[0122] As a preferred implementation, the present application can be implemented according to the following steps:

[0123] First, the current position and preset target position of each movable weight unit are obtained through a high-precision position sensor. For example, suppose there are three units A, B, and C, and their current positions are 10 cm, 20 cm, and 30 cm (relative to the front end of the wheelchair seat), and the target positions are 15 cm, 25 cm, and 35 cm, respectively.

[0124] Then, calculate the movement distance of each unit. In this example, the movement distances of units A, B, and C are 5cm, 5cm, and 5cm respectively.

[0125] Next, the initial moving speed of each unit is determined based on the moving distance and the current speed of the electric wheelchair. Assuming the current speed of the wheelchair is 2km / h, the initial moving speed can be set to 0.5cm / s. The current speed of the wheelchair determines when the rear wheels come into contact with the uneven road surface.

[0126] During the movement, the gyroscope and accelerometer installed on the wheelchair are used to obtain the wheelchair's tilt angle and vertical acceleration data in real time. The comfort threshold is set to a tilt angle of no more than 5 degrees and a vertical acceleration of no more than 0.5g.

[0127] When the tilt angle or vertical acceleration is detected to exceed the preset comfort threshold, for example, the tilt angle reaches 6 degrees, the system will recalculate the remaining distance. Assume that unit A has moved 2cm at this time and the remaining distance is 3cm.

[0128] The remaining distance is divided into three equidistant moving intervals, each with a length of 1 cm.

[0129] In the first moving interval, unit A is controlled to move at a speed of 0.5 cm / s; in the second moving interval, the speed is reduced to 0.4 cm / s (80% of the speed in the previous interval); in the third moving interval, the speed is further reduced to 0.32 cm / s.

[0130] Through this decreasing speed control method, the impact of sudden changes in the center of gravity on the stability of the wheelchair can be effectively reduced, achieving a smoother shock absorption effect.

[0131] This application achieves active and adaptive shock absorption by introducing movable weights and segmented deceleration speed control. This method can not only better cope with complex and changeable road conditions, but also dynamically adjust according to the user's real-time comfort, greatly improving the comfort and safety of electric wheelchairs.

[0132] In some of the above-mentioned embodiments, during the implementation of the present application, there is still the problem of how to achieve smooth adjustment of the movable weight during the shock absorption process of the electric wheelchair to improve user comfort.

[0133] In this regard, the present application further proposes to obtain the current inclination angle and vertical acceleration data of the electric wheelchair; based on the inclination angle and vertical acceleration data, combined with a preset comfort threshold, calculate the maximum allowable moving speed of the movable heavy object under the current road conditions; when the remaining distance is divided into three equidistant moving intervals; in the first moving interval, control the movement of the movable heavy object at the maximum allowable moving speed; in the second moving interval, control the movement of the movable heavy object at 70% of the maximum allowable moving speed; in the third moving interval, control the movement of the movable heavy object at 40% of the maximum allowable moving speed; when it is detected that the inclination angle or vertical acceleration of the wheelchair exceeds the preset comfort threshold, reduce the moving speed of the current moving interval to 50% of the previous level moving speed.

[0134] The present application obtains the tilt angle and vertical acceleration data of the electric wheelchair, and calculates the maximum allowable moving speed of the movable heavy object in combination with the preset comfort threshold. This step ensures that the movement of the heavy object does not exceed the user's comfort tolerance range. The remaining distance is divided into three equidistant moving intervals, and a decreasing moving speed is used in each interval. This design can achieve smooth adjustment of the heavy object. The maximum allowable speed is used in the first moving interval to quickly complete most of the adjustments; the second moving interval is reduced to 70%, and the adjustment speed begins to slow down; the third moving interval is further reduced to 40% to achieve fine adjustment in the final stage. This design of decreasing speed can effectively reduce vibration during the adjustment process and improve user comfort. When it is detected that the wheelchair tilt angle or vertical acceleration exceeds the preset comfort threshold, the moving speed of the current moving interval is reduced to 50% of the previous moving speed. This emergency measure can quickly reduce the adjustment speed when the road conditions suddenly change or the user feels uncomfortable, further ensuring user comfort. This solution achieves smooth adjustment during the shock absorption process of the electric wheelchair by finely controlling the adjustment process of the movable heavy object, effectively improving user comfort. The innovativeness of the solution lies in the use of segmented decreasing speed control and dynamic adjustment mechanism, which can flexibly adjust the heavy object movement strategy according to real-time road conditions and user reactions, thereby maximizing user comfort experience while ensuring the shock absorption effect.

[0135] The technical solution of the present application can be implemented in a variety of ways. For example, when calculating the maximum allowable moving speed of a movable heavy object under current road conditions, a fuzzy logic control algorithm can be used. The algorithm can use the tilt angle and vertical acceleration as input variables, and output the maximum allowable moving speed through a preset fuzzy rule set. For example, when the tilt angle is small and the vertical acceleration is low, a higher moving speed is allowed; otherwise, the moving speed is reduced.

[0136] Dividing the remaining distance into three equally spaced moving intervals can be achieved through software algorithms. The system can calculate the current position and target position of the movable weight in real time, and then divide the distance between the two into three equal sections. The length of each interval can be adjusted according to the actual situation.

[0137] Controlling the moving speed of the movable weight within different moving intervals can be achieved by precise motor control. For example, a stepper motor or a servo motor can be used to drive the movable weight, and the moving speed can be precisely controlled by adjusting the motor speed.

[0138] When the wheelchair tilt angle or vertical acceleration is detected to exceed the preset comfort threshold, the system can immediately trigger the emergency response mechanism. This can be achieved by setting a threshold comparator. When the sensor data exceeds the preset threshold, a signal is immediately sent to the control unit, which then reduces the speed of the current moving interval to 50% of the previous level.

[0139] In actual application, first, the system will calculate the maximum allowable moving speed of the movable weight based on the current tilt angle and vertical acceleration data, combined with the preset comfort threshold. This speed is the fastest speed at which the weight can be adjusted while ensuring user comfort. Assume that the calculated maximum allowable moving speed is 10cm / s.

[0140] Next, the system divides the remaining distance of the movable weight into three equally spaced moving intervals. For example, if the remaining distance is 30cm, then the length of each interval is 10cm. In the first 10cm interval, the system controls the movement of the weight at the maximum allowed moving speed of 10cm / s. This allows most adjustments to be completed quickly.

[0141] After entering the second 10cm interval, the system will reduce the movement speed to 70% of the maximum allowed movement speed, that is, 7cm / s. This speed reduction can make the movement of heavy objects more stable and reduce the impact on users.

[0142] In the last 10cm interval, the system further reduces the speed to 40% of the maximum allowed movement speed, that is, 4cm / s. This lower speed allows for fine adjustment of the position of the weight to ensure that the optimal position is finally reached.

[0143] During the entire adjustment process, the system continuously monitors the inclination angle and vertical acceleration of the wheelchair. If at any time it is detected that these parameters exceed the preset comfort threshold, such as a sudden increase in the inclination angle to 10 degrees or a sudden increase in the vertical acceleration to 2m / s², the system will immediately trigger an emergency response mechanism. Assuming this happens in the second movement interval, the system will immediately reduce the current movement speed of 7cm / s to 50% of the previous movement speed, that is, 5cm / s (50% of 10cm / s).

[0144] The segmented decreasing speed control and dynamic adjustment mechanism proposed in this application can flexibly adjust the heavy object movement strategy according to real-time road conditions and user reactions, thereby maximizing the user comfort experience while ensuring the shock absorption effect.

[0145] In some of the above-mentioned embodiments, during the implementation of the present application, there is still the problem of how to reasonably divide the adjustment range of the movable weight according to the driving state and road condition information of the electric wheelchair to achieve smooth and efficient shock absorption control.

[0146] In this regard, the present application further proposes obtaining the current driving speed and predicted road condition information of the electric wheelchair; calculating the adjustment time required for the movable weight to reach the optimal position based on the current driving speed, predicted road condition information and the remaining distance of the movable weight; determining the number of moving intervals based on the adjustment time and the shock absorption response characteristics of the electric wheelchair; and evenly dividing the remaining distance into the determined number of equidistant moving intervals.

[0147] This application achieves fine control of the shock absorption process by obtaining the real-time driving data and predicted road condition information of the electric wheelchair, dynamically calculating the adjustment time and equidistant movement interval of the movable weight. By dividing the remaining distance into multiple equidistant movement intervals, a smoother movement of the weight can be achieved in each interval, thereby improving the shock absorption effect and user comfort.

[0148] The technical solution of this application involves the following key features:

[0149] Obtaining the current speed and predicted road condition information of the electric wheelchair: This step can be achieved in a variety of ways. For example, the current speed can be obtained using a wheel encoder or GPS module, while the predicted road condition information can be predicted by combining the data collected by the front wheel with a machine learning algorithm. This information provides the basis for subsequent calculations and can reflect the real-time movement status of the wheelchair and the upcoming road conditions.

[0150] Calculate the adjustment time required for the movable weight to reach the optimal position: Based on the obtained driving speed, road conditions and remaining distance, the adjustment time can be calculated using a preset algorithm. For example, factors such as the road condition complexity index and the difference between the current speed and the expected speed can be considered to calculate the adjustment time through weighted calculation. This step provides an important basis for the subsequent interval division.

[0151] Determine the number of moving intervals: Based on the calculated adjustment time and the shock absorption response characteristics of the electric wheelchair, a dynamic interval number determination mechanism can be set. For example, when the adjustment time is short or the shock absorption response is fast, a smaller number of intervals can be selected; otherwise, the number of intervals can be increased. This can make the interval division more reasonable and adapt to different driving conditions.

[0152] Evenly divide the remaining distance: divide the calculated remaining distance into a certain number of equally spaced intervals. Even division facilitates smooth shock control and avoids sudden changes in the position of the load.

[0153] The technical solution of this application can be operated as follows in practical application:

[0154] First, the control system of the electric wheelchair will obtain the current driving speed in real time, which can be achieved through the encoder installed on the wheel axle of the wheelchair, and at the same time, predict the road conditions that will be encountered.

[0155] Next, based on the speed and road condition information obtained, the control system calculates the adjustment time required for the movable weight to reach the optimal position. For example, if the road conditions ahead are predicted to be complex and the current speed is high, the system may calculate that a longer adjustment time is required to ensure a smooth transition.

[0156] The system then determines the number of movement intervals based on the calculated adjustment time and the shock absorption response characteristics of the wheelchair. The shock absorption response characteristics can be obtained through pre-experimental data, reflecting the shock absorption effect of the wheelchair in different situations. For example, if the adjustment time is long and the shock absorption response of the wheelchair is slow, the system may choose to divide more movement intervals to achieve more detailed control.

[0157] Finally, the system will evenly divide the remaining moving distance into a certain number of equally spaced moving intervals. For example, if the remaining distance is 60cm and the number of intervals is 3, then the length of each interval is 20cm.

[0158] Through this dynamic division method, the electric wheelchair can more accurately control the position adjustment of the movable weight according to the real-time driving status and road condition information. This not only improves the shock absorption effect, but also can be adaptively adjusted according to different situations, greatly enhancing the adaptability and user comfort of the electric wheelchair in complex road conditions.

[0159] As a specific example, suppose an electric wheelchair is traveling at 5 km / h and the front wheel detects an uneven road surface. The control system first obtains this information and then calculates based on a preset algorithm that the movable weight requires 2 seconds of adjustment time to reach the optimal position. Based on this adjustment time and the shock absorption response characteristics of the wheelchair (assuming that it is known that at this speed, the wheelchair can effectively respond to a shock absorption adjustment every 0.5 seconds), the system decides to set the number of moving intervals to 4.

[0160] Assuming that the remaining distance of the movable weight is 40cm, the system will evenly divide it into 4 equidistant moving intervals, each with a length of 10cm. In the next 2 seconds, the system will control the movable weight to pass through these 4 intervals in turn, completing the movement of one interval every 0.5 seconds. This segmented movement method can ensure that the adjustment of the weight position is synchronized with the shock absorption response of the wheelchair, thereby achieving a smoother shock absorption effect.

[0161] Throughout the process, the system continuously monitors the wheelchair's posture and the user's comfort. If an abnormality is detected during movement in a certain interval (such as a sudden bump), the system can immediately adjust the movement strategy for subsequent intervals, such as slowing down the movement speed or temporarily increasing the number of intervals to adapt to the emergency.

[0162] Compared with the traditional fixed parameter shock absorption method, this method of dynamically dividing the moving range based on real-time data can adaptively adjust the shock absorption strategy according to different driving conditions and road conditions, providing more precise and personalized shock absorption control.

[0163] In some of the above-mentioned embodiments, during the implementation of the present application, there is still a technical problem of how to ensure the shock absorption effect and user comfort of the electric wheelchair under complex road conditions.

[0164] In this regard, the present application further proposes obtaining real-time data of the front wheels, rear wheel shock absorbers and movable weights of the electric wheelchair; calculating the complexity index of the current road conditions based on the real-time data; dynamically adjusting the sampling frequency and data processing priority of the front wheels based on the complexity index; starting the high-frequency sampling mode to improve data processing accuracy when it is detected that the road condition complexity index exceeds a preset threshold; allocating the front wheel data and rear wheel shock absorber parameters to two independent microprocessor units for parallel processing; using a data fusion algorithm to integrate the front wheel data and rear wheel shock absorber parameters to generate a road condition prediction model; and calculating the optimal adjustment strategy for the movable weight based on the road condition prediction model.

[0165] This technical solution achieves accurate perception, efficient processing and intelligent adjustment of complex road conditions through the collaborative work of multiple technical features such as real-time data acquisition, road complexity calculation, dynamic adjustment of sampling frequency, high-frequency sampling mode, parallel processing, data fusion and optimal adjustment strategy calculation. The system can adaptively adjust data acquisition and processing strategies according to the complexity of road conditions, and improve processing efficiency and accuracy through parallel computing and data fusion. Finally, by calculating the optimal adjustment strategy for movable weights, the shock absorption effect of electric wheelchairs under complex road conditions is optimized and user comfort is improved.

[0166] The technical solution of this application involves multiple key features, and specific implementation methods may include:

[0167] Real-time data acquisition: The front wheel obtains its vibration information on the road surface, the rear wheel shock absorber obtains the current shock absorption parameters, and the movable weight obtains the current position information. The sensor can be a combination of accelerometer, gyroscope or pressure sensor.

[0168] Road condition complexity calculation: Based on the front wheel data, the road vibration spectrum is analyzed using Fast Fourier Transform (FFT), and the complexity index is calculated by combining the amplitude and frequency characteristics. The complexity index can be defined in the range of 0-100, where 0 represents a flat road and 100 represents an extremely complex road condition.

[0169] Dynamically adjust the sampling frequency: According to the complexity index, the sampling frequency is adjusted using a piecewise function. For example, when the complexity index is less than 30, the sampling frequency is 50Hz; between 30-70, the sampling frequency is 100Hz; when greater than 70 and less than 80, the sampling frequency is 200Hz.

[0170] High-frequency sampling mode: When the complexity index exceeds the preset threshold (such as 80), the high-frequency sampling mode is activated. In this mode, the sampling frequency can be increased to 500Hz, and the priority of data processing is increased.

[0171] Parallel processing: Using two independent microprocessor units, one dedicated to processing front wheel data and the other to processing rear wheel shock absorber parameters. This parallel architecture can significantly increase data processing speed.

[0172] Data fusion algorithm: Use Kalman filter to fuse the front wheel data and rear wheel shock absorber parameters to generate a more accurate road condition prediction model.

[0173] Optimal adjustment strategy calculation: Based on the road condition prediction model, the model predictive control (MPC) algorithm is used to calculate the optimal position of the movable weight. The MPC algorithm can optimize the control output in the future while considering the system constraints.

[0174] There are close connections and interactions between these features. For example, the calculation of road complexity directly affects the adjustment of sampling frequency and the triggering of high-frequency sampling mode. The more detailed data obtained in high-frequency sampling mode can provide richer information for parallel processing and data fusion, thereby generating a more accurate road condition prediction model. Ultimately, based on this accurate prediction model, the system can calculate a better adjustment strategy for movable heavy objects.

[0175] Specifically, when an electric wheelchair enters a complex road from a flat road, the front wheel detects the change in road surface first. The system immediately calculates the road complexity index and increases the sampling frequency accordingly. If the complexity index exceeds the preset threshold, the high-frequency sampling mode is activated. These high-precision data are quickly processed by the parallel processing unit, and then an accurate road condition prediction model is generated through the data fusion algorithm. Based on this model, the system can calculate the optimal adjustment strategy for the movable weight before the rear wheel contacts the complex road condition, thereby optimizing the center of gravity distribution and shock absorption effect of the wheelchair in advance.

[0176] This adaptive and predictive control method can significantly improve the shock absorption effect of electric wheelchairs in complex road conditions. By adjusting the position of the movable weight in advance, the system can actively balance the wheelchair and reduce sudden vibrations caused by changes in road conditions. At the same time, because the system can respond quickly to changes in road conditions, it can better maintain the user's comfort and reduce fatigue caused by continuous vibrations.

[0177] In addition, the technical solution of this application also has the advantage of resource optimization. By dynamically adjusting the sampling frequency and processing priority, the system can save computing resources under simple road conditions and concentrate resources for high-precision processing under complex road conditions. This intelligent resource allocation not only improves the overall efficiency of the system, but also may extend the battery life of the electric wheelchair.

[0178] As a preferred implementation, the electric wheelchair shock-absorbing suspension control method of the present application can be performed according to the following steps:

[0179] First, the system acquires road vibration data through the front wheel (such as a three-axis accelerometer) with an initial sampling frequency of 100 Hz. At the same time, the position sensor of the rear wheel shock absorber collects the current shock absorption parameters at a frequency of 50 Hz, and the position encoder of the movable weight records its current position at a frequency of 20 Hz.

[0180] Next, the system uses Fast Fourier Transform (FFT) to analyze the vibration data of the front wheel. For example, the frequency range of 0-10Hz can be divided into 10 sub-bands, the energy of each sub-band is calculated, and the complexity index is calculated based on the preset weights. Assume that the complexity index obtained is 75 (range 0-100).

[0181] Based on this complexity index, the system adjusts the sampling frequency of the front wheel to 200Hz and the frequency of rear wheel shock absorber parameter collection to 100Hz. If the complexity index exceeds the preset threshold of 80, the system will start the high-frequency sampling mode and increase the data processing priority of the front wheel to the highest level of the system.

[0182] In the parallel processing stage, the first microprocessor unit processes the front wheel data, performs real-time FFT analysis and road condition feature extraction, while the second microprocessor unit processes the rear wheel shock absorber parameters and calculates the current shock absorption effect.

[0183] The system then uses the Extended Kalman Filter (EKF) algorithm to fuse the front wheel data and rear wheel shock absorber parameters. The state vector of the EKF algorithm includes the predicted road height, slope and roughness, and the observation vector includes the front wheel acceleration and rear wheel displacement. Through this fusion, the system generates an accurate road condition prediction model for the next 5-meter section.

[0184] Finally, based on this road condition prediction model, the system uses a model predictive control (MPC) algorithm to calculate the optimal adjustment strategy for the movable weight. The objective function of the MPC algorithm includes minimizing the predicted wheelchair tilt angle and vertical acceleration while considering the position constraints and energy consumption of the movable weight. For example, the algorithm may calculate that moving the movable weight backward by 3cm in the next 2 seconds can minimize the impact of the upcoming shock.

[0185] Through this precise prediction and control, the electric wheelchair can optimize the shock absorption system before the rear wheels come into contact with complex road conditions, thereby significantly improving the user's riding comfort.

[0186] In some of the above-mentioned embodiments, during the implementation of the present application, there is still the problem of how to calculate the optimal adjustment strategy for the movable weight under the electric wheelchair seat based on the road condition prediction model to improve the comfort and stability of the electric wheelchair when traveling on uneven roads.

[0187] The core of this application is to pre-adjust the position of the movable weight according to the predicted road conditions to optimize the distribution of the center of gravity of the wheelchair, thereby improving riding comfort and stability. It is mainly achieved through the following steps:

[0188] First, obtain the road condition and driving speed data: obtain the road surface unevenness and wheelchair driving speed data from the road condition prediction model as the basis for calculation.

[0189] Secondly, calculate the expected posture and acceleration: Based on the road condition and driving speed data, calculate the expected tilt angle and vertical acceleration of the electric wheelchair under the road conditions it is about to encounter, which are key indicators for evaluating comfort.

[0190] Then, determine the adjustment range and position change: Based on the preset user comfort threshold, determine the adjustment range of the movable weight, and calculate the optimal position change within this range to minimize the expected tilt angle and vertical acceleration while taking into account energy consumption optimization.

[0191] Finally, the adjustment instructions are generated and executed: an adjustment instruction sequence including the position change of the movable weight and the execution timing is generated, and the instructions are sent to the drive device to achieve precise control of the movable weight, and are executed synchronously with the rear wheel shock absorber parameter adjustment, thereby achieving coordinated control of the front and rear wheel shock absorption.

[0192] Through the above steps, the present application can pre-adjust the position of the movable heavy objects according to the predicted road conditions and optimize the distribution of the center of gravity of the wheelchair, so as to make shock absorption preparations before the rear wheels actually contact the uneven road surface, effectively reduce the tilt and vibration of the wheelchair, and improve the user's comfort and riding experience.

[0193] In this application, the acquisition of the traffic prediction model can be achieved in a variety of ways. For example, a machine learning algorithm can be used to train based on historical traffic data and real-time sensor data to obtain a model that can predict future traffic conditions. Another method is to use GPS and map data, combined with real-time traffic information, to build a dynamically updated traffic prediction system.

[0194] The road surface roughness data can be quantified using data collected by the front wheels. For example, an accelerometer can be used to measure the acceleration change in the vertical direction, or a laser range sensor can be used to measure the road surface height change. These data can be converted into a standardized road surface roughness index, such as the International Roughness Index (IRI).

[0195] The wheelchair speed data can be directly obtained through the wheelchair speed sensor or calculated through the GPS positioning system. In order to improve the accuracy, multiple sensor data can be combined for fusion processing.

[0196] The calculation of the expected tilt angle and vertical acceleration involves the wheelchair dynamics model. A simplified wheelchair dynamics model can be established, taking into account factors such as wheelchair mass, center of gravity position, suspension characteristics, etc. By inputting road surface unevenness data and driving speed data into this model, the tilt angle and vertical acceleration of the wheelchair under future road conditions can be predicted.

[0197] The setting of the user comfort threshold needs to consider factors such as the user's physical condition and personal preferences. In order to determine a reasonable range, user feedback is usually collected and a large amount of experimental data is analyzed. For example, setting the tilt angle threshold to ±5° means that when the wheelchair tilts more than plus or minus 5 degrees, the system will start adjustment, with the aim of limiting the tilt of the wheelchair to a smaller range and improving riding comfort. The vertical acceleration threshold is similarly set to ±0.5g, which limits the acceleration in the vertical direction and reduces the sense of vibration. ±5° and ±0.5g are just example values. In actual applications, they will be adjusted according to the specific user group and wheelchair performance to find a balance point that is both comfortable and sensitive.

[0198] The adjustment range of the movable weight depends on the structural design of the wheelchair and safety considerations. Usually, the adjustment range can be limited to a rectangular area under the wheelchair seat, such as 150mm front and back and 100mm left and right.

[0199] The calculation of the position change is a multi-objective optimization problem that needs to consider minimizing the expected tilt angle and vertical acceleration, as well as optimizing energy consumption. Optimization methods such as particle swarm optimization or genetic algorithm can be used to solve this problem.

[0200] The generation of the adjustment instruction sequence needs to consider the characteristics and response time of the drive device of the movable weight. The position change can be discretized into a series of small step adjustments, each of which contains the position change and execution time.

[0201] The synchronization with the adjustment of the parameters of the rear wheel shock absorber can be coordinated by a central controller, which receives data from the road condition prediction model and controls the drive device of the movable weight and the parameter adjustment mechanism of the rear wheel shock absorber at the same time.

[0202] In some of the above-mentioned embodiments, during the implementation of the present application, there is still the problem of how to achieve precise control of the shock absorption system of the electric wheelchair to optimize the comfort and stability of the wheelchair.

[0203] In this regard, the present application further proposes a method for controlling the shock-absorbing suspension of an electric wheelchair based on a state space model, predictive control and adaptive parameter estimation. The method first obtains the system state vector x(k) of the electric wheelchair, including the wheelchair tilt angle x1, the wheelchair center of gravity position x2 and the wheelchair vertical acceleration x3; obtains the control input vector u(k), including the position of the movable weight and the parameters of the rear wheel shock absorber; obtains the measurable interference vector d(k), including the predicted road surface unevenness and the wheelchair travel speed;

[0204] Based on these inputs, a state-space model of the electric wheelchair shock absorption system is established:

[0205] x(k+1) = A(k)x(k) + B(k)u(k) + E(k)d(k) + w(k)

[0206] y(k) = C(k)x(k) + v(k)

[0207] Among them, y(k) is the observed output vector, including the measured wheelchair tilt angle and the measured wheelchair vertical acceleration; A(k), B(k), C(k) and E(k) are time-varying system matrices; w(k) and v(k) are system noise and observation noise, respectively.

[0208] Next, construct the predictive control objective function:

[0209] J = Σ (w1 * x1^2 + w2 * x2^2 + w3 * x3^2 ​​+ w4 * Σ ui^2 + w5 * Σ Δui^2)

[0210] Among them, w1, w2, w3, w4 and w5 are weight coefficients, Δui is the rate of change of the control input, and ui is the moving distance of each unit of the movable weight.

[0211] In order to achieve real-time estimation and update of parameters, the adaptive law is adopted:

[0212] θ(k+1) = θ(k) + γ * Φ(k) * e(k)

[0213] Among them, θ(k) is the parameter vector to be estimated, γ is the learning rate, Φ(k) is the regression vector, and e(k) is the prediction error.

[0214] According to the state space model, predictive control objective function and adaptive law, the optimal position of the movable weight and the optimal parameters of the rear wheel shock absorber are calculated and updated in real time. Finally, the calculated optimal position and optimal parameters are applied to the shock absorption system of the electric wheelchair to achieve dynamic adjustment of the position of the movable weight and the parameters of the rear wheel shock absorber.

[0215] This method achieves precise control of the position of the movable weight and the parameters of the rear wheel shock absorber by establishing a state space model of the electric wheelchair shock absorption system, combining predictive control and adaptive parameter estimation. The system state vector x(k) contains the key state information of the wheelchair, such as the tilt angle, center of gravity position and vertical acceleration, which directly affect the stability and comfort of the wheelchair. The control input vector u(k) represents the moving distance of each unit of the movable weight and is the main control method of the system. The interference vector d(k) contains the predicted road conditions and wheelchair speed, which are external factors that have a significant impact on the shock absorption effect.

[0216] The state space model describes the dynamic change process of the system state, taking into account the influence of control input, external disturbance, and system and observation noise. This model provides a theoretical basis for the subsequent control strategy. The predictive control objective function comprehensively considers the system state, control input and its rate of change. By adjusting the weight coefficients w1 to w5, a balance can be achieved between the shock absorption effect and energy consumption. The adaptive law is used to estimate and update the system parameters in real time, so that the control strategy can adapt to different road conditions and changes in the wheelchair state.

[0217] In practical applications, the establishment of state space models can be achieved in many ways. For example, linearization methods can be used to approximate nonlinear systems, or piecewise linear models can be used to describe system characteristics in different working intervals. The time-varying system matrices A(k), B(k), C(k), and E(k) can be updated in real time through online parameter identification algorithms to adapt to changes in the system's dynamic characteristics.

[0218] The weight coefficients w1 to w5 in the predictive control objective function can be dynamically adjusted according to different usage scenarios. For example, when driving on a flat road, the weights of w4 and w5 can be increased to reduce the change of control input and improve energy efficiency. On uneven roads, the weights of w1, w2 and w3 can be increased to prioritize the stability and comfort of the wheelchair.

[0219] The learning rate γ of the adaptive law is a key parameter that determines the speed of system parameter update. An adaptive learning rate mechanism can be designed to increase the learning rate when the system state changes dramatically to quickly adapt to the new environment; and reduce the learning rate when the system state is relatively stable to avoid excessive fluctuations in parameter estimates.

[0220] The core innovation of this method is to combine predictive control, state space modeling and adaptive parameter estimation to form a closed-loop real-time optimization system. It can predict future shock absorption needs based on current road conditions and wheelchair status, and adjust movable weights and shock absorber parameters in advance, thereby significantly improving the comfort and stability of electric wheelchairs under complex road conditions. At the same time, through adaptive parameter estimation, the system can continuously learn and optimize its control strategy to adapt to the needs of different users and environments.

[0221] Compared with the traditional fixed parameter shock absorption system, this method has higher flexibility and adaptability. It can effectively solve the shock absorption problem of electric wheelchairs when changing between different road conditions, especially the coordinated control of the front and rear wheels when encountering different road conditions, thus providing disabled users with a more comfortable and stable riding experience.

[0222] As a specific embodiment, the electric wheelchair shock-absorbing suspension control method of the present application can be implemented according to the following steps:

[0223] 1. System initialization: Set the initial state vector x(0), including the initial tilt angle of the wheelchair (e.g. 0°), the initial center of gravity (e.g. 50 cm from the front end of the wheelchair), and the initial vertical acceleration (e.g. 0 m / s²).

[0224] 2. Sensor data acquisition: Use high-precision gyroscopes and accelerometers to collect the real-time tilt angle and vertical acceleration of the wheelchair, and the sampling frequency is set to 100Hz. At the same time, use position sensors to monitor the current position of the movable weight with an accuracy of ±1mm.

[0225] 3. Road condition prediction: Based on the mechanical response of the front wheels and the current speed of the wheelchair (assuming it is 2 km / h), the road conditions that the rear wheels will encounter in 0.5 seconds are predicted.

[0226] 4. State space model update: Based on the latest collected sensor data and road condition prediction results, the time-varying system matrices A(k), B(k), C(k), and E(k) in the state space model are updated. For example, the A(k) matrix can be adjusted according to the current wheelchair posture and road condition characteristics to more accurately describe the system dynamics. A(k) is the state transfer matrix, which describes how the system state evolves, B(k) is the control matrix, which describes how the control input affects the state, C(k) is the output matrix, which maps the state to the observable output, and E(k) is the interference matrix, which describes the impact of external interference.

[0227] 5. Predictive control objective function optimization: Use the quadratic programming algorithm to solve the minimum value of the predictive control objective function J. Set the prediction time domain to 10 sampling periods and the control time domain to 5 sampling periods. The weight coefficients are initially set to: w1 = 0.4, w2 = 0.3, w3 = 0.2, w4 = 0.05, w5 = 0.05.

[0228] 6. Adaptive parameter estimation: Adopt recursive least squares method to realize adaptive law, and set the initial learning rate γ to 0.01. After each iteration, the learning rate is dynamically adjusted according to the size of the prediction error e(k). The larger the error, the higher the learning rate, and the maximum value does not exceed 0.1.

[0229] 7. Control command generation: Based on the optimization results, the target position of the movable weight and the target parameters of the rear wheel shock absorber are calculated. For example, the movable weight needs to be moved forward 2cm and the damping coefficient of the rear wheel shock absorber needs to be increased by 10%.

[0230] 8. Execution control: The motor drive system controls the movable weight to move to the target position and adjusts the damping coefficient of the rear wheel shock absorber. During the control process, the wheelchair posture is monitored in real time. If the tilt angle exceeds 5° or the vertical acceleration exceeds 0.5g, the emergency adjustment mechanism is immediately triggered.

[0231] 9. Feedback and iteration: After executing the control, re-collect the sensor data and evaluate the control effect. If the wheelchair posture and user comfort indicators do not meet expectations, return to step 4 for a new round of optimization and control.

[0232] 10. Parameter self-learning: After the system has been running for a period of time (e.g. 1 hour), the accumulated data is analyzed offline to optimize the structure and parameters of the state space model and improve the initial performance when it is used next time.

[0233] In this way, the electric wheelchair shock absorption system can achieve precise control under complex and changeable road conditions. For example, when the wheelchair enters an uneven road section from a flat road, the system will predict the change in road conditions in advance and start adjusting the position of the movable weight and the parameters of the rear wheel shock absorber before the rear wheel contacts the uneven road surface. This predictive control can significantly reduce the bumps of the wheelchair and improve the user's riding comfort.

[0234] Second, refer to Figure 2 The present application further proposes a shock-absorbing suspension control device for an electric wheelchair, the device comprising:

[0235] The prediction module 210 is used to obtain road surface information collected based on the mechanical response of the front wheel of the electric wheelchair, and predict the road conditions that the rear wheel will encounter based on the road surface information and the distance between the front and rear wheels of the electric wheelchair;

[0236] The control module 220 is used to calculate and control the optimal position of the movable weight under the electric wheelchair seat and the optimal parameters of the rear wheel shock absorber according to the predicted road conditions, wherein the adjustment of the movable weight is used to optimize the distribution of the center of gravity of the wheelchair, and the adjustment of the rear wheel shock absorber is completed before the rear wheel contacts the uneven road surface;

[0237] The adjustment module 230 is used to monitor the wheelchair posture and user comfort in real time, and dynamically adjust the position of the movable weight and the parameters of the rear wheel shock absorber according to the monitoring results.

[0238] The road condition of the rear wheels is predicted by acquiring road information collected by the front wheels, and the position of the movable weight and the rear wheel shock absorber parameters are adjusted in advance according to the prediction results. At the same time, the wheelchair posture and user comfort are monitored in real time for dynamic adjustment, thereby effectively solving the problem of poor shock absorption effect of electric wheelchairs when driving on uneven roads in the prior art, and has the advantage of improving the comfort and stability of electric wheelchairs when driving on uneven roads.

[0239] In addition, in some preferred embodiments, an electric wheelchair shock-absorbing suspension control device proposed in the present application can perform any one of the steps in the above method.

[0240] The above description is only an embodiment of the present application and is not intended to limit the protection scope of the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for controlling a shock-absorbing suspension of an electric wheelchair, characterized in that: The method includes: Acquire road surface information collected based on the mechanical response of the front wheel of the electric wheelchair, and predict the road conditions that the rear wheel will encounter based on the road surface information and the distance between the front and rear wheels of the electric wheelchair; According to the predicted road conditions, the optimal position of the movable weight under the electric wheelchair seat and the optimal parameters of the rear wheel shock absorber are calculated and controlled, wherein the adjustment of the movable weight is used to optimize the distribution of the center of gravity of the wheelchair, and the adjustment of the rear wheel shock absorber is completed before the rear wheel contacts the uneven road surface; Real-time monitoring of the wheelchair posture and user comfort, and dynamic adjustment of the position of the movable weight and the parameters of the rear wheel shock absorber according to the monitoring results; The step of calculating and controlling the optimal position of the movable weight under the electric wheelchair seat and the optimal parameters of the rear wheel shock absorber according to the predicted road conditions comprises: Acquiring road surface roughness data in predicted road conditions based on the road surface information collected by the front wheel; Get the current speed data of the electric wheelchair; Calculating the optimal position of the movable weight and the optimal parameters of the rear wheel shock absorber based on the road surface unevenness data and the driving speed data; Also includes: Dividing the movable weight into a plurality of units and calculating the moving time interval of each unit; Control each unit to move to the corresponding optimal position according to the moving time interval; Real-time monitoring of wheelchair posture and user comfort during each unit movement; When the wheelchair posture or user comfort exceeds a preset range, dynamically adjust the movement strategy of the subsequent units and the rear wheel shock absorber parameters to optimize the wheelchair center of gravity distribution and shock absorption effect; The step of calculating the optimal position of the movable weight and the optimal parameters of the rear wheel shock absorber based on the road surface unevenness data and the driving speed data comprises: Obtain the system state vector x(k) of the electric wheelchair, including the wheelchair tilt angle x1, the wheelchair center of gravity position x2, and the wheelchair vertical acceleration x3; Obtain the control input vector u(k), including the position of the movable weight and the parameters of the rear wheel shock absorber; Obtaining a measurable disturbance vector d(k), including the predicted road surface roughness and the wheelchair travel speed; Build a state-space model of the electric wheelchair shock absorption system: x(k+1) = A(k)x(k) + B(k)u(k) + E(k)d(k) + w(k) y(k) = C(k)x(k) + v(k) Where y(k) is the observed output vector, including the measured wheelchair tilt angle and the measured wheelchair vertical acceleration; A(k), B(k), C(k) and E(k) are time-varying system matrices; w(k) and v(k) are system noise and observation noise, respectively; Construct the predictive control objective function: J = Σ (w1 * x1^2 + w2 * x2^2 + w3 * x3^2 ​​+ w4 * Σ ui^2 + w5 * Σ Δui^2) Among them, w1, w2, w3, w4 and w5 are weight coefficients, Δui is the rate of change of the control input, and ui is the moving distance of each unit of the movable weight; Use adaptive law for parameter estimation: θ(k+1) = θ(k) + γ * Φ(k) * e(k) Among them, θ(k) is the parameter vector to be estimated, γ is the learning rate, Φ(k) is the regression vector, and e(k) is the prediction error; Calculate and update the optimal position of the movable weight and the optimal parameters of the rear wheel shock absorber in real time according to the state space model, the predictive control objective function and the adaptive law; The calculated optimal position and optimal parameters are applied to the shock absorption system of the electric wheelchair to achieve dynamic adjustment of the position of the movable weight and the parameters of the rear wheel shock absorber.

2. The electric wheelchair shock-absorbing suspension control method according to claim 1, characterized in that: The step of dividing the movable weight into a plurality of units and calculating the moving time interval of each unit comprises: Get the current position and target position of each unit; Determine the movement priority of each unit based on the impact of the position change of each unit on the center of gravity of the wheelchair; Calculate the moving time interval between adjacent units based on the moving priority, the wheelchair speed and the predicted road condition; Also includes: Before each unit moves, obtain the real-time tilt angle and vertical acceleration of the wheelchair; When the tilt angle or vertical acceleration exceeds a preset comfort threshold, extending the movement time interval of the unit; According to the calculated moving time interval, the moving timing control instructions of each unit are generated.

3. The electric wheelchair shock-absorbing suspension control method according to claim 1, characterized in that: The step of controlling each unit to move to the corresponding optimal position according to the moving time interval comprises: Get the current position and target position of each unit; Calculate the moving distance of each unit according to the current position and the target position; Determining an initial moving speed of each unit based on the moving distance and the current travel speed of the electric wheelchair; During the movement of each unit, the wheelchair's tilt angle and vertical acceleration data are acquired in real time; When the tilt angle or vertical acceleration data exceeds a preset comfort threshold, calculating the remaining distance for the movable weight to reach the optimal position; Dividing the remaining distance into a plurality of equidistant moving intervals; In each moving interval, the movable weight is controlled to complete position adjustment at a decreasing moving speed, wherein the moving speed of each interval is a preset percentage of the speed of the previous interval.

4. The electric wheelchair shock-absorbing suspension control method according to claim 3, characterized in that: The step of controlling the movable weight to complete position adjustment at a decreasing moving speed in each moving interval comprises: Get the current tilt angle and vertical acceleration data of the electric wheelchair; Calculate the maximum permissible moving speed of the movable heavy object under the current road conditions based on the tilt angle and vertical acceleration data and a preset comfort threshold; When the remaining distance is divided into three equidistant moving intervals: In the first movement interval, controlling the movable heavy object to move at the maximum allowable movement speed; In the second movement interval, the movable weight is controlled to move at 70% of the maximum allowable movement speed; In the third moving interval, the movable heavy object is controlled to move at 40% of the maximum permissible moving speed; When it is detected that the wheelchair tilt angle or vertical acceleration exceeds the preset comfort threshold, the movement speed of the current movement interval is reduced to 50% of the previous movement speed.

5. The electric wheelchair shock-absorbing suspension control method according to claim 3, characterized in that: The step of dividing the remaining distance into a plurality of equidistant moving intervals comprises: Get the current speed and predicted road condition information of the electric wheelchair; Calculating the adjustment time required for the movable heavy object to reach the optimal position according to the current driving speed, the predicted road condition information and the remaining distance of the movable heavy object; Determining the number of movement intervals based on the adjustment time and the shock absorption response characteristics of the electric wheelchair; The remaining distance is evenly divided into a determined number of equidistant moving intervals.

6. The electric wheelchair shock-absorbing suspension control method according to claim 1, characterized in that: The method further comprises: Get real-time data on the front wheels, rear wheel shock absorbers and movable weights of the electric wheelchair; Calculating the complexity index of the current road condition according to the real-time data; Based on the complexity index, dynamically adjusting the sampling frequency and data processing priority of the front wheel; When it is detected that the road complexity index exceeds the preset threshold, the high-frequency sampling mode is activated to improve the data processing accuracy; Also includes: Distribute front wheel data and rear wheel shock absorber parameters to two independent microprocessor units for parallel processing; Use data fusion algorithm to integrate front wheel data and rear wheel shock absorber parameters to generate a road condition prediction model; According to the road condition prediction model, the optimal adjustment strategy of the movable heavy object is calculated.

7. The electric wheelchair shock-absorbing suspension control method according to claim 6, characterized in that: The step of calculating the optimal adjustment strategy for the movable heavy object according to the road condition prediction model comprises: Acquiring road surface unevenness data and wheelchair travel speed data in the road condition prediction model; Calculating the expected tilt angle and vertical acceleration of the electric wheelchair based on the road surface unevenness data and the wheelchair travel speed data; Determine the adjustment range of the movable weight according to a preset user comfort threshold; Calculating the position change of the movable weight within the adjustment range to minimize the expected tilt angle and vertical acceleration while optimizing energy consumption; generating an adjustment instruction sequence including a position change amount of the movable weight and an execution timing; The adjustment instruction sequence is sent to the driving device of the movable weight and is executed synchronously with the adjustment of the rear wheel shock absorber parameters.

8. A shock-absorbing suspension control device for an electric wheelchair, characterized in that: The device includes: A prediction module, used to obtain road surface information collected based on the mechanical response of the front wheel of the electric wheelchair, and predict the road conditions that the rear wheel will encounter based on the road surface information and the distance between the front and rear wheels of the electric wheelchair; A control module, used to calculate and control the optimal position of a movable weight under the electric wheelchair seat and the optimal parameters of a rear wheel shock absorber according to the predicted road conditions, wherein the adjustment of the movable weight is used to optimize the distribution of the center of gravity of the wheelchair, and the adjustment of the rear wheel shock absorber is completed before the rear wheel contacts the uneven road surface; An adjustment module, used for real-time monitoring of the wheelchair posture and user comfort, and dynamically adjusting the position of the movable weight and the parameters of the rear wheel shock absorber according to the monitoring results; The step of calculating and controlling the optimal position of the movable weight under the electric wheelchair seat and the optimal parameters of the rear wheel shock absorber according to the predicted road conditions comprises: Acquiring road surface roughness data in predicted road conditions based on the road surface information collected by the front wheel; Get the current speed data of the electric wheelchair; Calculating the optimal position of the movable weight and the optimal parameters of the rear wheel shock absorber based on the road surface unevenness data and the driving speed data; Also includes: Dividing the movable weight into a plurality of units and calculating the moving time interval of each unit; Control each unit to move to the corresponding optimal position according to the moving time interval; Real-time monitoring of wheelchair posture and user comfort during each unit movement; When the wheelchair posture or user comfort exceeds a preset range, dynamically adjust the movement strategy of the subsequent units and the rear wheel shock absorber parameters to optimize the wheelchair center of gravity distribution and shock absorption effect; The step of calculating the optimal position of the movable weight and the optimal parameters of the rear wheel shock absorber based on the road surface unevenness data and the driving speed data comprises: Obtain the system state vector x(k) of the electric wheelchair, including the wheelchair tilt angle x1, the wheelchair center of gravity position x2, and the wheelchair vertical acceleration x3; Obtain the control input vector u(k), including the position of the movable weight and the parameters of the rear wheel shock absorber; Obtaining a measurable disturbance vector d(k), including the predicted road surface roughness and the wheelchair travel speed; Build a state-space model of the electric wheelchair shock absorption system: x(k+1) = A(k)x(k) + B(k)u(k) + E(k)d(k) + w(k) y(k) = C(k)x(k) + v(k) Where y(k) is the observed output vector, including the measured wheelchair tilt angle and the measured wheelchair vertical acceleration; A(k), B(k), C(k) and E(k) are time-varying system matrices; w(k) and v(k) are system noise and observation noise, respectively; Construct the predictive control objective function: J = Σ (w1 * x1^2 + w2 * x2^2 + w3 * x3^2 ​​+ w4 * Σ ui^2 + w5 * Σ Δui^2) Among them, w1, w2, w3, w4 and w5 are weight coefficients, Δui is the rate of change of the control input, and ui is the moving distance of each unit of the movable weight; Use adaptive law for parameter estimation: θ(k+1) = θ(k) + γ * Φ(k) * e(k) Among them, θ(k) is the parameter vector to be estimated, γ is the learning rate, Φ(k) is the regression vector, and e(k) is the prediction error; Calculate and update the optimal position of the movable weight and the optimal parameters of the rear wheel shock absorber in real time according to the state space model, the predictive control objective function and the adaptive law; The calculated optimal position and optimal parameters are applied to the shock absorption system of the electric wheelchair to achieve dynamic adjustment of the position of the movable weight and the parameters of the rear wheel shock absorber.

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