Lower limb exoskeleton rehabilitation robot control system

Abstract

The invention relates to the technical field of rehabilitation robot control, and discloses a lower limb exoskeleton rehabilitation robot control system, which comprises a data acquisition module for acquiring biological data of a patient in real time by using a sensor; acquiring design parameters of the lower limb exoskeleton rehabilitation robot in real time; executing a biological parameter establishment strategy according to the biological data, and establishing biological parameters of the human body; according to the design parameters and the biological parameters, a kinetic model establishment strategy is executed, and the control torque of the lower limb exoskeleton rehabilitation robot is obtained; a self-adaptive control signal design strategy is executed, and the control torque of the lower limb exoskeleton rehabilitation robot is updated; the exoskeleton posture adjustment control module is used for executing a multi-target optimization control strategy and optimizing a plurality of adjustment targets; the stability evaluation strategy is executed, the stability of the lower limb exoskeleton rehabilitation robot control system is evaluated, an efficient and accurate control scheme is provided for the exoskeleton rehabilitation robot, rehabilitation of a patient can be effectively promoted, and safety of the patient can be effectively guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of rehabilitation robot control, and in particular to a lower limb exoskeleton rehabilitation robot control system. Background Art

[0002] The lower limb exoskeleton rehabilitation robot is a high-tech device used to improve the patient's motor function. It is mainly used for rehabilitation treatment of patients with stroke, spinal cord injury or degenerative diseases. It helps patients restore their motor ability and reduce movement disorders through mechanical assistance and intelligent control. First, the lower limb exoskeleton rehabilitation robot can assist patients in joint movement and muscle movement to promote limb function recovery. Secondly, the equipment can be personalized according to the patient's physiological parameters to ensure comfort and safety during the rehabilitation process. The lower limb exoskeleton rehabilitation robot can also provide adjustable loads and simulate normal movement patterns to help patients perform strength training and gradually restore their motor ability. With the development of intelligent control technology, the exoskeleton can achieve multi-objective optimization, comprehensively consider the patient's posture, comfort and motor ability, and provide personalized and precise rehabilitation training programs.

[0003] Existing control technologies for lower limb exoskeleton rehabilitation robots face some key shortcomings. First, many traditional control systems cannot fully consider individual differences among patients, such as physiological characteristics such as joint range of motion and force tolerance, resulting in poor adaptability of the system and the inability to adjust the control strategy in real time according to the needs of different patients. Secondly, existing control methods usually focus on posture adjustment and motion accuracy, but the optimization of patient comfort is relatively insufficient, and often ignore the discomfort or pain that patients may experience during the rehabilitation process, limiting the long-term use and rehabilitation effect of patients. In addition, traditional stability analysis methods mostly rely on static or linear models, which cannot accurately evaluate the dynamic stability of exoskeletons in complex environments, and there are certain risks.

[0004] The present invention proposes a lower limb exoskeleton rehabilitation robot control system, which combines advanced control algorithms, dynamic modeling, stability analysis and real-time feedback mechanisms to provide a set of efficient and precise control solutions for the exoskeleton rehabilitation robot, which can effectively promote patient rehabilitation and ensure their safety. Summary of the invention

[0005] The present invention provides a lower limb exoskeleton rehabilitation robot control system, which is used to promote the solution of the problems mentioned in the above background technology.

[0006] The present invention provides the following technical solution: a lower limb exoskeleton rehabilitation robot control system, comprising:

[0007] Data acquisition module: uses sensors to collect patients' biological data in real time;

[0008] Real-time acquisition of the design parameters of the lower limb exoskeleton rehabilitation robot, including the joint angle θ(t) and joint velocity of the exoskeleton. and joint acceleration

[0009] Data processing module:

[0010] Based on biological data, execute biological parameter establishment strategy to establish biological parameters of human body;

[0011] According to the design parameters and biological parameters, the dynamic model establishment strategy is implemented to obtain the control torque of the lower limb exoskeleton rehabilitation robot;

[0012] Adaptive Control Module:

[0013] Obtaining target joint acceleration of lower limb exoskeleton rehabilitation robot Target joint angle θ1 and target joint velocity

[0014] Calculate the target joint angle minus the joint angle, recorded as the angle error;

[0015] Calculate the target joint speed minus the joint speed, which is recorded as the speed error;

[0016] Implement adaptive control signal design strategy to update the control torque of the lower limb exoskeleton rehabilitation robot;

[0017] Exoskeleton posture adjustment control module:

[0018] Execute multi-objective optimization control strategy to optimize multiple regulation targets;

[0019] Stability Assessment Module:

[0020] Implement stability assessment strategies to evaluate the stability of the control system of the lower limb exoskeleton rehabilitation robot.

[0021] Optionally, executing a biological parameter establishment strategy based on biological data to establish biological parameters of a human body includes:

[0022] The biological parameters include the inertia matrix M(θ0)∈R m×m , Coriolis moment matrix and the gravity matrix G(θ0)∈R m , where θ0 is the human joint angle at time t, is the human joint velocity at time t, and m is the dimension of the matrix;

[0023] Calculate the inertia matrix of the human body M(θ0)∈R m×m ,include:

[0024] Get the mass of the i-th joint in the biological data The length a of the i-th joint i 2 The distance from the center of mass of the human body to the i-th joint axis

[0025]

[0026] Calculate the Coriolis moment matrix include:

[0027] in, is the coupling torque, describing the coupling effect between the i-th joint and the j-th joint, is the joint velocity of the jth joint;

[0028] Calculate the gravity matrix G(θ0)∈R m ,include:

[0029] Where g is the acceleration due to gravity, i,j=1,2,3…n, and n is the number of joints in the human body.

[0030] Optionally, executing a dynamic model establishment strategy according to design parameters and biological parameters to obtain a control torque of a lower limb exoskeleton rehabilitation robot includes:

[0031] Build a dynamic model of the exoskeleton:

[0032]

[0033] in, is the modified inertia matrix, ΔM(θ(t)) represents the influence of the exoskeleton on the human inertia matrix, and θ(t) represents the joint angle of the lower limb exoskeleton rehabilitation robot;

[0034] is the modified Coriolis moment matrix, Indicates the structural differences between the exoskeleton and the human body;

[0035] is the modified gravity matrix, ΔG(θ(t)) represents the influence of the exoskeleton on the gravity matrix;

[0036] τ is the control torque of the lower limb exoskeleton rehabilitation robot.

[0037] Optionally, executing the adaptive control signal design strategy to update the control torque of the lower limb exoskeleton rehabilitation robot includes:

[0038] in, is an adaptive gain term, used to dynamically adjust the control input;

[0039] Update the adaptive gain term Among them, α is a constant used to adjust the adaptive gain term, e θ is the angle error, is the velocity error, and is the adaptive gain term based on Γ0.

[0040] Optionally, the executing a multi-objective optimization control strategy to optimize multiple adjustment objectives includes:

[0041] The adjustment targets include a first adjustment target, a second adjustment target and a third adjustment target;

[0042] Calculate the first adjustment target J1, in, is the weight of the i-th joint, θ i is the human joint angle of the i-th joint, θ i ' is the target joint angle of the i-th joint, i = 1, 2, 3...n, λ i is the weighting factor used to adjust the human joint velocity error, is the human joint velocity of the ith joint, is the target joint velocity of the i-th joint;

[0043] Calculate the second adjustment target J2, J2 = ||τ||;

[0044] Calculate the third adjustment target J3, Among them, τ i (t) is the torque on the i-th joint when the control torque τ is applied to the human body, and T is the total time for posture adjustment;

[0045] Calculate the overall goal of the multi-objective optimization control strategy J = w1×J1+w2×J2+w3×J3, where w1+w2+w3=1, w1, w2, w3 are the first adjustment target weight, the second adjustment target weight, and the third adjustment target weight, respectively.

[0046] Optionally, the executing a multi-objective optimization control strategy to optimize multiple adjustment objectives further includes:

[0047] Optimize the overall goal

[0048] Calculate the gradient of the first adjustment target

[0049] Calculate the gradient of the second adjustment target

[0050] Calculate the gradient of the third adjustment target

[0051] Calculate the gradient of the overall objective

[0052] Set the gradient threshold;

[0053] If the gradient of the total objective is less than the gradient threshold, the multi-objective optimization control strategy is stopped to obtain the optimal solution;

[0054] If the gradient of the total target is greater than or equal to the gradient threshold, the total target is updated.

[0055] Optionally, the executing stability assessment strategy to assess the stability of the lower limb exoskeleton rehabilitation robot control system includes:

[0056] Use Lyapunov stability analysis function Indicates the degree to which the control system of the lower limb exoskeleton rehabilitation robot deviates from the target;

[0057] Calculate the first derivative of the Lyapunov stability analysis function;

[0058] Among them, ζ is a constant term; if The control system of the lower limb exoskeleton rehabilitation robot is stable.

[0059] The present invention has the following beneficial effects:

[0060] 1. The control system of the lower limb exoskeleton rehabilitation robot collects the patient's biological data and exoskeleton design parameters in real time through sensors. The system can obtain accurate feedback information about the patient's physical state and the operating status of the exoskeleton at any time. This real-time feedback provides solid data support for subsequent control and adjustment. Specifically, the sensor can accurately measure the patient's biological parameters such as joint angle, speed, acceleration, and the exoskeleton's design parameters such as joint angle, speed and acceleration. These data are crucial for real-time calculation of control inputs because they ensure that the control system can always make timely adjustments based on the current patient status and machine status. Through accurate data collection, potential risks caused by inaccurate parameters, such as patient discomfort or unstable exoskeleton movement caused by misoperation, can be avoided. Therefore, sensor data acquisition is an important guarantee for system accuracy and safety, providing basic data support for the operation of all subsequent modules, ensuring the efficient operation of the entire exoskeleton system and the safety of patients.

[0061] 2. The control system of the lower limb exoskeleton rehabilitation robot can accurately establish key information such as the human body's inertia matrix, Coriolis moment matrix, and gravity matrix by implementing the biological parameter establishment strategy. Biological parameters are the core components of the dynamic model, and accurate biological parameters can greatly improve the motion control accuracy of the exoskeleton system. For example, the human body's inertia matrix reflects the mass distribution of each joint and body part, while the Coriolis moment matrix describes the coupling effect between each joint. This information is very important for the control of the exoskeleton. By accurately measuring parameters such as the mass, length, and distance of the joint axis of each joint of the human body, parameters that are more in line with the actual human body can be obtained, so that the exoskeleton control system can accurately adapt to the patient's dynamic needs. In addition, through accurate biological parameters, the exoskeleton can better simulate the biomechanical characteristics of the human body, avoid misoperation or discomfort caused by mismatching the control model, and thus improve the patient's comfort and treatment effect.

[0062] 3. The control system of the lower limb exoskeleton rehabilitation robot can accurately calculate the control torque applied by the lower limb exoskeleton rehabilitation robot by combining the design parameters of the exoskeleton with the biological parameters to establish an accurate dynamic model. This accurate dynamic model can effectively predict the behavior of the exoskeleton and adjust the control strategy according to the real-time status of the patient, thereby optimizing its motion path and posture adjustment. For example, when parameters such as the inertia matrix, Coriolis torque matrix, and gravity matrix are combined with the design factors of the exoskeleton, more accurate torque control can be achieved. This not only improves the accuracy of the system, but also effectively reduces the risk of incoordination or injury caused by excessive or insufficient torque. In addition, by accurately calculating the control torque, the exoskeleton can finely adjust the output control force according to the actual needs of the patient (such as the adjustment of a specific posture or movement), provide personalized rehabilitation services, and ensure that each movement can meet the patient's physiological needs to the greatest extent. This accurate control torque calculation method provides a more scientific and efficient guarantee for the patient's rehabilitation treatment.

[0063] 4. The control system of the lower limb exoskeleton rehabilitation robot, the adaptive control signal design can respond to the patient's posture error and speed error in real time, so as to automatically adjust the control torque of the exoskeleton. Specifically, the system dynamically adjusts the control strategy according to the error between the target joint angle and the actual joint angle (angle error) and the error between the target joint speed and the actual joint speed (speed error). This adaptive mechanism enables the exoskeleton to be accurately adjusted according to the patient's real-time physiological state, avoiding excessive or insufficient exercise force. For example, when the patient is fatigued or has muscle tension, the control system can automatically adjust the output torque to reduce the risk of excessive load; and when the patient's condition gradually improves, the system can gradually increase the intensity of exercise to promote the rehabilitation process. In addition, the introduction of adaptive control can also dynamically adjust according to the patient's specific needs at different stages of treatment, so that the exoskeleton can adapt to the patient's rehabilitation process. In this way, patients can not only get a tailored treatment plan, but also enjoy maximum comfort and safety throughout the rehabilitation process.

[0064] 5. The control system of the lower limb exoskeleton rehabilitation robot can find the best balance between multiple adjustment targets by introducing a multi-objective optimization control strategy. Multiple targets such as posture error, control input and comfort often need to be considered and optimized at the same time. Traditional control methods often focus on a single goal, such as minimizing posture error or reducing energy consumption, while ignoring the balance of other key factors. The multi-objective optimization control strategy can consider and optimize multiple targets at the same time to ensure the comprehensive achievement of goals such as posture adjustment, motion trajectory optimization, comfort improvement and minimization of control input. For example, during the optimization process, the system will reduce the posture error while ensuring that the control input is not too large to avoid patient discomfort caused by excessive force. In addition, the multi-objective optimization control strategy can also dynamically adjust the weight according to the patient's needs to ensure that different targets can be prioritized at different stages. In this way, the system can provide a more comprehensive and personalized control strategy, so that patients can enjoy better treatment effects and experience during the rehabilitation process.

[0065] 6. The control system of the lower limb exoskeleton rehabilitation robot uses the Lyapunov stability analysis function to evaluate the stability of the system, which can effectively detect whether the exoskeleton control system is in a stable state. Stability is a key requirement for exoskeleton rehabilitation robots, because only when the system remains stable can the patient be guaranteed safety during the rehabilitation process. By calculating the first-order derivative of the Lyapunov stability analysis function, the system can detect any signs of deviation from the target and make adjustments when necessary. If there is a problem with the stability of the system, the control system can respond quickly and restore the stable state by adjusting the control input. This stability assessment strategy ensures the safety of the entire rehabilitation process and avoids patient discomfort or injury caused by an unstable control system. In addition, the stability assessment mechanism can also adjust the control strategy in real time according to the patient's physiological feedback at different stages of rehabilitation, ensuring that the exoskeleton is always in the optimal working state at each stage of rehabilitation, providing maximum safety and effectiveness. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 It is a schematic diagram of the process of the present invention.

[0067] Figure 2 It is a schematic diagram of the module of the present invention. DETAILED DESCRIPTION

[0068] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0069] Embodiment 1, refer to Figure 1 , a lower limb exoskeleton rehabilitation robot control system, comprising:

[0070] Data acquisition module: uses sensors to collect patients' biological data in real time;

[0071] Real-time acquisition of the design parameters of the lower limb exoskeleton rehabilitation robot, including the joint angle θ(t) and joint velocity of the exoskeleton. and joint acceleration

[0072] The velocities in this embodiment are all angular velocities.

[0073] By collecting the patient's biological data and exoskeleton design parameters in real time through sensors, the system can obtain real-time and accurate feedback information, thereby realizing dynamic monitoring of the patient and exoskeleton status. Sensors can measure biological parameters such as human joint angles, speeds, and accelerations, while monitoring the exoskeleton's design parameters such as joint angles, speeds, and accelerations. This data collection method provides reliable data support for subsequent control decisions, ensuring the real-time and accuracy of the exoskeleton control system. For example, the joint angles and speeds of the human body are crucial to the motion control of the exoskeleton. Real-time collection of these data can reflect the patient's current physiological state and help the exoskeleton adjust the motion trajectory in real time according to the patient's needs. In addition, by monitoring the exoskeleton design parameters, any abnormal conditions during the movement (such as joint jamming, excessive force, etc.) can be intervened in time to avoid harm to the patient.

[0074] In general, accurate data collection ensures the accuracy and real-time performance of the control system, avoiding control errors caused by inaccurate or delayed data. The role of sensors is not limited to providing status information, but also provides the system with flexible adjustment capabilities through dynamic feedback mechanisms, thereby improving patient comfort, safety and treatment effects.

[0075] Data processing module:

[0076] Based on biological data, execute biological parameter establishment strategy to establish biological parameters of human body;

[0077] According to the design parameters and biological parameters, the dynamic model establishment strategy is implemented to obtain the control torque of the lower limb exoskeleton rehabilitation robot;

[0078] Adaptive Control Module:

[0079] Obtaining target joint acceleration of lower limb exoskeleton rehabilitation robot Target joint angle θ1 and target joint velocity

[0080] Calculate the target joint angle minus the joint angle, recorded as the angle error;

[0081] Calculate the target joint speed minus the joint speed, which is recorded as the speed error;

[0082] Implement adaptive control signal design strategy to update the control torque of the lower limb exoskeleton rehabilitation robot;

[0083] Exoskeleton posture adjustment control module:

[0084] Execute multi-objective optimization control strategy to optimize multiple regulation targets;

[0085] Stability Assessment Module:

[0086] Implement stability assessment strategies to evaluate the stability of the control system of the lower limb exoskeleton rehabilitation robot.

[0087] In this embodiment, the module diagram refers to Figure 2 ;

[0088] The method of executing a biological parameter establishment strategy based on biological data to establish biological parameters of a human body includes:

[0089] The biological parameters include the inertia matrix M(θ0)∈R m×m , Coriolis moment matrix and the gravity matrix G(θ0)∈R m , where θ0 is the human joint angle at time t, is the human joint velocity at time t, and m is the dimension of the matrix;

[0090] Calculate the inertia matrix of the human body M(θ0)∈R m×m ,include:

[0091] Get the mass of the i-th joint in the biological data The length a of the i-th joint i 2 The distance from the center of mass of the human body to the i-th joint axis

[0092]

[0093] Calculate the Coriolis moment matrix include:

[0094] in, is the coupling torque, describing the coupling effect between the i-th joint and the j-th joint, is the joint velocity of the jth joint;

[0095] Calculate the gravity matrix G(θ0)∈R m ,include:

[0096] Where g is the acceleration due to gravity, i,j=1,2,3…n, and n is the number of joints in the human body.

[0097] The establishment of accurate biological parameters is the basis for the efficient operation of the exoskeleton control system. By accurately calculating biological parameters such as the inertia matrix, Coriolis moment matrix and gravity matrix of the human body, the system can perform more accurate motion simulation while considering factors such as the mass distribution, coupling effect and gravity influence of each joint and its movement. These parameters help the exoskeleton accurately reflect the changes in the patient's physiological state, optimize the motion path and torque output, and thus improve the adaptability and effect of the system. Specifically, the inertia matrix describes the mass distribution of each joint and body part, while the Coriolis moment matrix reflects the coupling effect between different joints. The precise establishment of these parameters enables the exoskeleton to accurately simulate the biomechanical characteristics of the human body in dynamic interaction with the patient. In addition, the gravity matrix describes the torque influence of the human joints under the action of gravity. The accurate calculation of biological parameters can greatly improve the control accuracy of the system and avoid discomfort or injury caused by the mismatch of the biomechanical model.

[0098] By accurately establishing biological parameters, the exoskeleton system can fully consider the individual differences of patients in a complex rehabilitation environment and provide personalized rehabilitation treatment plans that conform to physiological laws. This accuracy not only improves the stability of the system, but also significantly improves the patient's rehabilitation effect and comfort, and avoids unnecessary side effects.

[0099] According to the design parameters and biological parameters, the dynamic model establishment strategy is implemented to obtain the control torque of the lower limb exoskeleton rehabilitation robot, including:

[0100] Build the exoskeleton's dynamic model:

[0101]

[0102] in, is the modified inertia matrix, ΔM(θ(t)) represents the influence of the exoskeleton on the human inertia matrix, and θ(t) represents the joint angle of the lower limb exoskeleton rehabilitation robot;

[0103] is the modified Coriolis moment matrix, Indicates the structural differences between the exoskeleton and the human body;

[0104] is the modified gravity matrix, ΔG(θ(t)) represents the influence of the exoskeleton on the gravity matrix;

[0105] τ is the control torque of the lower limb exoskeleton rehabilitation robot.

[0106] The establishment of a dynamic model is the core of the precise control of the exoskeleton control system. By combining the design parameters of the exoskeleton with the biological parameters of the human body, the system can accurately establish the dynamic model of the exoskeleton and calculate the control torque of the exoskeleton. An accurate dynamic model not only helps to improve the motion control accuracy of the exoskeleton, but also optimizes the motion trajectory and posture adjustment.

[0107] The exoskeleton's dynamic model includes core parameters such as the inertia matrix, Coriolis moment matrix, and gravity matrix, and the modified versions of these parameters take into account the structural differences between the exoskeleton and the human body. By correcting and optimizing these parameters, the control system can more accurately simulate the interaction between the exoskeleton and the human body, ensuring that the adjustment of torque and motion path is more in line with the patient's physiological needs.

[0108] Through the optimization of the dynamic model, the exoskeleton can not only provide precise motion control during the patient's rehabilitation process, but also make real-time adjustments based on the patient's different needs (such as range of motion, exercise intensity, etc.), avoiding discomfort caused by excessive force or insufficient torque. The precise dynamic model enables the exoskeleton to achieve a smooth and comfortable movement pattern, thereby protecting the patient's safety while promoting their rehabilitation process.

[0109] Implement adaptive control signal design strategy to update the control torque of the lower limb exoskeleton rehabilitation robot, including:

[0110] in, is an adaptive gain term, used to dynamically adjust the control input;

[0111] Update the adaptive gain term Among them, α is a constant used to adjust the adaptive gain term, e θ is the angle error, is the velocity error, and is the adaptive gain term based on Γ0.

[0112] The adaptive control signal can automatically adjust the control strategy based on the patient's real-time feedback. Specifically, the adaptive control signal dynamically adjusts the control input based on the error between the target joint angle and the actual joint angle (angle error) and the error between the target joint velocity and the actual joint velocity (velocity error), thereby optimizing the control torque output.

[0113] This control mechanism ensures that the exoskeleton system can make fine adjustments according to the patient's actual physiological state at different stages of rehabilitation. For example, when the patient feels tired or has muscle tension, the system can automatically reduce the intensity of exercise to reduce excessive load; and when the patient gradually recovers, the system will increase the intensity in time to promote the rehabilitation process.

[0114] The introduction of adaptive control improves the flexibility and adaptability of the exoskeleton system, enabling it to dynamically respond to changes in the patient, ensuring that tailored treatment plans are provided throughout the rehabilitation process. Through real-time feedback mechanisms, control inputs can more accurately match the patient's physiological state, avoiding the risks of over- or under-force, thereby maximizing patient comfort and treatment outcomes.

[0115] Execute multi-objective optimization control strategy to optimize multiple regulation targets, including:

[0116] The adjustment targets include a first adjustment target, a second adjustment target and a third adjustment target;

[0117] Calculate the first adjustment target J1, in, is the weight of the i-th joint, θ i is the human joint angle of the i-th joint, θ i ' is the target joint angle of the i-th joint, i = 1, 2, 3...n, λ i is the weighting factor used to adjust the human joint velocity error, is the human joint velocity of the ith joint, is the target joint velocity of the i-th joint;

[0118] Calculate the second adjustment target J2, J2 = ||τ||;

[0119] Calculate the third adjustment target J3, Among them, τ i (t) is the torque on the i-th joint when the control torque τ is applied to the human body, and T is the total time for posture adjustment;

[0120] Calculate the total target J of the multi-objective optimization control strategy = w1×J1+w2×J2+w3×J3, where w1+w2+w3=1, w1, w2, w3 are the first adjustment target weight, the second adjustment target weight and the third adjustment target weight respectively. Execute the multi-objective optimization control strategy to optimize multiple adjustment targets, including:

[0121] Optimize the overall goal

[0122] Calculate the gradient of the first adjustment target

[0123] Calculate the gradient of the second adjustment target

[0124] Calculate the gradient of the third adjustment target

[0125] Calculate the gradient of the overall objective

[0126] Set the gradient threshold;

[0127] If the gradient of the total objective is less than the gradient threshold, the multi-objective optimization control strategy is stopped to obtain the optimal solution;

[0128] If the gradient of the total target is greater than or equal to the gradient threshold, the total target is updated.

[0129] The multi-objective optimization control strategy enables the exoskeleton system to consider multiple objectives at the same time, such as posture error, motion comfort, control input, etc., to find the best balance point. This strategy avoids the problem of focusing on a single objective in traditional control methods, allowing the system to dynamically balance between multiple objectives. For example, during the posture adjustment process, the system will optimize the posture error and control input at the same time to ensure that the motion trajectory is not only accurate but also does not cause excessive force burden on the patient. In addition, multi-objective optimization can also dynamically adjust the weights of each objective during the treatment process, giving priority to the patient's current needs. For example, in the early stage of rehabilitation, the system may pay more attention to comfort and safety, while in the later stage of rehabilitation, it may focus more on improving motor function and speed. The multi-objective optimization control strategy can adjust the optimization objectives at different stages of treatment according to the actual needs of the patient, thereby maximizing the patient's rehabilitation effect. By considering multiple objectives and optimizing them, the exoskeleton system can provide a more comprehensive and accurate treatment plan to ensure the best treatment effect throughout the rehabilitation process.

[0130] Implement stability assessment strategies to evaluate the stability of the lower limb exoskeleton rehabilitation robot control system, including:

[0131] Use Lyapunov stability analysis function Indicates the degree to which the control system of the lower limb exoskeleton rehabilitation robot deviates from the target;

[0132] Calculate the first derivative of the Lyapunov stability analysis function;

[0133] Among them, ζ is a constant term;

[0134] like The control system of the lower limb exoskeleton rehabilitation robot is stable.

[0135] System stability is a key requirement for exoskeleton control systems. By introducing the existing technology Lyapunov stability analysis, the system is able to evaluate its stability in real time and dynamically adjust the control strategy based on the evaluation results to ensure that the system is in a stable state at all times. The stability evaluation function can detect and correct any signs of deviation from the target in a timely manner by calculating the stability threshold of the system. Lyapunov stability analysis provides an accurate mathematical tool for evaluating whether the control system can operate stably. When the stability of the system is insufficient, the control strategy will be quickly adjusted to restore the system's stable state, thereby avoiding any risks caused by instability, such as patient discomfort or poor rehabilitation results.

[0136] By introducing a stability assessment mechanism, the exoskeleton control system can not only effectively prevent the system from losing control or becoming unstable, but can also adjust the control strategy in real time according to the status of different patients. Stability assessment ensures that the patient is always in a safe state throughout the rehabilitation process, avoiding potential injuries or discomfort caused by system instability.

[0137] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0138] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A lower limb exoskeleton rehabilitation robot control system, characterized in that: include: Data acquisition module: uses sensors to collect patients' biological data in real time; Real-time acquisition of the design parameters of the lower limb exoskeleton rehabilitation robot, including the joint angle θ(t) and joint velocity of the exoskeleton. and joint acceleration Data processing module: Based on biological data, execute biological parameter establishment strategy to establish biological parameters of human body; According to the design parameters and biological parameters, the dynamic model establishment strategy is implemented to obtain the control torque of the lower limb exoskeleton rehabilitation robot; Adaptive Control Module: Obtaining target joint acceleration of lower limb exoskeleton rehabilitation robot Target joint angle θ1 and target joint velocity Calculate the target joint angle minus the joint angle, recorded as the angle error; Calculate the target joint speed minus the joint speed, which is recorded as the speed error; Implement adaptive control signal design strategy to update the control torque of the lower limb exoskeleton rehabilitation robot; Exoskeleton posture adjustment control module: Execute multi-objective optimization control strategy to optimize multiple regulation targets; Stability Assessment Module: Implement stability assessment strategies to evaluate the stability of the control system of the lower limb exoskeleton rehabilitation robot.

2. The lower limb exoskeleton rehabilitation robot control system according to claim 1, characterized in that: The method of executing a biological parameter establishment strategy based on biological data to establish biological parameters of a human body includes: The biological parameters include the inertia matrix M(θ0)∈R m×m , Coriolis moment matrix and the gravity matrix G(θ0)∈R m , where θ0 is the human body joint angle at time t, is the human joint velocity at time t, and m is the dimension of the matrix; Calculate the inertia matrix of the human body M(θ0)∈R m×m ,include: Get the mass of the i-th joint in the biological data The length of the i-th joint The distance from the center of mass of the human body to the i-th joint axis Calculate the Coriolis moment matrix include: in, is the coupling torque, describing the coupling effect between the i-th joint and the j-th joint, is the human joint velocity of the jth joint; Calculate the gravity matrix G(θ0)∈R m ,include: Where g is the acceleration due to gravity, i,j=1,2,3…n, and n is the number of joints in the human body.

3. The lower limb exoskeleton rehabilitation robot control system according to claim 2, characterized in that: The method of executing a dynamic model establishment strategy according to the design parameters and the biological parameters to obtain the control torque of the lower limb exoskeleton rehabilitation robot includes: Build the exoskeleton's dynamic model: in, is the modified inertia matrix, ΔM(θ(t)) represents the influence of the exoskeleton on the human inertia matrix, and θ(t) represents the joint angle of the lower limb exoskeleton rehabilitation robot; is the modified Coriolis moment matrix, Indicates the structural differences between the exoskeleton and the human body; is the modified gravity matrix, ΔG(θ(t)) represents the influence of the exoskeleton on the gravity matrix; τ is the control torque of the lower limb exoskeleton rehabilitation robot.

4. The lower limb exoskeleton rehabilitation robot control system according to claim 3, characterized in that: The method of executing the adaptive control signal design strategy to update the control torque of the lower limb exoskeleton rehabilitation robot includes: in, is an adaptive gain term, used to dynamically adjust the control input; Update the adaptive gain term Among them, α is a constant used to adjust the adaptive gain term, e θ is the angle error, is the velocity error, and is the adaptive gain term based on Γ0.

5. The lower limb exoskeleton rehabilitation robot control system according to claim 3, characterized in that: The multi-objective optimization control strategy is executed to optimize multiple adjustment objectives, including: The adjustment targets include a first adjustment target, a second adjustment target and a third adjustment target; Calculate the first adjustment target J1, in, is the weight of the i-th joint, θ i is the human joint angle of the i-th joint, θ i ' is the target joint angle of the i-th joint, i = 1, 2, 3...n, λ i is the weighting factor used to adjust the human joint velocity error, is the human joint velocity of the ith joint, is the target joint velocity of the i-th joint; Calculate the second adjustment target J2, J2 = ||τ||; Calculate the third adjustment target J3, Among them, τ i (t) is the torque on the i-th joint when the control torque τ is applied to the human body, and T is the total time for posture adjustment; Calculate the overall goal of the multi-objective optimization control strategy J = w1×J1+w2×J2+w3×J3, where w1+w2+w3=1, w1, w2, w3 are the first adjustment target weight, the second adjustment target weight, and the third adjustment target weight, respectively.

6. The lower limb exoskeleton rehabilitation robot control system according to claim 5, characterized in that: The executing of the multi-objective optimization control strategy to optimize multiple adjustment objectives also includes: Optimize the overall goal Calculate the gradient of the first adjustment target Calculate the gradient of the second adjustment target Calculate the gradient of the third adjustment target Calculate the gradient of the overall objective Set the gradient threshold; If the gradient of the total objective is less than the gradient threshold, the multi-objective optimization control strategy is stopped to obtain the optimal solution; If the gradient of the total target is greater than or equal to the gradient threshold, the total target is updated.

7. The lower limb exoskeleton rehabilitation robot control system according to claim 4, characterized in that: The execution stability evaluation strategy evaluates the stability of the lower limb exoskeleton rehabilitation robot control system, including: Use Lyapunov stability analysis function Indicates the degree to which the control system of the lower limb exoskeleton rehabilitation robot deviates from the target; Calculate the first derivative of the Lyapunov stability analysis function; Among them, ζ is a constant term; like The control system of the lower limb exoskeleton rehabilitation robot is stable.

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