A bio-integrated parallel hip joint rehabilitation exoskeleton control system and method

Abstract

The present invention discloses a bio-fusion parallel hip joint rehabilitation exoskeleton control system and method. The control system includes a human-computer interaction module, a control module, a data storage module, a sensor network module, a bus module, and a motor drive module. The control method is designed according to the above control system to control the movement of the parallel mechanism with patent number ZL202110356926.4, and finally drive the lower limbs to perform rehabilitation exercises. The present invention does not need to establish a complete exoskeleton dynamic model to simultaneously achieve dual control of human leg posture and hip joint restraint force. On the one hand, it ensures the safety of patients during rehabilitation exercises. On the other hand, it greatly reduces the amount of data calculation, so that the real-time performance of control tracking is effectively guaranteed.

Description

Technical Field

[0001] The present invention relates to a bio-fusion parallel hip joint rehabilitation exoskeleton control method and system, belonging to the field of wearable exoskeletons. Background Art

[0002] As the population ages, the number of people suffering from lower limb motor dysfunction due to functional decline, cardiovascular, and neurological diseases continues to rise year by year, increasing the demand for motor function recovery devices. Compared to traditional rehabilitation equipment, exoskeletons have attracted considerable attention due to their advantages such as light weight, flexibility, and high training efficiency. Hip exoskeletons, a typical type of exoskeleton, assist the human body in exercise and rehabilitation training by transmitting power to the lower limbs. They can effectively improve hip joint mobility and delay the decline of hip physiological function, and have gradually become a research hotspot.

[0003] Existing hip joint exoskeletons are mainly designed with serial mechanisms. Although such products have the advantages of simple structure, easy control, and no motion singularities, there is a deviation between the human and the machine posture. The deviation between the human and the machine causes uncontrollable interaction forces between the human and the machine, which leads to discomfort, pain, and even injury to the wearer during movement. To solve this problem, the inventors proposed a bio-coupled hip joint assisted exoskeleton (ZL202110356926.4). The exoskeleton uses a virtual constraint branch composed of the human hip bone, hip joint, and femur together with three RRPS branches to form a parallel mechanism with three rotational degrees of freedom. The parallel mechanism rotates around the center of the hip joint, and there is no problem of human-machine posture deviation.

[0004] When analyzing the strength of the aforementioned parallel mechanism, the structure and material of the three RRPS branches can be optimized or modified, but the lower limbs that serve as the restraining branches cannot be changed. Therefore, the inventors considered how to control the motion of the parallel mechanism (patent number ZL202110356926.4) while ensuring that the restraining force at the hip joint remains within acceptable limits and does not strain the human hip joint. Summary of the Invention

[0005] In order to solve the above technical problems, the present invention provides a bio-fusion parallel hip joint rehabilitation exoskeleton control system and method, which can control the movement of the parallel mechanism with patent number ZL202110356926.4, ensure that the constraint force at the hip joint is within the tolerable limit range, and will not strain the human hip joint.

[0006] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0007] A bio-integrated parallel hip joint rehabilitation exoskeleton control system, comprising a human-computer interaction module, a control module, a data storage module, a sensor network module, a bus module, and a motor drive module;

[0008] The human-computer interaction module includes a touch screen and a host computer, which are used to select the system's operating mode and adjust and set initialization parameters; the initialization parameters include exoskeleton initial position calibration, sensor network module initialization, storage module initialization, and motor drive module initialization; the human-computer interaction module transmits the initialization parameters to each module of the control system and transmits trajectory planning information to the control module;

[0009] The control module includes a microcontroller and functional peripherals, which are used for data processing and calculation of drive signals. It performs preliminary processing and noise reduction on the system status information collected by the sensor network module, obtains the system control signal through the fuzzy PD algorithm, and converts the control signal of the control module into a drive signal through the motor drive module, thereby driving the motor module to control the motor movement.

[0010] The data storage module includes a storage chip and a communication part, which is used to store trajectory planning information, motor operation information and various system status information obtained by the sensor network module after the above processing; the bus module is used for information exchange between modules.

[0011] A further improvement of the technical solution of the present invention is that the system status information collected by the sensor network module includes motor angle information, pressure information and human body posture information.

[0012] A further improvement of the technical solution of the present invention is that the human-computer interaction module includes a touch screen and a host computer, which are used to select the operating mode of the system and adjust and set the initialization parameters; wherein the initialization parameters include the initial position calibration of the exoskeleton, the initialization of the sensor network module, the initialization of the storage module, and the initialization of the motor drive module;

[0013] The sensor network module includes an encoder, a current sensor, an inertial measurement unit, and a pressure sensor. The encoder is installed on the motor to obtain the motor's angle and angular velocity information in real time. The current sensor includes a resistor connected in series with the drive circuit, an isolated sampling amplifier circuit, and an analog-to-digital conversion circuit, which is used to obtain the current information when the motor is running. The inertial measurement unit is strapped to the human leg to obtain the human leg's posture information in real time. The pressure sensor is arranged at the thigh strap to obtain the interaction force between the exoskeleton and the human leg.

[0014] The bus module includes a serial communication bus, a control local area network bus and a 485 bus.

[0015] In order to solve the above technical problems, another technical solution adopted by the present invention is:

[0016] A bio-fusion parallel hip joint rehabilitation exoskeleton control method specifically includes the following steps:

[0017] S1. Plan hip joint movement trajectory according to rehabilitation exercise requirements;

[0018] S2. Substitute the above trajectory planning results into the human-machine inverse kinematics model to solve the expected motion law of the motor in the joint space;

[0019] S3. Design a fuzzy adaptive PD controller with the goal of tracking the desired motion law of the motor;

[0020] S4. During hip joint movement, the force exerted by the exoskeleton on the human leg is collected using the pressure sensor arranged at the thigh strap;

[0021] S5. Substituting the installation azimuth angle and the measured value of the pressure sensor into the hip joint force model to obtain an estimated force value F of the hip joint;

[0022] S6. Compare the estimated value F with the force threshold F0. If F>F0, the output is regulated by the output regulator, and the regulated control parameters are then input into the motor drive system composed of the motor drive module and the motor. If F≤F0, there is no need to regulate the output, and the control parameters can be directly input into the motor drive system.

[0023] S7. The motor drives the end of the parallel hip joint exoskeleton to move, thereby driving the lower limbs to perform rehabilitation exercises.

[0024] A further improvement of the technical solution of the present invention is that the human-machine inverse kinematics model expression in step S2 is:

[0025] q=J -1 θ

[0026] Where q represents the rotation angle of the motor, θ represents the spatial posture angle of the hip joint, and J represents the Jacobian matrix.

[0027] A further improvement of the technical solution of the present invention is that the design process of the fuzzy adaptive PD controller in step S3 is:

[0028] The microcontroller calculates the pulse number of the encoder to obtain the real-time angle value θ of each motor. i (t), the motor expected value r i (t) and the real-time angle value θ i (t) to obtain the motor angle error value e(t), and perform differential operation on it to obtain the rate of change

[0029] Establish the scaling parameter K based on the expert rule p , differential parameter K d The adjusted fuzzy rule table is stored in the microcontroller; among them, the proportional parameter K p Use the principle of "large error, large gain; medium error, small gain; small error, stable gain" to adjust the differential parameter K. d According to the error e(t) and its rate of change The product is adjusted using the principle of "positive increase and negative decrease";

[0030] e(t), As the input variable of the fuzzy controller, K is obtained through fuzzy processing, fuzzy reasoning, and clarity operation. p and K d Adjustment value ΔK d and ΔK d and transmit it to the PD controller;

[0031] The PD controller calculates the duty cycle of the pulse width modulation signal used to control the motor, which is then transmitted to the motor driver after saturation limiting to control the motor movement.

[0032] A further improvement of the technical solution of the present invention is that the hip joint force model expression in step S5 is:

[0033]

[0034] Where, f O 、n O They represent the constraint force and constraint moment acting on the center of the hip joint; f C 、n C are the equivalent forces and moments of the lower leg and foot at the center of mass of the hip joint, respectively; f B 、n B are the equivalent forces and moments of the six pressure sensors at the center B of the thigh strap; a, ε, ω are the linear acceleration, angular acceleration, and angular velocity at the center of mass of the thigh, respectively; r O 、r B 、r C They represent the position vectors of the hip joint center O, the thigh strap center B, and the thigh mass center C in the fixed coordinate system respectively; m represents the mass of the thigh; g represents the gravitational acceleration vector; and I represents the inertia matrix of the thigh.

[0035] A further improvement of the technical solution of the present invention is that the output regulator is designed as follows:

[0036] U=U0+ΔU

[0037] Where U is the control signal of the motor through the output regulator; U0 is the output of the fuzzy PD controller; ΔU is the adjustment amount of the output regulator, and ΔU=KU lim ;

[0038] Wherein, K is the adjustment coefficient, and K=(F-F0) / F0; U lim is the output limit value.

[0039] Due to the adoption of the above technical solution, the technical advancements achieved by the present invention are:

[0040] (1) The present invention can obtain the constraint force and torque expressions at the hip joint by only analyzing the force on the thigh. The constraint force expression can be used to estimate the force on the hip joint during exercise, and the torque expression can be used to evaluate the amount of power provided by the exoskeleton for lower limb movement. This method of evaluating the force and power provided by the hip joint without establishing a complete dynamic model of the exoskeleton greatly reduces the amount of data calculation, so that the real-time performance of control tracking is effectively guaranteed. In addition, during the rehabilitation training process, by comparing the constraint force with the force threshold and making online real-time changes to the parameters in the output regulator, dual control of the leg posture and the hip joint constraint force can be achieved simultaneously, ensuring the safety of the patient during rehabilitation exercise.

[0041] (2) In order to meet the control requirements of fast following, the controller structure is simplified and the integral term in the adaptive fuzzy PID is removed, which avoids the system oscillation caused by integral saturation and enhances the stability of the system. In addition, in the fuzzy rule design, the proportional parameter K p The design is based on the principle of "large error, large gain; medium error, small gain; small error, stable gain". The differential parameter K d According to the error e(t) and its rate of change The product of is adjusted and the design is carried out using the principle of "positive increase and negative decrease", which enhances the speed of the system and the stability of tracking.

[0042] (3) The control system is modularly designed, which reduces the complexity of the exoskeleton system while ensuring the normal function of the system, so that the functional risks of each part cannot overflow, and the failure rate and maintenance difficulty are significantly reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 is a flow chart of the control method of the present invention;

[0044] Figure 2 It is the structural diagram of the fuzzy adaptive PD controller of the present invention;

[0045] Figure 3 This is a schematic diagram of a bio-fusion hip exoskeleton mechanism provided in an embodiment of the present invention;

[0046] Figure 4 yes Figure 3 Force analysis diagram of the middle hip joint;

[0047] Figure 5 It is a structural diagram of the control system of the present invention;

[0048] Figure 6 1 is a step response comparison diagram of the present invention;

[0049] Figure 7 This is the comparison diagram of the y=sin(2πt) tracking curve of the present invention

[0050] Figure 8 It is a comparison diagram of the rehabilitation gait tracking curve of the present invention. DETAILED DESCRIPTION

[0051] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0052] Example 1:

[0053] like Figure 1 As shown, a bio-fusion parallel hip joint rehabilitation exoskeleton control method includes the following steps:

[0054] S1. Plan hip joint movement trajectory according to rehabilitation exercise requirements;

[0055] S2. Substitute the above trajectory planning results into the human-machine inverse kinematics model to solve the expected motion law of the motor in the joint space; the expression of the human-machine inverse kinematics model in step S2 is:

[0056] q=J -1 θ

[0057] q represents the motor angle, θ represents the hip joint spatial posture angle, and J represents the Jacobian matrix. Taking a single leg as an example, q = [q1 q2 q3], where q1 represents the angle of motor 1, q2 represents the angle of motor 2, and q3 represents the angle of motor 3. θ = [αβγ], where α represents the angle of hip flexion / extension, β represents the angle of hip adduction / abduction, and γ represents the angle of hip internal rotation / external rotation.

[0058] S3. Design a fuzzy adaptive PD controller with the goal of tracking the desired motion law of the motor;

[0059] S4. During hip joint movement, the force exerted by the exoskeleton on the human leg is collected using a pressure sensor located at the thigh strap (the pressure sensor is the pressure sensor described in patent number ZL202110450054.8);

[0060] S5. Substitute the installation azimuth angle and measurement value of the pressure sensor into the hip joint force model to obtain an estimated force value F of the hip joint;

[0061] S6. Compare the estimated value F with the force threshold F0. If F>F0, the output is regulated by the output regulator, and the regulated control parameters are then input into the motor drive system composed of the drive module and the motor. If F≤F0, there is no need to regulate the output, and the control parameters can be directly input into the motor drive system.

[0062] S7. The motor drives the end of the parallel hip joint exoskeleton to move, thereby driving the lower limbs to perform rehabilitation exercises.

[0063] like Figure 2 As shown, the process of designing the fuzzy adaptive PD controller in step S3 of the present invention is as follows:

[0064] S31, the microcontroller calculates the pulse number of the encoder to obtain the real-time angle value θ of each motor i (t), the motor expected value r i (t) and the real-time angle value θ i (t) to obtain the motor angle error value e(t), and perform differential operation on it to obtain the rate of change

[0065] S32. Establishing the proportional parameter K according to the expert rule p , differential parameter K d The adjusted fuzzy rule table is stored in the microcontroller. Among them, the proportional parameter K p Use the principle of "large error, large gain; medium error, small gain; small error, stable gain" to adjust the differential parameter K. d According to the error e(t) and its rate of change The product is adjusted using the principle of "positive increase and negative decrease".

[0066] S33, e(t), As the input variable of the fuzzy controller, K is obtained through fuzzy processing, fuzzy reasoning, and clarity operation. p and K d Adjustment value ΔK d and ΔK d and transmit it to the PD controller;

[0067] Let u(t) represent the control output value at time t, K p (0) and K d (0) represent the proportional parameter value and differential parameter value corresponding to the initial moment, respectively, then the expression of u(t) is:

[0068]

[0069] S34. Obtain the duty cycle value of the pulse width modulation signal for controlling the motor through PD controller operation, transmit it to the motor driver after saturation limiting, and then control the motor movement.

[0070] like Figure 3 As shown, this is a schematic diagram of the mechanism with patent number ZL202110356926.4; among them, branch 1, branch 2 and branch 3 are all RRPS branches, and the driving pairs of the three branches are all selected at the first R pair; OB is the human body restraint branch.

[0071] Taking the human body constraint branch chain as the research object, the force analysis is carried out on it, such as Figure 4 shown.

[0072] According to the Newton-Euler equation, the hip joint force model expression can be obtained as follows:

[0073]

[0074] Where, f O 、n O They represent the constraint force and constraint moment acting on the center of the hip joint; f C 、n C are the equivalent forces and moments of the lower leg and foot at the center of mass of the hip joint, respectively; f B 、n B are the equivalent forces and moments of the six pressure sensors at the center B of the thigh strap; a, ε, ω are the linear acceleration, angular acceleration, and angular velocity at the center of mass of the thigh, respectively; r O 、r B 、r C They represent the position vectors of the hip joint center O, the thigh strap center B, and the thigh mass center C in the fixed coordinate system respectively; m represents the mass of the thigh; g represents the gravitational acceleration vector; and I represents the inertia matrix of the thigh.

[0075] The above parameters r O 、r B 、r C , m, g, I are all known, a, ε, ω can be obtained through the inertial measurement unit; f C 、n C It can be obtained based on the inertia matrix of the calf and foot, motion parameter information and the coordinates of point C; B、n B It can be obtained based on the installation position of the pressure sensor, the pressure value and the coordinates of point B.

[0076] In an embodiment, the output regulator is designed as follows:

[0077] U=U0+ΔU

[0078] Where U is the control signal of the motor through the output regulator; U0 is the output of the fuzzy PD controller; ΔU is the adjustment amount of the output regulator, and ΔU=KU lim ;

[0079] Wherein, K is the adjustment coefficient, and K=(F-F0) / F0; U lim is the output limit value.

[0080] like Figure 5 As shown, a bio-fusion parallel hip joint rehabilitation exoskeleton control system includes: a control module, a human-computer interaction module, a data storage module, a sensor network module, a bus module, and a motor drive module.

[0081] The control module consists of a microcontroller and functional peripherals. It performs preliminary processing and denoising on the system status information (including motor angle information, pressure information, and human posture information) collected by the sensor network module, and obtains the system control signal through fuzzy PD algorithm calculation.

[0082] The human-computer interaction module consists of a touch screen and a host computer, which is used to select the system's operating mode and adjust the initialization parameters (including exoskeleton initial position calibration, sensor network module initialization, storage module initialization, and motor drive module initialization).

[0083] The data storage module consists of a storage chip and its communication part, and is used to store system information, motor angle current and other parameter information, pressure sensor information, and inertial measurement unit information.

[0084] The sensor network module consists of an encoder, a current sensor, an inertial measurement unit and a pressure sensor. The encoder is installed on the motor and is used to obtain the motor's angle and angular velocity information in real time; the current sensor includes a resistor connected in series with the drive circuit, an isolated sampling amplifier circuit and an analog-to-digital conversion circuit, which is used to obtain the current information when the motor is running; the inertial measurement unit is tied to the human leg to obtain the human leg posture information in real time; the pressure sensor is arranged at the thigh strap to obtain the interaction force between the exoskeleton and the human leg.

[0085] The bus module consists of a serial communication bus, a control LAN bus and a 485 bus, and is used for information exchange between modules.

[0086] like Figure 6As shown in the figure, using the step function as the system reference input, the step response curves of the fuzzy adaptive PD controller and the classic PD controller are obtained. It can be seen from the figure that compared with the classic PD control, the fuzzy adaptive PD controller has a smaller overshoot, a faster response speed, and a better control effect. Figure 7 As shown in Figure 1, using the y=sin(2πt) curve as the system reference curve, the tracking curves of the fuzzy adaptive PD controller and the classic PD controller are obtained. The fuzzy adaptive PD controller has a smaller tracking error. Figure 8 As shown in the figure, the human gait curve is used as the system reference curve to obtain the tracking curves of the fuzzy adaptive PD controller and the classic PD controller. It can be seen from the figure that throughout the gait cycle, the tracking error of the fuzzy adaptive PD controller remains at a low level, the tracking is smoother, and the tracking accuracy is higher.

Claims

1. A bio-integrated parallel hip joint rehabilitation exoskeleton control system, characterized by: Including human-computer interaction module, control module, data storage module, sensor network module, bus module, motor drive module; The human-computer interaction module includes a touch screen and a host computer, which are used to select the system's operating mode and adjust and set initialization parameters; the initialization parameters include exoskeleton initial position calibration, sensor network module initialization, storage module initialization, and motor drive module initialization; the human-computer interaction module transmits the initialization parameters to each module of the control system and transmits trajectory planning information to the control module; The control module includes a microcontroller and functional peripherals, which are used for data processing and calculation of drive signals. It performs preliminary processing and noise reduction on the system status information collected by the sensor network module, obtains the system control signal through the fuzzy PD algorithm, and converts the control signal of the control module into a drive signal through the motor drive module, thereby driving the motor module to control the motor movement. The data storage module includes a storage chip and a communication part, which is used to store trajectory planning information, motor operation information and various system status information obtained by the sensor network module after the above processing; the bus module is used for information exchange between modules; The control method of the bio-fusion parallel hip joint rehabilitation exoskeleton control system specifically includes the following steps: S1. Plan hip joint movement trajectory according to rehabilitation exercise requirements; S2. Substitute the above trajectory planning results into the human-machine inverse kinematics model to solve the expected motion law of the motor in the joint space; S3. Design a fuzzy adaptive PD controller with the goal of tracking the desired motion law of the motor; S4. During hip joint movement, the force exerted by the exoskeleton on the human leg is collected using the pressure sensor arranged at the thigh strap; S5. Substitute the installation azimuth angle and measurement value of the pressure sensor into the hip joint force model to obtain an estimated force value F of the hip joint; The hip joint force model expression is: Where, f O 、n O They represent the constraint force and constraint moment acting on the center of the hip joint; f C 、n C are the equivalent forces and moments of the lower leg and foot at the center of mass of the hip joint, respectively; f B 、n B are the equivalent forces and moments of the six pressure sensors at the center B of the thigh strap; a, ε, ω are the linear acceleration, angular acceleration, and angular velocity at the center of mass of the thigh, respectively; r O 、r B 、r C Respectively represent the position vectors of the hip joint center O, the thigh strap center B, and the thigh mass center C in the fixed coordinate system; m represents the thigh mass; g represents the gravity acceleration vector; I represents the inertia matrix of the thigh; S6. Compare the estimated value F with the force threshold F0. If F>F0, the output is regulated by the output regulator, and the regulated control parameters are then input into the motor drive system composed of the motor drive module and the motor. If F≤F0, there is no need to regulate the output, and the control parameters can be directly input into the motor drive system. S7. The motor drives the end of the parallel hip joint exoskeleton to move, thereby driving the lower limbs to perform rehabilitation exercises.

2. The bio-fusion parallel hip joint rehabilitation exoskeleton control system according to claim 1, characterized in that: The system status information collected by the sensor network module includes motor angle information, pressure information and human body posture information.

3. The bio-fusion parallel hip joint rehabilitation exoskeleton control system according to claim 1, characterized in that: The sensor network module includes an encoder, a current sensor, an inertial measurement unit, and a pressure sensor. The encoder is installed on the motor to obtain the motor's angle and angular velocity information in real time. The current sensor includes a resistor connected in series with the drive circuit, an isolated sampling amplifier circuit, and an analog-to-digital conversion circuit, which is used to obtain the current information when the motor is running. The inertial measurement unit is strapped to the human leg to obtain the human leg's posture information in real time. The pressure sensor is arranged at the thigh strap to obtain the interaction force between the exoskeleton and the human leg. The bus module includes a serial communication bus, a control local area network bus and a 485 bus.

4. The bio-fusion parallel hip joint rehabilitation exoskeleton control system according to claim 1, characterized in that: The human-machine inverse kinematics model expression in step S2 is: q=J -1 i Where q represents the rotation angle of the motor, θ represents the spatial posture angle of the hip joint, and J represents the Jacobian matrix.

5. The bio-fusion parallel hip joint rehabilitation exoskeleton control system according to claim 1, characterized in that: The design process of the fuzzy adaptive PD controller in step S3 is as follows: The microcontroller calculates the pulse number of the encoder to obtain the real-time angle value θ of each motor. i (t), the motor expected value r i (t) and the real-time angle value θ i (t) to obtain the motor angle error value e(t), and perform differential operation on it to obtain the rate of change Establish the scaling parameter K based on the expert rule p , differential parameter K d The adjusted fuzzy rule table is stored in the microcontroller; among them, the proportional parameter K p Use the principle of "large error, large gain; medium error, small gain; small error, stable gain" to adjust the differential parameter K. d According to the error e(t) and its rate of change The product is adjusted using the principle of "positive increase and negative decrease"; e(t), As the input variable of the fuzzy controller, K is obtained through fuzzy processing, fuzzy reasoning, and clarity operation. p and K d Adjustment value ΔK p and ΔK d and transmit it to the PD controller; The PD controller calculates the duty cycle of the pulse width modulation signal used to control the motor, which is then transmitted to the motor driver after saturation limiting to control the motor movement.

6. The bio-fusion parallel hip joint rehabilitation exoskeleton control system according to claim 1, characterized in that: The output regulator is designed as follows: U=U0+ΔU Where, U is the control signal of the motor through the output regulator; U0 is the output of the fuzzy PD controller; ΔU is the adjustment amount of the output regulator, and ΔU=KU lim ; Wherein, K is the adjustment coefficient, and K=(F-F0) / F0; U lim is the output limit value.

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