A Control Method and System for Rehabilitation Robots Based on Power-Integral Method

Abstract

Embodiments of the present invention relate to the field of robot technology, and disclose a rehabilitation robot control method and system based on power integral. Among them, the control method includes: responding to the interaction action of an operator using the rehabilitation robot to obtain an interaction torque; determining a position error and a speed error according to the actual position and the target position; calculating a control torque based on the power integral algorithm according to the position error, the speed error and the interaction torque; applying the control torque to the rehabilitation robot. Embodiments of the present invention use low-power regulation of power integral to achieve fast convergence of large errors and stable convergence of small errors, and rely on the sublinear scaling characteristic of 0 < p < 1 to achieve non-linear adaptive regulation of errors.

Description

Technical Field

[0001] This invention relates to the field of robotics, specifically to a control method and system for a rehabilitation robot based on power integral. Background Technology

[0002] As an important branch of the medical robotics field, rehabilitation robots have been widely used in the motor function rehabilitation training of patients with stroke, spinal cord injury, etc.

[0003] Its core mission is to guide or assist patients in completing specific actions while ensuring safety, thereby promoting the reconstruction of neurological and motor functions.

[0004] In this process, the robot system and the patient form a tightly coupled human-computer interaction system, and its control performance directly affects the training effect and the patient experience.

[0005] Taking lower limb exoskeleton rehabilitation robots (LLRRs) as an example, the key requirements for LLRRs as alternatives to rehabilitation therapists include teaching functionality, individual adaptability, and functional stability and reliability. In other words, LLRRs need to have precise trajectory tracking capabilities, meaning they must accurately learn the therapist's skills according to the rehabilitation plan to help patients complete the specified gait trajectory. Secondly, LLRRs need to work closely with patients. Because patients' limb dynamics models are difficult to detect, and patients' limb geometry, weight, motor skills, and muscle activation levels vary individually, and exoskeleton model measurements are inaccurate, these factors lead to inaccurate dynamic model parameters. Therefore, the control algorithm of LLRRs must have strong robustness. Thirdly, during training, due to patient muscle spasms or active participation, or interference from other environmental forces, the control system of LLRRs must have sufficient stability to ensure functional stability and reliability.

[0006] Both Fixed-Time Convergence Control (FixedIDC) and Finite-Time Convergence Control (RFTCC) can achieve faster convergence and stronger robustness, which is of great significance for improving the steady-state accuracy and convergence speed of the control system.

[0007] However, the commonly used fixed-time convergence control algorithm is as follows: The finite-time convergence control algorithm is as follows: Both employ homogeneous control algorithms. These algorithms require precise knowledge of the system model. The existence and calculation of homogeneity strictly depend on the precise structure and parameters of the model. When the system has unmodeled dynamics, parameter uncertainties, or external disturbances, the homogeneity of the system will be disrupted, causing the theoretical homogeneity to be undefinable or invalid. Summary of the Invention

[0008] To address the aforementioned shortcomings, this invention discloses a robot control method and system based on power integral, which can improve the anti-interference and robustness of rehabilitation robots.

[0009] The first aspect of this invention discloses a control method for a rehabilitation robot based on power integral, comprising: The system acquires interactive torque in response to the interactive actions of the operator using the rehabilitation robot. Determine the position error and velocity error based on the actual position and the target position: in, For positional error, For speed error, For the target location, For actual location, For actual speed, This is the actual acceleration; Based on the position error, velocity error, and interaction torque, the control torque is calculated using a power-law integral algorithm. : in, The nominal inertia matrix, The nominal Coriolis force and centrifugal force matrix, For the nominal gravity term, The torque is the interaction torque; K1 and K2 are the first positive constant and the second positive constant, respectively; g and p are the first exponent and the second exponent, respectively, and p = (2-g) / g, 1 < g < 2; The control torque is applied to the rehabilitation robot.

[0010] As an optional implementation, in the first aspect of the present invention, K1=K2=15.

[0011] As an optional implementation, in the first aspect of the present invention, g = 1.3-1.5.

[0012] As an optional implementation, in a first aspect of the present invention, the interactive torque is acquired by a torque sensor mounted on a rehabilitation robot.

[0013] As an optional implementation, in a first aspect of the present invention, applying the control torque to the rehabilitation robot includes: The control torque is converted into control current using the torque formula: in, To control the current, This is the torque coefficient; The control current is applied to the rehabilitation robot so that the rehabilitation robot can cooperate with the operator to complete rehabilitation training.

[0014] A second aspect of this invention discloses a rehabilitation robot control system based on power integral, comprising: The response unit is used to respond to the interactive actions of the operator using the rehabilitation robot and to acquire the interactive torque; The determining unit is used to determine the position error and velocity error based on the actual position and the target position. in, For positional error, For speed error, For the target location, For actual location, For actual speed, This is the actual acceleration; The calculation unit is used to calculate the control torque based on the position error, velocity error, and interaction torque using a power-law integral algorithm. : in, The nominal inertia matrix, The nominal Coriolis force and centrifugal force matrix, For the nominal gravity term, The torque is the interaction torque; K1 and K2 are the first positive constant and the second positive constant, respectively; g and p are the first exponent and the second exponent, respectively, and p = (2-g) / g, 1 < g < 2; A control unit for applying the control torque to the rehabilitation robot.

[0015] A third aspect of the present invention discloses an electronic device, comprising: a memory storing executable program code; a processor coupled to the memory; the processor calling the executable program code stored in the memory to execute the rehabilitation robot control method based on exponentiation integral disclosed in the first aspect of the present invention.

[0016] A fourth aspect of the present invention discloses a computer-readable storage medium storing a computer program, wherein the computer program causes a computer to execute the rehabilitation robot control method based on exponentiation integral disclosed in the first aspect of the present invention.

[0017] Compared with the prior art, the beneficial effects of the present invention are as follows: In the embodiment of the present invention, by adjusting the low power of the power integral, it is used to achieve fast convergence for large errors and stable convergence for small errors. Relying on the sublinear scaling characteristic of 0 < p < 1, the non-linear adaptive adjustment of the error is realized. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0019] Figure 1 is a schematic flowchart of a rehabilitation robot control method based on power integral disclosed in an embodiment of the present invention; Figure 2 is a principle block diagram of a rehabilitation robot control method based on power integral disclosed in an embodiment of the present invention; Figure 3 is a simulation effect diagram of knee joint position tracking of each algorithm without interference disclosed in an embodiment of the present invention; Figure 4 is a simulation effect diagram of knee joint position tracking error of each algorithm without interference disclosed in an embodiment of the present invention; Figure 5 is a simulation effect diagram of knee joint position tracking of each algorithm under interference disclosed in an embodiment of the present invention; Figure 6 is a simulation effect diagram of knee joint position tracking error of each algorithm under interference disclosed in an embodiment of the present invention; Figure 7 is a schematic structural diagram of a rehabilitation robot control system based on power integral provided in an embodiment of the present invention; Figure 8 is a schematic structural diagram of an electronic device provided in an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0020] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments.

[0021] All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0022] It should be noted that the terms first, second, third, fourth, etc. in the specification and claims of the present invention are used to distinguish different objects, rather than to describe a specific order.

[0023] The terms including and having and any variations thereof in the embodiments of the present invention are intended to cover non-exclusive inclusion. Exemplarily, a process, method, system, product or device including a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0024] The embodiments of the present invention are used to achieve fast convergence with large errors and stable convergence with small errors through power integral low-power adjustment. Relying on the sublinear scaling characteristic of 0 < p < 1, non-linear adaptive adjustment of errors is achieved. The following is a detailed description thereof.

[0025] Embodiment 1 Please refer to Figure 1 , Figure 1 which is a schematic flowchart of a rehabilitation robot control method based on power integral disclosed in the embodiments of the present invention.

[0026] Among them, the execution subject of the method described in the embodiments of the present invention is an execution subject composed of software or / and hardware. This execution subject can receive relevant information through wired or / and wireless means and can send certain instructions.

[0027] Of course, it can also have certain processing functions and storage functions.

[0028] This execution subject can control multiple devices, such as remote physical servers or cloud servers and related software, or can also be a local host or server and related software that performs relevant operations on devices placed somewhere.

[0029] In some scenarios, it can also control multiple storage devices, and the storage devices can be placed in the same place or different places as the devices.

[0030] As Figure 1 shown, the rehabilitation robot control method based on power integral includes the following steps: S110. In response to the interaction action of an operator using the rehabilitation robot, obtain the interaction torque.

[0031] Collecting the interaction torque through a torque sensor ensures that the interaction intention can be accurately and quickly captured, providing a real and reliable data basis for subsequent intelligent algorithm processing, and is the key hardware guarantee for the entire system to move from theory to engineering application.

[0032] Taking lower limb rehabilitation robots as an example, strain-type six-dimensional force / torque sensors installed on the end effector of the lower limb rehabilitation robot can be used to collect interactive torques. The accuracy can reach 0.5%-1% FS, and the sampling frequency can be 1ms, etc.

[0033] S120. Determine the position error and velocity error based on the actual position and the target position.

[0034] Please refer to Figure 2 As shown, the position error can be determined based on the actual position and the target position: Based on the actual position and the target position, the actual velocity and the target velocity can also be determined. Then, the difference between the actual velocity and the target velocity is used to obtain the velocity error. in, For positional error, For speed error, For the target location, For actual location, For actual speed, This is the actual acceleration.

[0035] S130. Based on the position error, velocity error, and interaction torque, calculate the control torque using a power-integral algorithm. .

[0036] In a preferred embodiment of the present invention, a finite-time controller with power integral is used to calculate the control torque, thereby overcoming the shortcomings of existing homogeneous control algorithms that cannot quantitatively analyze the relationship between disturbance and convergence domain and cannot provide a quantitative convergence time set, thus improving the anti-interference performance. Moreover, under uncertain model parameters and uncertain external loads, the speed loop of the servo control system is controlled to improve the robustness of the system.

[0037] The formula for the power-integral control algorithm is: in, The nominal inertia matrix, The nominal Coriolis force and centrifugal force matrix, For the nominal gravity term, The interaction torque is represented by K1 and K2, which are the first and second positive constants, respectively; g and p are the first and second exponents, respectively, and p = (2-g) / g, 1 < g < 2.

[0038] This formula can also be converted to: in, is the feedforward term, which has the same algorithmic expression as that of the prior art, while constitutes a non-linear error feedback term of double power, constitutes a composite variable with respect to the position error x1 and the velocity error x2.

[0039] For this composite variable, after the rehabilitation robot starts trajectory tracking or is disturbed, position error x1 and velocity error x2 are first generated. Performing the g-th power operation on the velocity error x2 belongs to super-linear amplification. When x2 is large (for example, the trajectory deviation is serious and the speed does not match), it will quickly amplify the feedback signal of the velocity error, enabling the system to respond preferentially to large velocity deviations; when x2 is small, the amplification effect weakens, avoiding over-regulation under small errors.

[0040] Performing a linear proportion on the position error x1 ensures that the position error is always involved in the feedback regulation, forms a collaborative constraint with the velocity error, and avoids the decoupling problem where the velocity error converges but the position error still exists, ensuring the position-velocity double precision of trajectory tracking.

[0041] The core function of this stage is to fuse the position and velocity errors into an overall adjustment signal, enabling the system to respond to the two core errors simultaneously, laying a foundation for the non-linear convergence of subsequent power-added integration.

[0042] Belongs to the low-power regulation of power-added integration, used to achieve fast convergence of large errors and stable convergence of small errors. Relying on the sub-linear scaling characteristic of 0 < p < 1, it realizes the non-linear adaptive regulation of errors: When the fused error signal is large, the sub-linear scaling of will keep the fused signal with a large feedback amplitude. Combining with the positive gain K1, the system will output a large control correction amount to quickly suppress large errors and achieve fast convergence under large errors, greatly shortening the initial time of error adjustment; when the fused error signal is small, the sub-linear scaling of will smoothly reduce the feedback amplitude, avoiding the oscillation overshoot problem of traditional linear control under small errors, enabling the error to approach zero in a stable and non-overshooting manner. At the same time, K1 can adjust the convergence speed in the small-error stage to ensure the steady-state accuracy; The non-linear regulation of this stage only depends on the error itself and fixed parameters, and has no direct coupling with the nominal dynamic matrix of the rehabilitation robot. Even if the model parameters are uncertain and the external load changes, it will not change the non-linear regulation characteristics of the p-th power, ensuring the stability of the error adjustment process.

[0043] The non-linear error feedback term of the double power treats all model uncertainties and external load disturbances as a lumped total disturbance. Since the parameter combination of g ∈ (1, 2) and 0 < p < 1 makes the power integral term satisfy the Lyapunov condition of finite-time stability, as long as this total disturbance is bounded, the controller can drive and maintain the tracking error within a very small neighborhood in finite time, and even converge to zero theoretically.

[0044] When g is close to 1 (such as 1 - 1.2), the value of p is close to 1. At this time, the linear term is dominant, the overall convergence process is smooth, the overshoot is small, but the time to reach the target neighborhood and the exact convergence time are both at a medium level.

[0045] When g is 1.3 - 1.5, fast convergence can be achieved in the large error stage, and the speed loop has significant high-gain characteristics in the small error stage, which can strongly suppress the residual error and achieve a fast speed to zero.

[0046] When g is greater than 1.5, the system will exhibit the characteristics of starting and stopping suddenly, that is, starting extremely fast and braking extremely hard when reaching the target.

[0047] Although this characteristic has the shortest convergence time theoretically, it is easy to excite mechanical resonances of the system, etc., requires extremely high bandwidth for the actuator, and may cause problems such as a decrease in patient comfort.

[0048] Therefore, in the preferred embodiment of the present invention, the selection range of the g value is 1.3 - 1.5.

[0049] K1 and K2 can be set as needed. For example, they can both be set to 15.

[0050] S140, apply the control torque to the rehabilitation robot.

[0051] Through the torque formula, convert the control torque into a control current and apply it to the rehabilitation robot so that the rehabilitation robot can cooperate with the operator to complete the rehabilitation training: <000{0221> Among them, is the control current, is the torque conversion coefficient. }

[0052] Taking knee joint rehabilitation as an example, simulate the robot control method of the present invention: Use several algorithms such as PD control, finite-time homogeneous convergence (RFTCC), fixed-time convergence (FixedIDC), sliding mode control (SMC), and the finite-time power integral convergence (RHFTC) of the embodiment of the present invention as comparison algorithms to perform simulations on the dynamic model of the knee joint rehabilitation robot to verify the convergence performance and anti-interference ability of the embodiment of the present invention.

[0053] Convergence performance: The target trajectory is a pre-acquired gait trajectory Xd with an initial position Xd(0)=1, while the actual position is X(0)=0. The above algorithms are used to obtain... Figure 3 The knee joint position tracking curve shown is used to compare the time required for convergence to Xd-X being less than 0.01. Figure 4 As shown), by Figure 4 The simulation results show that the convergence performance of the above algorithms is as follows: RHFTC <FixedIDC<RFTCC<SMC<PD。

[0054] Anti-interference capability: At 15-20s, M, C, and G are adjusted to 0.6 times their true values. At 20-25s, an interference force with a random magnitude and direction and a maximum amplitude of 20 N·m is applied. The above algorithms are then used to obtain... Figure 5 The knee joint position tracking curve shown is then obtained. Figure 6 The tracking errors of the various algorithms shown are due to Figure 6 The simulation results show that RHFTC has the strongest anti-interference capability.

[0055] Example 2 Please see Figure 7 , Figure 7 This is a schematic diagram of the structure of a rehabilitation robot control system based on power integral disclosed in an embodiment of the present invention.

[0056] like Figure 7 As shown, the rehabilitation robot control system based on power integral can include: Response unit 210 is used to respond to the interactive actions of the operator using the rehabilitation robot and acquire the interactive torque; Determining unit 220 is used to determine position error and velocity error based on actual position and target position: in, For positional error, For speed error, For the target location, For actual location, For actual speed, This is the actual acceleration; The calculation unit 230 is used to calculate the control torque based on the position error, velocity error, and interaction torque using a power integral algorithm. : in, The nominal inertia matrix, The nominal Coriolis force and centrifugal force matrix, For the nominal gravity term, The torque is the interaction torque; K1 and K2 are the first positive constant and the second positive constant, respectively; g and p are the first exponent and the second exponent, respectively, and p = (2-g) / g, 1 < g < 2; Control unit 240 is used to apply the control torque to the rehabilitation robot.

[0057] Example 3 Please see Figure 8 , Figure 8 This is a schematic diagram of the structure of an electronic device disclosed in an embodiment of the present invention.

[0058] Electronic devices can be computers and servers, and under certain circumstances, they can also be smart devices such as mobile phones, tablets, and monitoring terminals, as well as image acquisition devices with processing capabilities.

[0059] like Figure 8 As shown, the electronic device may include: Memory 310 storing executable program code; Processor 320 coupled to memory 310; The processor 320 calls the executable program code stored in the memory 310 to execute some or all of the steps in the rehabilitation robot control method based on exponentiation integral in Embodiment 1.

[0060] This invention discloses a computer-readable storage medium storing a computer program that causes a computer to perform some or all of the steps in the power integral-based rehabilitation robot control method of Embodiment 1.

[0061] This invention also discloses a computer program product, wherein when the computer program product is run on a computer, the computer performs some or all of the steps in the rehabilitation robot control method based on power integral in Embodiment 1.

[0062] This invention also discloses an application publishing platform, which is used to publish computer program products. When the computer program products are run on a computer, the computer executes some or all of the steps in the rehabilitation robot control method based on power integral in Embodiment 1.

[0063] In various embodiments of the present invention, it should be understood that the sequence number of each process does not necessarily imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0064] The unit described as a separate component may or may not be physically separate. The component shown as a unit may or may not be a physical unit, meaning it may be located in one place or distributed across multiple network units.

[0065] Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0066] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0067] The integrated unit can be implemented in either hardware or software functional units.

[0068] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-accessible memory.

[0069] Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a memory and includes several requests to cause a computer device (which may be a personal computer, server, or network device, specifically a processor in the computer device) to execute some or all of the steps of the methods described in the various embodiments of the present invention.

[0070] In the embodiments provided by the present invention, it should be understood that B corresponding to A means that B is associated with A, and B can be determined based on A.

[0071] However, it should also be understood that determining B based on A does not mean determining B solely based on A; B can also be determined based on A and / or other information.

[0072] Those skilled in the art will understand that some or all of the steps in the various methods of the embodiments described can be implemented by a program instructing related hardware. This program can be stored in a computer-readable storage medium, including read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-Erasable Programmable Read-Only Memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, disk storage, magnetic tape storage, or any other computer-readable medium capable of carrying or storing data.

[0073] The foregoing has provided a detailed description of the rehabilitation robot control method, system, electronic device, and storage medium based on power integral disclosed in the embodiments of the present invention. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A rehabilitation robot control method based on power integration, characterized by, include: The system acquires interactive torque in response to the interactive actions of the operator using the rehabilitation robot. Determine the position error and velocity error based on the actual position and the target position: wherein, is a position error, is a velocity error, is a target position, is an actual position, is an actual velocity, is an actual acceleration; According to the position error, the velocity error and the interaction torque, a control torque is calculated based on a power integral algorithm : wherein, is a nominal inertia matrix, is a nominal Coriolis and centrifugal force matrix, is a nominal gravitational term, is an interaction torque; K1 and K2 are respectively a first and a second positive constant; g and p are respectively a first and a second exponent, and p = (2 - g) / g, 1 < g < 2; The control torque is applied to the rehabilitation robot.

2. The power-integration-based rehabilitation robot control method according to claim 1, wherein K1=K2=15.

3. The power-integration-based rehabilitation robot control method according to claim 1, wherein g=1.3-1.5。 4. The rehabilitation robot control method based on power integral as described in any one of claims 1-3, characterized in that, The interactive torque is acquired by a torque sensor installed on the rehabilitation robot.

5. The rehabilitation robot control method based on power integral as described in any one of claims 1-3, characterized in that, Applying the control torque to the rehabilitation robot includes: The control torque is converted into control current using the torque formula: in, To control the current, This is the torque coefficient; The control current is applied to the rehabilitation robot so that the rehabilitation robot can cooperate with the operator to complete rehabilitation training.

6. A rehabilitation robot control system based on power integral, characterized in that, include: The response unit is used to respond to the interactive actions of the operator using the rehabilitation robot and to acquire the interactive torque; The determining unit is used to determine the position error and velocity error based on the actual position and the target position. in, For positional error, For speed error, For the target location, For actual location, For actual speed, This is the actual acceleration; The calculation unit is used to calculate the control torque based on the position error, velocity error, and interaction torque using a power-law integral algorithm. : in, The nominal inertia matrix, The nominal Coriolis force and centrifugal force matrix, For the nominal gravity term, The torque is the interaction torque; K1 and K2 are the first positive constant and the second positive constant, respectively; g and p are the first exponent and the second exponent, respectively, and p = (2-g) / g, 1 < g < 2; A control unit for applying the control torque to the rehabilitation robot.

7. An electronic device, characterized in that, include: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the rehabilitation robot control method based on exponentiation integral as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein the computer program causes a computer to execute the rehabilitation robot control method based on power integral as described in any one of claims 1 to 5.

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