On-demand assistive control system for exoskeleton rehabilitation robot with adaptive position constraints

Abstract

The present invention discloses an on-demand auxiliary control system for an exoskeleton rehabilitation robot with adaptive position constraints, which relates to the technical field of rehabilitation robot control. The control system comprises the following steps: collecting a patient's historical motion time-series data and recording the patient's corresponding motion intention; establishing a deep learning network model, using the historical motion time-series data as input samples and the corresponding motion intention as a label to train the model; collecting the patient's real-time motion time-series data, inputting it into the trained model to obtain the patient's real-time motion intention data, and calculating and generating target state parameters based on the real-time motion intention data; establishing a state parameter adaptive adjustment model, collecting current state parameters, and analyzing and generating control outputs for the motion joints based on the generated target state parameters; collecting the patient's real-time heart rate data, correcting the data in the control output, and issuing control instructions to the exoskeleton rehabilitation robot based on the corrected data.

Description

Technical Field

[0001] The present invention relates to the technical field of rehabilitation robot control, and in particular to an on-demand auxiliary control system for an exoskeleton rehabilitation robot with adaptive position constraints. Background Art

[0002] In recent years, with the increasing prevalence of an aging society and the growing number of patients suffering from sports injuries, the demand for rehabilitation therapy has increased significantly. Exoskeleton rehabilitation robots, as advanced assistive therapy tools, have garnered widespread attention due to their potential to enhance patient strength, improve motor function, and accelerate recovery. These robots can provide adjustable motion assistance, aiding patients in rehabilitation training and, to a certain extent, reducing the workload of medical staff. However, existing exoskeleton rehabilitation robots still face numerous technical challenges and limitations in practical application.

[0003] First, existing exoskeleton rehabilitation robots often use fixed-parameter control strategies in their control system design. This approach is difficult to adapt to the individual differences of different patients and their changing physiological states during the rehabilitation process. Factors such as the patient's muscle strength, range of motion, and motor control ability may vary due to individual differences. As a result, the same control parameters may not be able to meet the needs of all patients, thereby affecting the rehabilitation effect. Moreover, traditional exoskeleton robots lack a real-time feedback mechanism during training. Patients cannot obtain timely physiological status monitoring and sports performance feedback, making it difficult to make effective adjustments based on their own conditions. This lack of personalized and adaptive control design not only makes patients feel frustrated during the rehabilitation process, but may also lead to inefficient training and a prolonged rehabilitation period.

[0004] Secondly, existing technologies also have shortcomings in multi-sensor data fusion and dynamic control. Although some exoskeletons are equipped with multiple sensors, they often lack effective sensor data processing and fusion algorithms, resulting in insufficient real-time and accurate data. This results in inaccurate assessments of the patient's motion state, and the control system struggles to adjust in time to the patient's movements, which in turn affects the overall rehabilitation outcome.

[0005] Prior art publication CN116386811A discloses an adaptive position-constrained on-demand assistive control method and system for a rehabilitation robot. The method comprises: step S1, collecting the current position, angular velocity, and human-machine interaction torque of a human-machine interaction system; step S2, performing position constraint transformation based on the desired trajectory, position, and angular velocity to obtain a position error conversion value for the human-machine interaction system; step S3, linearly combining the position error conversion value and the human-machine interaction torque to obtain a human motion performance function; step S4, using the human motion performance function as an input variable to design a robot assistance level function with dead zone characteristics, saturation characteristics, and continuous differentiability; and step S5, using the robot assistance level function as a weighting factor for the human-machine interaction system controller to design a position-constrained controller. However, step S3 uses a linear combination to combine the position error conversion value with the human-machine interaction torque to obtain the human motion performance function. However, in actual human motion, the relationship between torque and position error is often nonlinear and affected by multiple complex factors. Therefore, a simple linear combination may not accurately reflect the actual performance of human motion, resulting in error accumulation or unsatisfactory control effects. At the same time, the patient's physiological parameters (such as heart rate, muscle fatigue, etc.) are not taken into account. Therefore, the effectiveness and practicality of the system are reduced by only performing general monitoring based on the current position, angular velocity and human-computer interaction torque of the human-computer interaction system.

[0006] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the Invention

[0007] The purpose of the present invention is to provide an on-demand auxiliary control system for an exoskeleton rehabilitation robot with adaptive position constraints to solve the problems raised in the above background technology.

[0008] To achieve the above object, the present invention provides the following technical solutions:

[0009] An on-demand assistive control system for an exoskeleton rehabilitation robot with adaptive position constraints, specifically including:

[0010] A historical data acquisition module is used to set up a comprehensive monitoring unit at the exoskeleton rehabilitation robot's motion joints to collect the patient's historical motion time series data and record the patient's corresponding motion intention. The comprehensive monitoring unit is composed of an acceleration sensor, an electromyography sensor, a force sensor, and a visual sensor. The motion intention includes the action type, motion direction, and motion speed;

[0011] The model training module is used to establish a deep learning network model. The historical motion time series data collected by the integrated monitoring unit is used as input samples, and the patient's corresponding motion intention is used as a label to train the deep learning network model to obtain a motion intention judgment model.

[0012] A target state analysis module is used to collect the patient's real-time motion time series data, input it into the completed training motion intention judgment model, obtain the patient's real-time motion intention data, and calculate the target state parameters based on the real-time motion intention data. The target state parameters include target joint angle, target motor speed, target joint torque and target applied force;

[0013] A control output generation module is used to establish a state parameter adaptive adjustment model, collect current state parameters, and analyze and generate control outputs for the exoskeleton rehabilitation robot's motion joints based on the generated target state parameters. The control outputs include output joint angles, output motor speeds, output joint torques, and output applied forces.

[0014] The control output correction module is used to collect the patient's real-time heart rate data, correct the parameters in the control output according to the control output of the motion joint, obtain the control adaptive output data, and issue control instructions to the exoskeleton rehabilitation robot based on the control adaptive output data.

[0015] Furthermore, the patient's historical motion timing data is collected, where the motion timing data includes: three-axis acceleration data, electromyographic signals, vertical and horizontal forces applied by the exoskeleton to the ground, and image data; the patient's corresponding motion intention is recorded, where the motion intention includes action type, motion direction, and motion speed, where the action type includes walking, standing, and squatting, and the motion direction includes forward, backward, and sideways movement. The action type and motion direction are encoded, and standing, walking, and squatting are encoded as 0, 1, and 2, respectively, and forward, backward, and sideways movement are calibrated as 3, 4, and 5, respectively.

[0016] Furthermore, a deep learning network model is established based on the combination of convolutional neural network and recurrent neural network. The deep learning network model has a 6-layer network structure, including input layer, convolution layer, pooling layer, recurrent layer, fully connected layer and output layer, wherein the convolution layer is the convolutional neural network layer, and the recurrent layer is the recurrent neural network layer. Finally, a motion intention judgment model is obtained, whose input is motion time series data and output is motion intention.

[0017] Furthermore, target state parameters are calculated based on the real-time motion intention data. The target state parameters include joint angle, motor speed, joint torque, and applied force. The logic for calculating and generating the target state parameters is as follows:

[0018] For the target joint angle θ(t), the formula for calculating the target joint angle θ(t) is:

[0019] θ(t)=θ0+k θ *(d type (t)+d dir (t)+v des (t))

[0020] Where θ(t) is the target joint angle at time t, θ0 is the initial joint angle, and k θ is the scaling factor of the joint angle, d type (t) is the code of the action type at time t, d dir (t) is the code of the motion direction at time t, v des (t) represents the speed of movement at time t;

[0021] For the target motor speed V(t), the formula for calculating the target motor speed V(t) is:

[0022]

[0023] Where V(t) represents the target motor speed at time t, k V is the adjustment factor of the motor speed, Δt is the time step, θ pre represents the joint angle of the previous time step;

[0024] The force F(t) applied to the target is calculated using the formula:

[0025]

[0026] Where F(t) represents the applied force at time t, k f is the scaling factor of the applied force;

[0027] For the target joint torque τ(t), the formula for calculating the target joint torque τ(t) is:

[0028] τ(t)=J(θ(t)) T *(F(t)-c*V(t))

[0029] Where τ(t) represents the target joint torque at time t, J(θ(t)) represents the Jacobian matrix, c represents the damping coefficient, and T represents the transpose of the matrix;

[0030] Based on the calculated target state parameters, a target state vector is generated. The target state vector X ref (t)={θ(t),V(t),F(t),τ(t)}.

[0031] Furthermore, a state parameter adaptive adjustment model is established to collect current state parameters. Based on the generated target state parameters, the logic of the control output of the exoskeleton rehabilitation robot motion joint is analyzed and generated as follows: the current state parameters are collected to generate the current state vector, wherein the current state vector X(t)={θ s (t),V s (t),F s (t),τ s (t)}, based on the current state vector X(t) and the target state vector X ref The calculation error vector of (t) is based on the formula:

[0032] e(t+p)=X ref (t+p)-X(t+p)

[0033] Where e(t+p) represents the deviation between the current state and the target state of the system at time t+p, and p represents the number of future steps starting from the current time t.

[0034] Furthermore, a state parameter adaptive adjustment model is established, and the control output is obtained according to the error vector through the model predictive control algorithm, wherein the cost function Q of the model predictive control algorithm is constructed, and the expression of the cost function Q is:

[0035]

[0036] Where N is the set prediction time domain, which represents the number of steps to be predicted in the future within the current time t, H is the state penalty matrix, R is the control penalty matrix, and u(t+p) is the control output vector at time t+p, which includes four-dimensional data of joint angle, motor speed, joint torque, and applied force.

[0037] In each control cycle, based on the cost function Q, the formula for solving the optimized control output vector is:

[0038]

[0039] In the formula, st represents the constraint. After solving the optimized control output vector, the first control input vector u(t) = {θ u (t),V u (t),F u (t),τ u (t)} is used as the optimized control output vector, where θ u (t),V u (t),F u (t) and τ u(t) represent the output joint angle, output motor speed, output joint torque and output applied force, respectively.

[0040] Furthermore, the patient's real-time heart rate data is collected, and the motor speed and applied force in the control output are corrected according to the control output vector of the motion joint. The formulas for the correction are:

[0041]

[0042] Where, F u (t) and V u (t) are the applied force and motor speed in the optimized control output vector, respectively, F adj (t) and V adj (t) are the corrected applied force and motor speed, HR represents the current heart rate, HR THR The heart rate threshold is set, HR max The maximum heart rate is obtained by combining the corrected applied force and motor speed with the control output vector to obtain the control adaptive output data, wherein the control adaptive output data Y(t)={θ Y (t),V adj (t),F adj (t),τ Y (t)}, where θ Y (t) = θ u (t), τ Y (t) = τ u (t); Based on the control adaptive output data, control instructions are issued to the exoskeleton rehabilitation robot.

[0043] Compared with the prior art, the present invention has the following beneficial effects:

[0044] A deep learning network model is established, using historical motion time-series data collected by the integrated monitoring unit as input samples and the patient's corresponding motion intention as a label for training the deep learning network model. Real-time motion time-series data is collected and fed into the trained motion intention judgment model to obtain the patient's real-time motion intention data. Target state parameters are then calculated based on this real-time motion intention data. A state parameter adaptive adjustment model is established to collect current state parameters and, based on the generated target state parameters, analyze and generate control outputs for the exoskeleton rehabilitation robot's motion joints. The patient's real-time heart rate data is collected to modify the motor speed and applied force in the control outputs. Control commands are then issued to the exoskeleton rehabilitation robot based on these modified data. This solution predicts the patient's motion intention using a deep learning network. Deep learning networks typically consist of a multi-layered network structure, enabling the network to learn and represent highly complex nonlinear relationships. Furthermore, by monitoring the patient's heart rate data in real time, the system can make appropriate corrections in the control output stage. When the patient's physiological load exceeds a safe range, the system can automatically reduce motor speed or applied force to prevent excessive fatigue or potential sports injuries. This physiological state monitoring and feedback mechanism improves training safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 It is a schematic diagram of the overall structure of the device of the present invention. DETAILED DESCRIPTION

[0046] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to specific embodiments.

[0047] It should be noted that, unless otherwise defined, the technical or scientific terms used in the present invention should have the usual meanings understood by people with ordinary skills in the field to which the present invention belongs. The "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative position relationships. When the absolute position of the object being described changes, the relative position relationship may also change accordingly.

[0048] Example:

[0049] See also Figure 1 , the present invention provides a technical solution:

[0050] An on-demand assistive control system for an exoskeleton rehabilitation robot with adaptive position constraints, specifically including:

[0051] A historical data acquisition module is used to set up a comprehensive monitoring unit at the exoskeleton rehabilitation robot's motion joints to collect the patient's historical motion time series data and record the patient's corresponding motion intention. The comprehensive monitoring unit is composed of an acceleration sensor, an electromyography sensor, a force sensor, and a visual sensor. The motion intention includes the action type, motion direction, and motion speed;

[0052] The patient's historical motion timing data is collected, including: three-axis acceleration data, electromyographic signals, the vertical and horizontal forces applied by the exoskeleton on the ground, and image data; the center of gravity of the patient when standing is taken as the origin, the current direction of the patient's right hand is the positive direction of the x-axis, the direction vertical to the sky is the positive direction of the z-axis, and the y-axis direction is determined by the right-hand rule to establish a spatial coordinate system. The three-axis acceleration data refers to different acceleration data in the x-axis, y-axis and z-axis directions. The electromyographic signals (usually voltage signals) reflect the contraction and relaxation state of the muscles. The vertical and horizontal forces applied by the exoskeleton on the ground are used to analyze the patient's force-applying ability; the image data is used to capture the patient's dynamic posture and motion trajectory, and monitor the patient's movement patterns, including walking, standing, squatting, etc.

[0053] The patient's corresponding movement intention was recorded, and the movement intention included movement type, movement direction, and movement speed. Movement types included walking, standing, and squatting, and movement directions included forward, backward, and sideways movement. The movement type and movement direction were encoded, with standing, walking, and squatting encoded as 0, 1, and 2, respectively, and forward, backward, and sideways movement calibrated as 3, 4, and 5, respectively, for subsequent calculations.

[0054] The model training module is used to establish a deep learning network model. The historical motion time series data collected by the integrated monitoring unit is used as input samples, and the patient's corresponding motion intention is used as a label to train the deep learning network model to obtain a motion intention judgment model.

[0055] A deep learning network model was established based on a combination of convolutional neural networks and recurrent neural networks. This model has a six-layer structure, including an input layer, a convolutional layer, a pooling layer, a recurrent layer, a fully connected layer, and an output layer. The convolutional layers are convolutional neural network layers, primarily used to extract local features from time series data. The recurrent layers are recurrent neural network layers, and the features extracted by the convolutional layers are fed into the recurrent neural network to process the temporal dependencies in the sequence. The model uses the ReLU activation function to introduce nonlinearity. The resulting model is a motion intention judgment model whose input is motion time series data and whose output is motion intent.

[0056] A target state analysis module is used to collect the patient's real-time motion time series data, input it into the completed training motion intention judgment model, obtain the patient's real-time motion intention data, and calculate the target state parameters based on the real-time motion intention data. The target state parameters include target joint angle, target motor speed, target joint torque and target applied force;

[0057] The target state parameters are calculated and generated based on the real-time motion intention data. The target state parameters include joint angle, motor speed, joint torque, and applied force. The logic for calculating and generating the target state parameters is as follows:

[0058] For the target joint angle θ(t), the formula for calculating the target joint angle θ(t) is:

[0059] θ(t)=θ0+k θ *(d type (t)+d dir (t)+v des (t))

[0060] Where θ(t) is the target joint angle at time t, θ0 is the initial joint angle, and k θ is the scaling factor of the joint angle, d type (t) is the code of the action type at time t, d dir (t) is the code of the motion direction at time t, v des (t) represents the movement speed at time t; the scaling factor k of the joint angle θ Used to adjust the desired joint angle response in the control system, usually used to improve control accuracy and smoothness, usually between 0.1 and 1.0.

[0061] For the target motor speed V(t), the formula for calculating the target motor speed V(t) is:

[0062]

[0063] Where V(t) represents the target motor speed at time t, k V is the adjustment factor of the motor speed, Δt is the time step, θ pre Indicates the joint angle of the previous time step; the adjustment factor k of the motor speed V This determines how responsive the motor speed is to changes in joint angle, affecting the dynamic response of the robot arm. It typically ranges from 0.5 to 2.0, depending on the motor's response characteristics and load.

[0064] The force F(t) applied to the target is calculated using the formula:

[0065] F(t)=kf *(d type (t)+v des (t))

[0066] Where F(t) represents the applied force at time t, k f is the scaling factor of the applied force; the scaling factor k of the applied force f Used to adjust the response of the force applied to the joint, commonly used in force control systems. Generally between 0.1 and 1.0.

[0067] For the target joint torque τ(t), the formula for calculating the target joint torque τ(t) is:

[0068] τ(t)=J(θ(t)) T *(F(t)-c*V(t))

[0069] Where τ(t) represents the target joint torque at time t, J(θ(t)) represents the Jacobian matrix, c represents the damping coefficient, and T represents the transpose of the matrix;

[0070] Based on the calculated target state parameters, a target state vector is generated. The target state vector X ref (t)={θ(t),V(t),F(t),τ(t)}.

[0071] A control output generation module is used to establish a state parameter adaptive adjustment model, collect current state parameters, and analyze and generate control outputs for the exoskeleton rehabilitation robot's motion joints based on the generated target state parameters. The control outputs include output joint angles, output motor speeds, output joint torques, and output applied forces.

[0072] Establish a state parameter adaptive adjustment model, collect current state parameters, and analyze and generate the control output of the exoskeleton rehabilitation robot motion joint based on the generated target state parameters: collect current state parameters and generate the current state vector, the current state vector X(t) = {θ s (t),V s (t),F s (t),τ s (t)}, based on the current state vector X(t) and the target state vector X ref The calculation error vector of (t) is based on the formula:

[0073] e(t+p)=X ref (t+p)-X(t+p)

[0074] Where e(t+p) represents the deviation between the current state and the target state of the system at time t+p, and p represents the number of future steps starting from the current time t.

[0075] A state parameter adaptive adjustment model is established, and the control output is obtained through the model predictive control algorithm according to the error vector. The cost function Q of the model predictive control algorithm is constructed, and the expression of the cost function Q is:

[0076]

[0077] Where N is the set prediction time domain, which represents the number of steps to be predicted in the future within the current time t, H is the state penalty matrix, R is the control penalty matrix, and u(t+p) is the control output vector at time t+p, which includes four-dimensional data of joint angle, motor speed, joint torque, and applied force.

[0078] The state penalty matrix H is usually a symmetric positive definite matrix that represents the weight of each state variable. The weight of each state is determined by analyzing the impact of each state on the final performance of the system. The control penalty matrix R is also a symmetric positive definite matrix that represents the weight of the control input. The two penalty matrices can be obtained by adjusting the values of H and R through experiments and simulations, observing system performance, or using empirical rules to set H and R. This is a common practice.

[0079] In each control cycle, based on the cost function Q, the formula for solving the optimized control output vector is:

[0080]

[0081] In the formula, st represents the constraint. After solving the optimized control output vector, the first control input vector u(t) = {θ u (t),V u (t),F u (t),τ u (t)} is used as the optimized control output vector, where θ u (t),V u (t),F u (t) and τ u (t) represent the output joint angle, output motor speed, output joint torque and output applied force, respectively.

[0082] After solving the optimization problem, a series of control output vectors u(t), u(t+1), …, u(t+N-1) are obtained. Since MPC is a rolling horizon control strategy, only the first control output u(t) needs to be executed and then re-optimized in the next control cycle.

[0083] In the formula for solving the optimized control output vector, different constraints can also be added, such as the range of joint angles and applied force. Constraints can be added and the constraint range can be changed according to the conditions of different patients.

[0084] The control output correction module is used to collect the patient's real-time heart rate data, correct the parameters in the control output according to the control output of the motion joint, obtain the control adaptive output data, and issue control instructions to the exoskeleton rehabilitation robot based on the control adaptive output data.

[0085] The patient's real-time heart rate data is collected, and the motor speed and applied force in the control output are corrected according to the control output vector of the moving joint. The formulas for the correction are:

[0086]

[0087] Where, F u (t) and V u (t) are the applied force and motor speed in the optimized control output vector, respectively, F adj (t) and V adj (t) are the corrected applied force and motor speed, HR represents the current heart rate, HR THR The heart rate threshold is set, HR max For the maximum heart rate, when the current heart rate is greater than the set heart rate threshold, the applied force and motor speed are reduced to avoid excessive fatigue or potential sports injuries. When the heart rate is less than the set heart rate threshold, the applied force and motor speed can be increased to improve the efficiency of rehabilitation exercise.

[0088] The corrected applied force and motor speed are combined with the control output vector to obtain the control adaptive output data, wherein the control adaptive output data Y(t)={θ u (t),V adj (t),F adj (t),τ u (t)}, based on the control adaptive output data, control instructions are issued to the exoskeleton rehabilitation robot.

[0089] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0090] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software depends on the specific application and design constraints of the technical solution.

[0091] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, and may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment as needed.

[0092] The above is only a specific implementation method of the present application, but the scope of protection of the present application is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed in this application, which should be covered by the scope of protection of the present application.

Claims

1. An on-demand assistive control system for an exoskeleton rehabilitation robot with adaptive position constraints, characterized in that: Specifically include: A historical data acquisition module is used to set up a comprehensive monitoring unit at the exoskeleton rehabilitation robot's motion joints to collect the patient's historical motion time series data and record the patient's corresponding motion intention. The comprehensive monitoring unit is composed of an acceleration sensor, an electromyography sensor, a force sensor, and a visual sensor. The motion intention includes the action type, motion direction, and motion speed; Collect the patient's historical motion time-series data, which includes: triaxial acceleration data, electromyographic signals, vertical and horizontal forces exerted by the exoskeleton on the ground, and image data; record the patient's corresponding motion intention, which includes movement type, movement direction, and movement speed, where movement types include walking, standing, and squatting, and movement directions include forward, backward, and sideways movement. The movement type and movement direction are encoded, with standing, walking, and squatting encoded as 0, 1, and 2, respectively, and forward, backward, and sideways movement as 3, 4, and 5, respectively. The model training module is used to establish a deep learning network model. The historical motion time series data collected by the integrated monitoring unit is used as input samples, and the patient's corresponding motion intention is used as a label to train the deep learning network model to obtain a motion intention judgment model. A deep learning network model is established based on a combination of a convolutional neural network and a recurrent neural network. The deep learning network model has a 6-layer network structure, including an input layer, a convolutional layer, a pooling layer, a recurrent layer, a fully connected layer, and an output layer. The convolutional layer is a convolutional neural network layer, and the recurrent layer is a recurrent neural network layer. Finally, a motion intention judgment model is obtained, whose input is motion timing data and whose output is motion intention. A target state analysis module is used to collect the patient's real-time motion timing data, input it into the trained motion intention judgment model, obtain the patient's real-time motion intention data, and calculate the target state parameters based on the real-time motion intention data. The target state parameters include target joint angle, target motor speed, target joint torque, and target applied force. The logic for calculating and generating target state parameters is as follows: For the target joint angle θ(t), the formula for calculating the target joint angle θ(t) is: θ(t)=θ0+k θ *(d type (t)+d dir (t)+v des (t)) Where θ(t) is the target joint angle at time t, θ0 is the initial joint angle, and k θ is the scaling factor of the joint angle, d type (t) is the code of the action type at time t, d dir (t) is the code of the motion direction at time t, v des (t) represents the speed of movement at time t; For the target motor speed V(t), the formula for calculating the target motor speed V(t) is: Where V(t) represents the target motor speed at time t, k V is the adjustment factor of the motor speed, Δt is the time step, θ pre represents the joint angle of the previous time step; The force F(t) applied to the target is calculated using the formula: F(t)=k f *(d type (t)+v des (t)) Where F(t) represents the applied force at time t, k f is the scaling factor of the applied force; For the target joint torque τ(t), the formula for calculating the target joint torque τ(t) is: τ(t)=J(θ(t)) T *(F(t)-c*V(t)) Where τ(t) represents the target joint torque at time t, J(θ(t)) represents the Jacobian matrix, c represents the damping coefficient, and T represents the transpose of the matrix; Based on the calculated target state parameters, a target state vector is generated. The target state vector X ref (t)={θ(t),V(t),F(t),τ(t)}; A control output generation module is used to establish a state parameter adaptive adjustment model, collect current state parameters, and analyze and generate control outputs for the exoskeleton rehabilitation robot's motion joints based on the generated target state parameters. The control outputs include output joint angles, output motor speeds, output joint torques, and output applied forces. The control output correction module is used to collect the patient's real-time heart rate data, correct the parameters in the control output according to the control output of the motion joint, obtain the control adaptive output data, and issue control instructions to the exoskeleton rehabilitation robot based on the control adaptive output data.

2. The on-demand assistive control system for an exoskeleton rehabilitation robot with adaptive position constraints according to claim 1, characterized in that: Establish a state parameter adaptive adjustment model, collect current state parameters, and analyze and generate the control output of the exoskeleton rehabilitation robot motion joint based on the generated target state parameters: collect current state parameters and generate the current state vector, the current state vector X(t) = {θ s (t),V s (t),F s (t),τ s (t)}, based on the current state vector X(t) and the target state vector X ref The calculation error vector of (t) is based on the formula: e(t+p)=X ref (t+p)-X(t+p) Where e(t+p) represents the deviation between the current state and the target state of the system at time t+p, and p represents the number of future steps starting from the current time t.

3. The on-demand assistive control system for an exoskeleton rehabilitation robot with adaptive position constraints according to claim 2, characterized in that: A state parameter adaptive adjustment model is established, and the control output is obtained through the model predictive control algorithm according to the error vector. The cost function Q of the model predictive control algorithm is constructed, and the expression of the cost function Q is: Where N is the set prediction time domain, which represents the number of steps to be predicted in the future within the current time t, H is the state penalty matrix, R is the control penalty matrix, and u(t+p) is the control output vector at time t+p, which includes four-dimensional data of joint angle, motor speed, joint torque, and applied force. In each control cycle, based on the cost function Q, the formula for solving the optimized control output vector is: In the formula, st represents the constraint. After solving the optimized control output vector, the first control input vector u(t) = {θ u (t),V u (t),F u (t),τ u (t)} is used as the optimized control output vector, where θ u (t),V u (t),F u (t) and τ u (t) represent the output joint angle, output motor speed, output joint torque and output applied force, respectively.

4. The on-demand assistive control system for an exoskeleton rehabilitation robot with adaptive position constraints according to claim 3, characterized in that: The patient's real-time heart rate data is collected, and the motor speed and applied force in the control output are corrected according to the control output vector of the moving joint. The formulas for the correction are: Where, F u (t) and V u (t) are the applied force and motor speed in the optimized control output vector, respectively, F adj (t) and V adj (t) are the corrected applied force and motor speed, HR represents the current heart rate, HR THR The heart rate threshold is set, HR max The maximum heart rate is obtained by combining the corrected applied force and motor speed with the control output vector to obtain the control adaptive output data, wherein the control adaptive output data Y(t)={θ Y (t),V adj (t),F adj (t),τ Y (t)}, where θ Y (t) = θ u (t), τ Y (t) = τ u (t); Based on the control adaptive output data, control instructions are issued to the exoskeleton rehabilitation robot.

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