Electric wheelchair seat self-balancing adjustment system

Abstract

The present application relates to a kind of electric wheelchair seat self-balancing adjustment system, belong to wheelchair seat balance control technical field.System includes: attitude perception module is continuously collected seat and the attitude original data of vehicle body by installed double angle sensor, and carries out attitude information solution;Identification and decision module utilizes attitude information solution result, based on the dynamic time weight algorithm of real-time driving speed, calculates the jolt degree index, and compares with preset threshold value to identify current road condition, generates control instruction;Collaborative control module, for by decoupling control strategy, the independent subtask of compound target is decomposed into front, rear push rod, and adopts PID control algorithm to drive push rod collaborative action.The present application perceives attitude by double-chip layout, combines speed adaptive jolt identification, and utilizes front and rear push rod collaborative control and real-time compensation, stabilizes barycenter and seat angle under complex road condition, significantly improves safety, stability and comfort.

Description

Technical Field

[0001] This invention belongs to the field of wheelchair seat balance control technology, specifically relating to an electric wheelchair seat self-balancing adjustment system. Background Technology

[0002] In the field of electric wheelchairs, existing technologies generally use fixed seats or simple suspension and cushioning systems to cope with uneven road surfaces. These solutions cannot dynamically adjust the seat posture to maintain the stability of the occupant's center of gravity when encountering potholes or steep slopes.

[0003] Existing technologies using a single attitude sensor cannot distinguish between seat posture changes caused by active adjustment of the push rod and vehicle posture changes caused by external road surface excitation, resulting in mixed and inaccurate attitude perception information. Secondly, the identification of bumpy road conditions often uses fixed thresholds or fixed time windows, failing to consider the impact of driving speed on bump perception characteristics. It is insensitive to continuous bumps at high speeds and prone to misjudgment due to sporadic bumps at low speeds, resulting in insufficient accuracy in road condition identification. Finally, the control of the front and rear push rods is often relatively independent or the coordination strategy is simple, failing to fully consider the mechanical coupling between the push rods and the overturning moment caused by the slope. This leads to uncontrolled deviation of the seat angle when adjusting the height or dealing with slopes, making it difficult to achieve both center of gravity stability and posture balance under complex road conditions, resulting in safety risks and poor comfort. Summary of the Invention

[0004] To address the aforementioned problems in the existing technology, this invention provides an electric wheelchair seat self-balancing adjustment system. By using a dual-chip layout to sense posture, combined with speed-adaptive bump recognition, and utilizing coordinated control and real-time compensation of front and rear push rods, the system stabilizes the center of gravity and seat angle under complex road conditions, significantly improving safety, stability, and comfort.

[0005] The objective of this invention can be achieved through the following technical solutions:

[0006] This disclosure provides an electric wheelchair seat self-balancing adjustment system, including a posture perception module, a recognition and decision-making module, and a collaborative control module;

[0007] The attitude perception module continuously collects raw attitude data of the seat and the vehicle body through the installed dual-angle sensors, and obtains the seat pitch angle, the vehicle tilt angle and the estimated real-time center of gravity position of the vehicle through attitude information calculation.

[0008] The identification and decision-making module uses the attitude information calculation results to calculate the bumpiness index based on the dynamic time weight algorithm of real-time driving speed, and compares it with a preset threshold to identify the current road conditions and generate control commands.

[0009] The cooperative control module is used for decomposing the composite target into independent sub-tasks of front and rear push rods by a decoupling control strategy, and driving the push rods to cooperatively act by using a PID control algorithm, so that the stability of the seat pitch angle is maintained while the height is adjusted or the slope is responded.

[0010] The attitude information solving includes the following steps:

[0011] S11, sensor layout: install a seat angle chip and a diagonal beam angle chip, wherein: the seat angle chip is installed in the gravity projection area of the seat skeleton, for directly measuring the real-time pitch angle of the seat; the diagonal beam angle chip is installed on the diagonal beam structure connecting the chassis and the seat, for measuring the whole vehicle inclination angle;

[0012] S12, data synchronous acquisition: initialize the seat angle chip and the diagonal beam angle chip, and synchronously read the attitude raw data of the two chips at a fixed high frequency, while obtaining the acceleration of the real-time running direction of the wheelchair;

[0013] S13, motion acceleration compensation: using the running acceleration to dynamically compensate the raw data of the chip, and through a complementary filtering algorithm, fusing the angular velocity data of the chip itself, calculating the compensated angle value;

[0014] S14, attitude and gravity solving: calculating the relative angle between the seat and the diagonal beam through the compensated seat pitch angle and the whole vehicle inclination angle, and then combining the physical model of the wheelchair, estimating the real-time gravity center position of the whole vehicle.

[0015] As a preferred technical solution of the application, the raw data includes the raw pitch angle, the raw pitch angular velocity obtained from the seat angle chip, and the raw inclination angle, the raw inclination angular velocity obtained from the diagonal beam angle chip.

[0016] As a preferred technical solution of the application, the expression of the calculated compensated angle value is:

[0017] ;

[0018] Wherein:

[0019] ;

[0020] In the formula, represents the compensated angle value; is a filtering coefficient; is a sampling time interval; represents the raw angle value measured by the angle chip at time t; is an angular velocity value measured by the angle chip; represents an angle estimation value based on acceleration calculation; a synthetic acceleration measured for the sensor; denotes the gravitational acceleration.

[0021] As a preferred technical solution of the present application, the estimation of the real-time center of gravity of the whole vehicle comprises:

[0022] calculating the relative angle: ; wherein, denotes the relative angle between the seat and the inclined beam, denotes the compensated seat pitch angle, denotes the compensated whole vehicle tilt angle;

[0023] estimating the center of gravity: substituting the compensated whole vehicle tilt angle into the physical model of the wheelchair to obtain the current real-time seat height :

[0024] ;

[0025] ; wherein, denotes the slope coefficient;

[0026] assuming a fixed vertical offset, the estimation formula of the center of gravity is:

[0027] ;

[0028] ; wherein, denotes the estimated real-time center of gravity of the whole vehicle relative to the height of the chassis; denotes the average vertical offset from the seat plane to the center of gravity of the occupant.

[0029] As a preferred technical solution of the present application, the generation of the control instruction comprises the following steps:

[0030] S21, calculating the dynamic jolt degree: obtaining the real-time driving speed from the wheelchair driving system, and calculating the time window for evaluating the angular velocity according to the driving speed;

[0031] In the time window, the original tilt angular velocity is integrated and averaged to calculate the effective jolt degree adaptive to the speed;

[0032] S22, road condition decision and instruction generation: comparing the effective jolt degree with the jolt threshold to make a jolt judgment, if the effective jolt degree is greater than the jolt threshold, it is determined as a jolt road condition, and a "execute jolt control" instruction is generated;

[0033] compared with the slope threshold , to make a slope judgment:

[0034] if , it is determined as an uphill, and a "execute uphill balance" instruction is generated;

[0035] like If the slope is determined to be downhill, a "Execute downhill balance" command is generated.

[0036] If none of the above conditions are met, a "hold" instruction will be generated.

[0037] As a preferred embodiment of the present invention, the time window is calculated using the following formula:

[0038] ;

[0039] In the formula, For time window, This is an adjustable scaling factor. The driving speed;

[0040] The effective bumpiness is expressed as:

[0041] ;

[0042] In the formula, B represents the effective bumpiness. This represents the original tilt angular velocity.

[0043] As a preferred embodiment of the present invention, the step of using a PID control algorithm to drive the push rod to coordinate action includes the following steps:

[0044] S31. Basic Target Reception and Parsing: Based on the generated control commands, query the built-in basic target mapping table and parse the commands into an initial target set;

[0045] S32. Risk assessment based on center of gravity estimation: Calculate the real-time risk coefficient based on the compensated vehicle tilt angle and center of gravity height;

[0046] S33. Adaptive Target Solution: The basic target set is dynamically corrected using the risk coefficient to generate the final adaptive target set, including height adaptation and control parameter adaptation.

[0047] S34. Torque Compensation Calculation: Based on the compensated vehicle tilt angle and center of gravity height, the overturning moment is estimated. The front push rod converts the overturning moment into the feedforward force that the front push rod needs to output. The rear push rod changes the lever arm by adjusting its height. Based on the force control characteristics of the servo push rod, the feedforward force is converted into the corresponding feedforward control quantity.

[0048] As a preferred technical solution of the present invention, the initial target set Represented as:

[0049] ;

[0050] In the formula, represents a base seat angle target; represents a base seat height target; represents a base PID controller parameter set;

[0051] The expression of the risk coefficient R is:

[0052] ;

[0053] In the formula, is a dimensionless risk coefficient; is the absolute value of the compensated vehicle tilt angle; G is the height of the center of gravity relative to the chassis;

[0054] The height target is self-adaptive: ; In the formula, is the final height target after self-adaptive calculation; is a height self-adaptive gain coefficient;

[0055] The control parameter is self-adaptive: ; In the formula, is the proportional gain after self-adaptive, is the base proportional gain, is a control parameter self-adaptive gain coefficient.

[0056] As a preferred technical solution of the present application, the overturning moment is represented as:

[0057] ;

[0058] In the formula, is a moment feedforward coefficient; represents the effective component of the slope angle converted into moment calculation; G represents the height of the center of gravity;

[0059] The feedforward force is:

[0060] ;

[0061] In the formula, represents the feedforward force required to be output by the front push rod, represents the equivalent force arm of the front push rod acting force to the vehicle overturning shaft;

[0062] The expression of the feedforward control amount is: ; In the formula represents the feedforward control amount for resisting the overturning moment; represents a force-control amount conversion coefficient.

[0063] As a preferred technical scheme of the present application, the step of driving the push rod to move cooperatively by using the PID control algorithm further comprises the steps of:

[0064] S35, angle coupling real-time compensation: when the rear push rod performs height adjustment, real-time calculation is performed on the coupling disturbance of the movement of the rear push rod to the seat angle, and the control output of the front push rod is injected in the form of feedforward, comprising:

[0065] calculating the expected value of the angle disturbance caused by the rear push rod: ; wherein, represents the expected value of the seat pitch angle velocity disturbance caused by the movement coupling of the rear push rod; represents the real-time movement speed of the rear push rod; represents the electromechanical coupling coefficient;

[0066] generating a feedforward compensation amount: integrating the expected value of the angle disturbance and multiplying it by a feedforward gain to obtain an additional feedforward control amount of the front push rod: ; wherein, represents the angle feedforward amount for compensating the mechanical coupling;

[0067] synthesizing a final control instruction: the final control instruction of the front push rod is composed of the output of the angle PID controller, the feedforward control amount for resisting the overturning moment, and the angle coupling feedforward amount:

[0068] ;

[0069] ; wherein, is the output of the angle PID controller, is the feedforward control amount for resisting the overturning moment, is the angle coupling feedforward amount.

[0070] The present application has the following beneficial effects:

[0071] This invention achieves intelligent, stable, and safe balance for electric wheelchairs on bumpy and sloping roads through the coordinated operation of three modules. First, the attitude perception module, through a specific layout of dual-angle chips and data fusion, accurately calculates the seat pitch angle, vehicle tilt angle, and real-time center of gravity position. By calculating the relative angle between the seat and the vehicle body, it effectively separates the internal system state from external excitations, providing a unique and high-precision perception foundation for subsequent control. Second, the recognition and decision-making module utilizes attitude information and introduces a dynamic time weighting algorithm based on real-time speed to calculate the effective bump degree, overcoming the misjudgment problem of fixed judgment methods at different speeds, and achieving accurate and adaptive recognition and command generation of bumps and slope conditions. Finally, the collaborative control module, through a decoupled control strategy, decomposes the composite target into independent sub-tasks of the front push rod (responsible for angle) and the rear push rod (responsible for height). On this basis, it introduces a risk assessment based on center of gravity estimation to dynamically adapt the control target and parameters. At the same time, it actively counteracts the overturning moment under slope through torque compensation calculation, and through an angle coupling real-time compensation mechanism, it explicitly cancels the angle disturbance caused by the lifting and lowering of the rear push rod in the front push rod control. Through coordinated control, the system can not only quickly lower the center of gravity to improve stability when facing severe bumps or steep inclines, but also ensure that the seat pitch angle is always maintained at the target balance position, thereby significantly improving the accuracy of operation, the comfort of riding, and fundamentally enhancing the active safety of the system. Attached Figure Description

[0072] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0073] Figure 1 This is a schematic diagram of the structure of an electric wheelchair seat self-balancing adjustment system provided in an embodiment of the present invention;

[0074] Figure 2 A schematic diagram illustrating the steps of attitude information calculation provided in this embodiment of the invention;

[0075] Figure 3 A schematic diagram illustrating the steps for generating control instructions provided in an embodiment of the present invention;

[0076] Figure 4 This is a schematic diagram illustrating the steps of using a PID control algorithm to drive the coordinated action of a push rod, as provided in an embodiment of the present invention. Detailed Implementation

[0077] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided.

[0078] This embodiment provides an electric wheelchair seat self-balancing adjustment system, such as...Figure 1 As shown, it comprises a posture perception module, an identification and decision module, and a collaborative control module.

[0079] The posture perception module continuously collects posture raw data of the seat and the vehicle body through the installed double-angle sensor, and obtains the seat pitch angle, the vehicle tilt angle, and the estimated real-time center of gravity position of the vehicle through posture information calculation.

[0080] The posture information calculation is as follows: Figure 2 As shown, it comprises the following steps:

[0081] S11, sensor layout: install seat angle chip and inclined beam angle chip, wherein:

[0082] The seat angle chip is installed in the center of gravity projection area of the seat skeleton, which is used to directly measure the real-time pitch angle of the seat, which directly reflects the center of gravity tilt trend of the occupant;

[0083] The inclined beam angle chip is installed on the inclined beam structure connecting the chassis and the seat, which is used to measure the tilt angle of the vehicle relative to the direction of gravity (also known as the vehicle tilt angle), which is linearly related to the seat height.

[0084] It should be noted that by calculating the relative angle between the seat and the vehicle body, this layout realizes the separation of the internal state change caused by the push rod adjustment and the external posture change caused by the road excitation, which cannot be realized by a single sensor solution, thereby providing a unique data basis for subsequent high-precision center of gravity estimation and intelligent decision-making.

[0085] S12, data synchronous acquisition: initialize the seat angle chip and the inclined beam angle chip, and synchronously read the posture raw data of the two chips at a fixed high frequency, while obtaining the acceleration of the wheelchair real-time running direction.

[0086] It should be noted that the seat angle chip and the inclined beam angle chip are IMU (Inertial Measurement Unit) chips that integrate three-axis accelerometers and three-axis gyroscopes. The raw data includes the original pitch angle, the original pitch angular velocity obtained from the seat angle chip, and the original tilt angle, the original tilt angular velocity obtained from the inclined beam angle chip.

[0087] S13, motion acceleration compensation: use the running acceleration to dynamically compensate the raw data of the chip to suppress the measurement error generated during the motion, and through a complementary filtering algorithm, fuse the angular velocity data of the chip itself to calculate the compensated angle value (seat pitch angle and vehicle tilt angle).

[0088] Specifically, the expression for calculating the compensated angle value is:

[0089] ;

[0090] wherein:

[0091] ;

[0092] wherein, represents the compensated angle value; is a filter coefficient, taking a value in the range (0, 1); is a sampling time interval; represents the original angle value measured by the angle chip at time t; is the angular velocity value measured by the angle chip; represents the angle estimation value calculated based on acceleration, is the combined acceleration measured by the sensor; represents the gravitational acceleration.

[0093] S14, attitude and gravity calculation: calculate the relative angle between the seat and the inclined beam by compensating the seat pitch angle and the whole vehicle tilt angle, and then estimate the real-time gravity center position of the whole vehicle in combination with the physical model of the wheelchair (such as the linear relationship between the seat height and the inclined beam angle).

[0094] Specifically, estimating the real-time gravity center position of the whole vehicle includes:

[0095] Calculate the relative angle: ; wherein, represents the relative angle between the seat and the inclined beam, represents the compensated seat pitch angle, represents the compensated whole vehicle tilt angle;

[0096] Estimate the gravity center: substitute the compensated whole vehicle tilt angle into the physical model of the wheelchair to obtain the current real-time seat height :

[0097] ;

[0098] wherein, represents the slope coefficient, which is determined by the mechanical structure of the wheelchair and is a constant pre-calibrated by measuring the highest and lowest seat heights and their corresponding angles.

[0099] Assuming a fixed vertical offset, the estimation formula for the gravity center position is:

[0100] ;

[0101] wherein, represents the estimated real-time gravity center of the whole vehicle relative to the height of the chassis, which is a scalar representing the vertical position of the gravity center; represents the average vertical offset of the seat plane from the center of gravity of the occupant, a constant determined by design or experiment.

[0102] The identification and decision module uses the attitude information solution result to calculate a jolt degree index based on a dynamic time weight algorithm of real-time driving speed, and compares it with a preset threshold to identify the current road condition (smooth / jolt / uphill / downhill) and generate a control instruction.

[0103] The generated control instruction, as shown in the figure, includes the following steps: Figure 3

[0104] S21, calculate the dynamic jolt degree: obtain the real-time driving speed (the current driving speed scalar of the wheelchair) from the wheelchair driving system, calculate the time window (the dynamically calculated time window length) for evaluating the angular velocity according to the driving speed, and the calculation formula is:

[0105] ;

[0106] In the formula, B is the effective jolt degree, is the time window, is the adjustable proportional coefficient (used to adjust the sensitivity of the time window to the speed), is the driving speed.

[0107] In the time window , the original tilt angular velocity is integrated and averaged to calculate the speed-adaptive effective jolt degree B (the effective jolt degree index calculated based on the speed and the angular velocity), and the expression is:

[0108] ;

[0109] In the formula, B is the effective jolt degree, is the original tilt angular velocity (the original tilt angular velocity obtained from the tilt beam angle chip).

[0110] It should be noted that the dynamic time window algorithm effectively solves the problem of low sensitivity to continuous jolt at high speed and excessive sensitivity to sporadic jolt at low speed under the fixed time window, which leads to misjudgment.

[0111] S22, road condition decision and instruction generation: compare the effective jolt degree with the jolt threshold (the preset jolt degree determination threshold) to determine the jolt, if the effective jolt degree is greater than the jolt threshold, it is determined that the road condition is jolt, and the "execute jolt control" instruction is generated;

[0112] Compare the compensated whole vehicle tilt angle with the slope threshold (preset slope determination threshold) to determine the slope:

[0113] If​ , determine as uphill, generate "execute uphill balance" instruction;

[0114] If , determine as downhill, generate "execute downhill balance" instruction;

[0115] If the above conditions are not met, generate "keep" instruction.

[0116] The cooperative control module is configured to decompose the composite target into independent sub-tasks of the front and rear push rods (the front push rod is responsible for the seat angle, and the rear push rod is responsible for the center of gravity height) by using a decoupling control strategy, and drive the push rods to move cooperatively by using a PID control algorithm, so as to maintain the stability of the seat pitch angle while adjusting the height or responding to the slope.

[0117] The driving of the push rods to move cooperatively by using the PID control algorithm includes the following steps, as shown in FIG. Figure 4

[0118] S31, basic target receiving and analysis: based on the generated control instruction, query the built-in basic target mapping table, and analyze the instruction into an initial target set :

[0119] ;

[0120] In the formula, represents the basic seat angle target, which is usually ; represents the basic seat height target; represents the basic PID controller parameter set.

[0121] It should be noted that the basic target mapping table is a query table preset in the control system, which defines the initial reaction strategy or default behavior mode of the system under various road conditions. It provides the starting point of control. The subsequent adaptive target calculation link (based on the correction of the risk coefficient ) is a fine-tuning and optimization based on this starting point according to the real-time vehicle body posture and center of gravity information, so that the control behavior of the system is upgraded from "standard reaction" to "intelligent adaptation".

[0122] S32, risk assessment based on center of gravity estimation: according to the compensated vehicle inclination angle and the center of gravity height G, calculate the real-time risk coefficient for quantifying the risk of overturning.

[0123] Wherein, the expression of the risk coefficient R is:

[0124] ;

[0125] In the formula,​ is a dimensionless risk coefficient, the greater the value, the higher the risk of system stability; is the absolute value of the compensated vehicle inclination angle; G is the height of the center of gravity relative to the chassis.

[0126] It can be understood that in the calculation formula of the risk coefficient, the absolute value of the compensated vehicle inclination angle and the height of the center of gravity relative to the chassis are dimensionless numbers after normalization processing.

[0127] S33, adaptive target calculation: using risk coefficient to dynamically correct the basic target set to generate the final adaptive target set , including height adaptation and control parameter adaptation:

[0128] Height target adaptation: ;

[0129] In the formula, is the final height target after adaptive calculation; is the height adaptation gain coefficient;

[0130] Control parameter adaptation (taking proportional gain as an example): ;

[0131] In the formula, is the proportional gain after adaptation, is the basic proportional gain, is the control parameter adaptation gain coefficient.

[0132] S34, moment compensation calculation: according to the compensated vehicle inclination angle and the center of gravity height G to estimate the overturning moment, the front push rod converts the overturning moment into the front feed force that the front push rod needs to output, and the rear push rod changes the force arm by adjusting the height.

[0133] Wherein, the overturning moment is expressed as:

[0134] ;

[0135] In the formula, is the moment feedforward coefficient; represents the effective component converted from the slope angle for moment calculation; G represents the height of the center of gravity.

[0136] The front feed force is:

[0137] ;

[0138] In the formula, FF represents the feed-forward force required by the front push rod, FF represents the equivalent force arm of the front push rod's force to the roll axis of the vehicle, in meter (m), which is a constant determined by the mechanical structure.

[0139] Generate the feed-forward control quantity: according to the force-control characteristic of the servo push rod, convert the feed-forward force FF into the corresponding feed-forward control quantity ; where FF represents the feed-forward control quantity for resisting the overturning torque, which is a dimensionless control signal (usually corresponding to the PWM duty ratio or current command); FF represents the force-control quantity conversion coefficient, which is determined by the specifications of the front push rod and the driver characteristics.

[0140] S35, angle coupling real-time compensation: when the rear push rod performs height adjustment, calculate the coupling disturbance of its motion to the seat angle in real time, and inject the control output of the front push rod in the form of feed-forward, including:

[0141] Calculate the expected value of the angle disturbance caused by the rear push rod: ; where FF represents the expected value of the seat pitch angle velocity disturbance caused by the rear push rod motion coupling; FF represents the real-time motion speed of the rear push rod, in m / s; FF represents the electromechanical coupling coefficient, which is determined by the mechanical structure of the wheelchair.

[0142] Generate feed-forward compensation quantity: integrate the expected value of the angle disturbance and multiply it by a feed-forward gain to obtain the additional feed-forward control quantity of the front push rod: ; where FF represents the angle feed-forward quantity for compensating mechanical coupling, which is a dimensionless control output; FF represents the feed-forward control gain.

[0143] Synthesize the final control command: the final control command of the front push rod is composed of the output of the angle PID controller, the feed-forward control quantity for resisting the overturning torque (from S34), and the angle coupling feed-forward quantity:

[0144] ;

[0145] ; where FF represents the output of the angle PID controller, FF represents the feed-forward control quantity for resisting the overturning torque, FF represents the angle coupling feed-forward quantity.

[0146] ​Need to explain, by synthesizing the final control instruction, in the bumpy road, the rear push rod quickly rises and falls to reduce the gravity center, the front push rod can "predict" the angle disturbance about to occur according to the speed of the rear push rod, and compensate in advance. This makes the system still be able to stably keep the seat angle at the target value when facing severe height changes, realizes the true decoupling and collaborative control, and significantly improves the accuracy of control and the comfort of riding.

[0147] The present application realizes intelligent, stable and safe balance of the electric wheelchair in bumpy and slope road conditions through the cooperative operation of three modules. First, the attitude perception module accurately calculates the seat pitch angle, the whole vehicle inclination angle and the real-time gravity center position through the specific layout and data fusion of the double angle chip. It effectively separates the system internal state and external excitation by calculating the relative angle between the seat and the vehicle body, providing a unique and high-precision perception basis for subsequent control. The recognition and decision module uses attitude information and introduces a dynamic time weight algorithm based on real-time speed to calculate the effective bump degree, overcoming the misjudgment problem of fixed judgment method at different speeds, and realizing accurate and adaptive identification and instruction generation of bumpy and slope road conditions. The cooperative control module decomposes the compound target into independent subtasks of the front push rod (responsible for angle) and the rear push rod (responsible for height) through decoupling control strategy, and on this basis, introduces risk assessment based on gravity estimation for dynamic self-adaptation of control target and parameters, and actively counteracts the overturning moment under slope through moment compensation calculation, and through the angle coupling real-time compensation mechanism, the angle disturbance caused by the lifting of the rear push rod is explicitly offset in the control of the front push rod. Through cooperative control, the system can not only quickly reduce the gravity center to improve stability when facing severe bumps or large slopes, but also ensure that the seat pitch angle always maintains at the target balance position, thereby significantly improving the accuracy of operation, the comfort of riding, and fundamentally enhancing the active safety of the system.

[0148] The present application builds a high-precision attitude perception basis that can separate the internal state of the perception system and external excitation by specifically arranging double angle chips on the seat and inclined beam. On this basis, the real-time driving speed is innovatively introduced as a dynamic variable into the bump judgment algorithm, forming a "time weight" road condition recognition mechanism that can adapt to different vehicle speeds. Finally, through a highly coordinated decoupling control strategy, the front and rear push rods are directed to focus on angle maintenance and height adjustment respectively, and the mutual interference between actuators and external overturning moment are solved through explicit moment feedforward and angle coupling real-time compensation, thereby intelligently and actively realizing the collaborative stability of the seat attitude and the whole vehicle gravity center in complex road conditions.

[0149] The above merely describes the preferred embodiments of the present application, and is not intended to limit the present application in any form. Although the present application has been disclosed with the preferred embodiments as above, it is not intended to limit the present application. Any person skilled in the art can make some changes or modifications to the above disclosed technical content to obtain equivalent embodiments with equivalent changes, as long as the changes or modifications do not deviate from the technical solution of the present application. Any modification, change, equivalent change and modification of the above embodiments made according to the technical essence of the present application still belong to the scope of the technical solution of the present application.

Claims

1. A power wheelchair seat self-balancing adjustment system, characterized by: The posture perception module, the identification and decision module, and the cooperative control module are included. The posture perception module continuously collects posture original data of the seat and the vehicle body through the installed double-angle sensor, and obtains the seat pitch angle, the vehicle tilt angle, and the estimated real-time center of gravity position of the vehicle through posture information solving. The identification and decision module calculates the jolt degree index based on the dynamic time weight algorithm of the real-time driving speed using the posture information solving result, compares it with the preset threshold to identify the current road condition, and generates a control instruction. The cooperative control module decomposes the composite target into independent sub-tasks of the front and rear push rods through a decoupling control strategy, and drives the push rods to act cooperatively using a PID control algorithm to maintain the stability of the seat pitch angle while adjusting the height or responding to the slope. The posture information solving includes the following steps: Sensor layout: install a seat angle chip and a diagonal beam angle chip, wherein the seat angle chip is installed at the gravity projection area of the seat skeleton to directly measure the real-time pitch angle of the seat, and the diagonal beam angle chip is installed on the diagonal beam structure connecting the chassis and the seat to measure the vehicle tilt angle. Data synchronous collection: initialize the seat angle chip and the diagonal beam angle chip, and synchronously read the posture original data of the two chips at a fixed high frequency to obtain the real-time running direction acceleration of the wheelchair. Motion acceleration compensation: use the running acceleration to dynamically compensate the original data of the chip, and calculate the compensated angle value through a complementary filtering algorithm by fusing the angular velocity data of the chip itself. Posture and center of gravity solving: calculate the relative angle between the seat and the diagonal beam through the compensated seat pitch angle and vehicle tilt angle, and then estimate the real-time center of gravity position of the vehicle in combination with the physical model of the wheelchair. The generation of the control instruction includes the following steps: Calculate the dynamic jolt degree: obtain the real-time driving speed from the wheelchair driving system, and calculate the time window for evaluating the angular velocity according to the driving speed. In the time window, integrate and average the original tilt angular velocity to calculate the effective jolt degree adaptive to the speed. Road condition decision and instruction generation: compare the effective jolt degree with the jolt threshold to determine the jolt, and if the effective jolt degree is greater than the jolt threshold, generate the "execute jolt control" instruction. compensated vehicle inclination angle compared with the gradient threshold comparison, gradient determination If , it is determined that the slope is upward, and an "execute uphill balance" instruction is generated; If , determine that it is a downhill, and generate an "execute downhill balance" instruction; If none of the above conditions are met, generate the "keep" instruction.

2. The self-balancing adjustment system for an electric wheelchair seat according to claim 1, characterized in that: The original data includes the original pitch angle, the original pitch angular velocity obtained from the seat angle chip, and the original tilt angle, the original tilt angular velocity obtained from the diagonal beam angle chip.

3. The self-balancing adjustment system for an electric wheelchair seat according to claim 2, characterized in that: The expression for calculating the compensated angle value is: ； Wherein: ； wherein denotes the compensated angle value; is a filter coefficient; is a sampling time interval; denotes the raw angle value measured by the angle chip at time t; is the angular velocity value measured by the angle chip; denotes the angle estimate based on the acceleration calculation; is the combined acceleration measured by the sensor; denotes the gravitational acceleration.

4. The self-balancing adjustment system for an electric wheelchair seat according to claim 3, characterized in that: The estimation of the real-time center of gravity position of the vehicle includes: Calculate relative angle: ; wherein, represents the relative angle between the seat and the inclined beam, represents the compensated seat pitch angle, represents the compensated vehicle inclination angle; Estimate the change of the center of gravity: put the compensated vehicle tilt angle into the wheelchair physical model to get the current real-time seat height : ； In the formula, denotes the slope coefficient; Assuming a fixed vertical offset, the estimation formula for the center of gravity position is: ； wherein represents the estimated real-time height of the vehicle's center of gravity relative to the chassis; represents the average vertical offset of the seat plane to the occupant's center of gravity.

5. The self-balancing adjustment system for an electric wheelchair seat according to claim 1, characterized in that: The calculation formula of the time window is: ； In the formula, is a time window, is an adjustable proportionality coefficient, is the driving speed; The expression for the effective jolt degree is: ； wherein B is the effective pitch, is the original tilt angular velocity.

6. The self-balancing adjustment system for an electric wheelchair seat according to claim 1, characterized in that: The use of the PID control algorithm to drive the push rod to act cooperatively includes the following steps: Basic target reception and analysis: based on the generated control instruction, query the built-in basic target mapping table to parse the instruction into an initial target set; Risk assessment based on center of gravity estimation: calculate the real-time risk coefficient according to the compensated vehicle tilt angle and the center of gravity height. Adaptive target solving: using risk coefficient to dynamically modify the basic target set to generate the final adaptive target set, including high adaptability and control parameter adaptability; Torque compensation solving: according to the estimated overturning torque of the compensated vehicle inclination angle and the height of the center of gravity, the front push rod converts the overturning torque into the front feed force required by the front push rod, and the rear push rod changes the force arm by adjusting the height; according to the force control characteristics of the servo push rod, the front feed force is converted into the corresponding front feed control quantity.

7. The self-balancing adjustment system for an electric wheelchair seat according to claim 6, characterized in that: The initial target set is represented as: ； wherein, represents a base seat angle target; represents a base seat height target; represents a base PID controller parameter set; The risk coefficient R The expression of the risk coefficient is: ； In the formula, is a dimensionless risk factor; is the absolute value of the compensated vehicle inclination angle; G is the height of the center of gravity relative to the chassis; The height target is self-adapted: ; in which, is the final height target after self-adaptation calculation; is a height self-adaptation gain coefficient; The control parameter is self-adaptive: ; wherein, is a proportional gain after self-adaption, is a basic proportional gain, is a control parameter self-adaption gain coefficient.

8. The self-balancing adjustment system for an electric wheelchair seat according to claim 7, characterized in that: the overturning moment is represented as: ； In the formula, is a torque feedforward coefficient; represents the effective component of the slope angle converted into torque calculation; represents the height of the center of gravity; The feedforward force Is: ； In the formula, represents the feed-forward force required by the front push rod to output, represents the equivalent force arm of the front push rod acting force to the vehicle rollover axis; The expression of the feedforward control amount is: ; in which represents a feedforward control amount for countering the overturning moment; represents a force-control amount conversion coefficient.

9. The self-balancing adjustment system for an electric wheelchair seat according to claim 8, characterized in that: The PID control algorithm is also used to drive the push rod to act in cooperation, and further includes the following steps: Angle coupling real-time compensation: when the rear push rod executes height adjustment, the coupling disturbance generated by its movement on the seat angle is calculated in real time, and is injected into the control output of the front push rod in the form of feedforward, including: The expected value of the angular disturbance caused by the push rod is calculated as follows: ; where represents the expected value of the seat pitch angular velocity disturbance caused by the push rod motion coupling; represents the real-time motion velocity of the push rod; represents the electromechanical coupling coefficient; Generating feedforward compensation: The angle disturbance expectation value is integrated and multiplied by a feedforward gain to obtain an additional feedforward control value for the feedforward lever: ; wherein denotes the angle feedforward value for compensating the mechanical coupling; Synthesis final control command: final control command for the front push rod is composed of the output of the angle PID controller, the feedforward control amount for countering overturning moment, and the angle-coupled feedforward amount ； wherein is the output of the angle PID controller, is the feedforward control amount against the overturning moment, is the angle coupling feedforward amount.

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