Flexible rehabilitation robot robust event trigger instruction filtering control method and system

Abstract

The invention belongs to the technical field of rehabilitation robot control, and provides a robust event trigger instruction filtering control method and system for a flexible rehabilitation robot, and the method comprises the steps: constructing a kinetic equation of the flexible rehabilitation robot driven by a flexible actuator, and building a state space model according to the kinetic equation; designing a disturbance observer according to the state space model, and estimating existing external disturbance by using the disturbance observer; according to the current motion information and the disturbance observation result, a continuous disturbance effect indicator is designed, and beneficial disturbance and harmful disturbance generated on the tracking control target in the disturbance estimation result are distinguished; based on the disturbance observer and the disturbance action indicator, a controller is designed, the controller adopts a switching threshold event triggering strategy, and the controller is used for controlling the flexible rehabilitation robot so that the flexible rehabilitation robot can be stably converged to an expected track under the disturbance condition. According to the invention, disturbance information can be fully utilized, communication resources are optimized, control precision is ensured, and a differential explosion problem is solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of rehabilitation robot control, and particularly relates to a robust event-triggered command filtering control method and system for a flexible rehabilitation robot. Background Technique

[0002] The statements in this part only provide background technical information related to the present invention, and do not necessarily constitute prior art.

[0003] In recent years, robot technology has developed rapidly and has been widely applied in the field of medical rehabilitation. Among them, flexible rehabilitation robots driven by flexible actuators have received great attention due to their excellent performance and wide applicability. Compared with traditional rigid-driven rehabilitation robots, flexible rehabilitation robots usually adopt flexible actuators to drive, which can provide more gentle motion control, greatly improving the safety of human-computer interaction while ensuring execution accuracy. However, the introduction of flexible actuators doubles the dynamic order of the system compared with traditional rehabilitation robots, with more complex dynamic characteristics, bringing great challenges to system modeling and control.

[0004] In addition, during the actual application process, such robots are often affected by various unknown disturbances, such as system parameter perturbations, external environmental disturbances, etc. These disturbances not only reduce the accuracy of the control system but may also affect the stability of the system and even lead to control failure. Therefore, how to improve control accuracy and robustness while ensuring system stability has become a key challenge in the design of flexible rehabilitation robot control systems.

[0005] Currently, backstepping control methods have been widely applied in the field of flexible rehabilitation robots and have played an important role in improving the control accuracy and robustness of the system. However, the existing backstepping control methods still have the following key problems, which limit their practical application effects in complex environments:

[0006] 1) Backstepping control methods usually rely on the successive differential operations of virtual control signals, and this repeated differential operation is prone to the phenomenon of "differential explosion". On the one hand, differential explosion increases the computational burden of the system and the complexity of the control algorithm; on the other hand, due to the influence of sensor measurement noise, excessive differentiation may amplify the noise, thereby reducing the robustness of the control system and even leading to system instability.

[0007] 2) Backstepping control methods usually use disturbance observers to compensate for external disturbances, thereby enhancing the anti-disturbance ability of the system. However, these methods only focus on how to suppress harmful disturbances and do not deeply analyze the beneficial effects that disturbances may have on the control target, resulting in the control performance not reaching the optimal.

[0008] 3) In practical applications, flexible rehabilitation robots usually involve the coordinated operation of multiple sensors, actuators, and control units. The system needs to perform high-frequency data acquisition and control signal transmission. However, limited by the hardware communication resources, an excessively high data transmission frequency may lead to excessive bandwidth occupancy, affecting the real-time performance and stability of the system. In addition, redundant communication overhead will also increase the system energy consumption, which is not conducive to the long-term stable operation of the rehabilitation robot. Summary of the Invention

[0009] To solve the above problems, the present invention proposes a robust event-triggered command filtering control method and system for a flexible rehabilitation robot. The present invention can make full use of disturbance information, optimize communication resources, ensure control accuracy at the same time, solve the problem of differential explosion, improve the utilization efficiency of the system for disturbances, reduce the communication burden, and ensure that the tracking error converges to an arbitrarily small region.

[0010] According to some embodiments, the present invention adopts the following technical solutions:

[0011] A robust event-triggered command filtering control method for a flexible rehabilitation robot, comprising the following steps:

[0012] Construct the dynamic equation of a flexible rehabilitation robot driven by a flexible actuator, and establish a state space model according to the dynamic equation;

[0013] According to the state space model, design a disturbance observer, and use the disturbance observer to estimate the existing matched and unmatched disturbances;

[0014] According to the current motion information and disturbance observation results, design a continuous disturbance action indicator to distinguish the beneficial and harmful disturbances in the disturbance estimation results that affect the tracking control target;

[0015] Based on the disturbance observer and the disturbance action indicator, design a controller. The controller adopts a switching threshold event-triggered strategy, and uses the controller to control the flexible rehabilitation robot so that it can stably converge to the desired trajectory under disturbances.

[0016] As an alternative embodiment, the process of constructing the dynamic equation of a flexible rehabilitation robot driven by a flexible actuator includes: The flexible rehabilitation robot system includes a motor, an elastic element, and a load. The dynamic equation of the flexible rehabilitation robot system is:

[0017]

[0018] Wherein, q and θ are the position signals on the load side and the motor side respectively, M(q) is the load inertia, The Coriolis force is \(F_{c}\), \(K\) is the stiffness of the elastic element, \(J\) is the moment of inertia of the motor, \(B\) represents the motor damping, \(G(q)\) represents the system gravity, \(d_{1}\) and \(d_{2}\) are the external disturbances acting on the load side and the motor side respectively, and \(u\) is the control torque.

[0019] As an alternative implementation, according to the dynamic equation, the process of establishing the state - space model includes defining the system state \(x_{1}=q\), \(x_{3}=\theta - q\) and The system state - space model is described as:

[0020]

[0021] where \(b_{1}=M\) -1 (x_{1})K, \(f_{1}=-M\) -1 (x_{1})(C(x_{1},x_{2})x_{2}+G(x_{1})+Kx_{1}-d_{1})\) is the lumped mismatched disturbance of the system, \(b_{2}=J\) -1 , \(f_{2}=-J\) -1 Kx_{3}+J -1 (d_{2}-B(x_{4}+x_{2}))\) is regarded as the lumped matched disturbance of the system.

[0022] As an alternative implementation, according to the state - space model, the process of designing a disturbance observer to estimate the existing matched and mismatched disturbances by using the disturbance observer includes: The disturbance observer is:

[0023]

[0024] where \(x\) if (i = 2,3,4)\) and \(u\) f respectively represent the filtered signals of \(x\) i and \(u\), and the initial values are set as \(x\) if (0)=0\) and \(\tau\) f (0)=0\). \(\hat{f}\) is the estimate of the disturbance \(f\) j , \(\chi\) and are the adjustment parameters of the disturbance observer.

[0025] As an alternative implementation, according to the current motion information and the disturbance observation result, the process of designing a continuous - type disturbance - action indicator to distinguish the beneficial disturbance and the harmful disturbance generated in the disturbance estimation result for the tracking - control target includes, the continuous - type disturbance - action indicator is:

[0026]

[0027] where \(\varepsilon\) j and is a small positive constant, s2 and s4 are compensation tracking errors, which are related to the system motion state and will be specifically defined later. If then P1 > 0. The disturbance action indicator represents the consistency between the states s2, s4 and the disturbance estimation . When P1 < 0, the disturbance characteristic is beneficial, when P1 > 0 the disturbance characteristic is harmful, and P = 0 indicates that the disturbance characteristic has no effect on the system.

[0028] As an alternative implementation, based on the disturbance observer and the disturbance action indicator, the process of designing the controller includes:

[0029] Apply the backstepping technique to design the following coordinate transformation:

[0030] z1 = x1 - q d

[0031] z j = x j - α jf , j = 2, 3, 4

[0032] where q d is the desired trajectory in the rehabilitation training task, z i , i = 1, 2, 3, 4, is the system tracking error, and α jf is the output of the following command filter:

[0033]

[0034] where τ i > 0 is the filter coefficient, and α j is the virtual control signal;

[0035] To compensate for the filtering error α jf - α j , design the compensation signal η i (i = 1, 2, 3, 4) as:

[0036]

[0037] where k i > 0 is the design parameter, and the initial value of the compensation signal is set as η i (0) = 0;

[0038] Based on the coordinate transformation z i and the compensation signal η i , define the compensation tracking error as:

[0039] s i = z i - η i , i = 1, 2, 3, 4

[0040] The controller is designed such that:

[0041]

[0042] wherein, H(P j ), j = 1, 2, are continuous switching logic functions:

[0043]

[0044] In the formula, P j is the continuous disturbance action indicator designed above, ι j > 0 is a small constant.

[0045] As an alternative implementation, the controller adopts a switching threshold event-triggering strategy as follows:

[0046]

[0047] In the formula, p(t) = w(t) - u(t), w(t) is the event-triggering intermediate signal for switching between two cases, p(t) is the measurement error, 0 < β < 1, l1 and l2 are positive design parameters, and μ is the switching boundary;

[0048] When the condition |u(t)| < μ holds, the event-triggering intermediate signal w(t) is expressed as:

[0049]

[0050] In the formula, τ and are positive design parameters that satisfy If t ∈ [t k , t k+1 ), the control signal u(t) will maintain the previous value w(t k ), otherwise, when the condition is triggered, the control signal will be updated to w(t k+1 );

[0051] When |u(t)| > μ, the event-triggering intermediate signal w(t) is expressed as:

[0052]

[0053] wherein, τ and are positive design constants,

[0054] A robust event-triggered command filtering control system for a flexible rehabilitation robot, comprising:

[0055] A model construction module for constructing the dynamic equation of a flexible rehabilitation robot driven by a flexible actuator, and establishing a state space model according to the dynamic equation;

[0056] A disturbance observation module for designing a disturbance observer according to the state space model, and estimating existing matched and unmatched disturbances by using the disturbance observer;

[0057] A disturbance action indicator module for designing a continuous disturbance action indicator according to the current motion information and disturbance observation results, and distinguishing beneficial disturbances and harmful disturbances that affect the tracking control target in the disturbance estimation results;

[0058] A control module for designing a controller based on the disturbance observer and the disturbance action indicator, where the controller adopts a relative threshold event-triggering strategy, and using the controller to control the flexible rehabilitation robot so that it can stably converge to the desired trajectory under disturbances.

[0059] A computer-readable storage medium for storing computer instructions, which, when executed by a processor, complete the steps in the above method.

[0060] An electronic device includes a memory, a processor, and computer instructions stored on the memory and running on the processor. When the computer instructions are run by the processor, the steps in the above method are completed.

[0061] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0062] The present invention makes full use of beneficial disturbances, enabling the system to converge to the desired trajectory more quickly. It adopts an instruction filtering control method, designs a new robust event-triggering mechanism for the flexible rehabilitation robot system, reduces the unnecessary control update frequency, and strictly proves the stability of the closed-loop system based on the Lyapunov stability theory.

[0063] The present invention can effectively cope with disturbances in the system, ensure that the system state converges to an arbitrarily small area, thereby improving the stability and reliability of the rehabilitation robot control. It has a wide range of applications and great practical value.

[0064] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following specifically gives preferred embodiments and, in conjunction with the accompanying drawings, makes a detailed description as follows. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] The specification drawings forming a part of the present invention are used to provide a further understanding of the present invention. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation to the present invention.

[0066] Figure 1Schematic diagram of the robust event-triggered command filtering control method for a flexible rehabilitation robot in an embodiment;

[0067] Figure 2 Tracking result graph under the control method adopted in this embodiment;

[0068] Figure 3 Comparison result graph of the tracking error between the control method adopted in this embodiment and the traditional command filtering control method;

[0069] Figure 4 Time interval result graph of adjacent event triggers of the control method adopted in this embodiment;

[0070] Figure 5 System diagram of the robust event-triggered command filtering control for a flexible rehabilitation robot in an embodiment;

[0071] Figure 6 Structural diagram of an electronic device in an embodiment. Detailed implementation manners

[0072] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0073] It should be noted that the following detailed description is illustrative and is intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs.

[0074] It should be noted that the terms used herein are only for describing specific implementation manners and are not intended to limit the exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0075] In the case of no conflict, the embodiments in the present application and the features in the embodiments can be combined with each other.

[0076] Embodiment 1

[0077] A robust event-triggered command filtering control method for a flexible rehabilitation robot, as Figure 1 shown, includes the following steps:

[0078] Step S1: Construct the dynamic equation of a flexible rehabilitation robot driven by a flexible actuator, and establish a state space model according to the dynamic equation;

[0079] Step S2: According to the state - space model, design a disturbance observer to estimate the existing matched and unmatched disturbances by using the disturbance observer;

[0080] Step S3: According to the current motion information and disturbance observation results, design a continuous - type disturbance action indicator to distinguish the beneficial disturbances and harmful disturbances to the tracking control target in the disturbance estimation results;

[0081] Step S4: Based on the disturbance observer and the disturbance action indicator, design a controller. The controller adopts a switching - threshold event - triggered strategy, and uses the controller to control the flexible rehabilitation robot so that it can stably converge to the desired trajectory under disturbances.

[0082] In this embodiment, it also includes constructing a Lyapunov function and strictly proving the stability of the closed - loop system according to the Lyapunov stability theory to ensure that the system can stably converge to the desired trajectory in the presence of disturbances.

[0083] It should be noted that the system in this embodiment refers to the flexible rehabilitation robot system.

[0084] The following details the specific processes of each step.

[0085] Considering the influence of external disturbances, the dynamic equation of a flexible rehabilitation robot driven by a flexible actuator can be described as

[0086]

[0087] where q and θ are the position signals of the load and the motor side respectively, M(q), K, J, B are the load inertia, Coriolis force, stiffness of the elastic element, motor inertia and motor damping respectively. G(q) represents the system gravity, d1 and d2 are the external disturbances acting on the load side and the motor side respectively, and u is the control torque.

[0088] Furthermore, according to the dynamic equation, establish the state - space model of the system. Define the system state x1 = q, x3 = θ - q and The system state - space model is described as:

[0089]

[0090] where, b1 = M -1 (x1)K, f1 = - M -1 (x1)(C(x1,x2)x2 + G(x1)+Kx1 - d1) is the lumped unmatched disturbance of the system, b2 = J -1 , f2 = - J -1 Kx3 + J -1(d2 - B(x4 + x2)) is regarded as the lumped matching disturbance of the system.

[0091] Assumption 1: The mismatched disturbance f1 and the matched disturbance f2 are bounded, and their first - order derivatives and are also bounded.

[0092] Furthermore, according to the system state - space model, a disturbance observer in the following form is designed to estimate the matched and mismatched disturbances existing in the system:

[0093]

[0094] and

[0095]

[0096] where x if (i = 2, 3, 4) and u f respectively represent the filtered signals of x i and u, and the initial values are set as x if (0)=0 and τ f (0)=0. is the estimation of the disturbance f j , and χ and are the adjustment parameters of the disturbance observer.

[0097] Furthermore, combining the motion information of the current system and the disturbance - observation results, a continuous - type disturbance - action indicator is designed:

[0098]

[0099] where ε j and are small positive constants, s2 and s4 are compensation tracking errors, which are related to the system motion state and are specifically defined later. If then P1>0. The disturbance - action indicator represents the consistency between the states s2, s4 and the disturbance estimation . When P1<0, the disturbance characteristic is beneficial, when P1>0 the disturbance characteristic is harmful, and P = 0 indicates that the disturbance characteristic has no effect on the system.

[0100] Furthermore, a controller is designed and its stability is analyzed.

[0101] First, applying the back - stepping technique, the following coordinate transformation is designed

[0102]

[0103] where q d is the desired trajectory in a specific rehabilitation training task, z i, where \(i = 1, 2, 3, 4\) is the system tracking error, and \(\alpha\) jf is the output of the following instruction filter:

[0104]

[0105] where \(\tau\) i > 0 is the filter coefficient. \(\alpha\) j is the virtual control signal, and its specific form will be given later.

[0106] To compensate for the filtering error \(\alpha\) jf - \(\alpha\) j , the compensation signal \(\eta\) i (\(i = 1, 2, 3, 4\)) is designed as:

[0107]

[0108] where \(k\) i > 0 is the design parameter, and \(\eta\) i \((0)=0\). Then, based on the coordinate transformation \(z\) i and the compensation signal \(\eta\) i , the compensation tracking error is defined as:

[0109] \(s\) i = \(z\) i - \(\eta\) i , \(i = 1, 2, 3, 4\) (9)

[0110] Based on the disturbance observer and the disturbance action indicator, the controller is designed as

[0111]

[0112] where \(H(P\) j ), \(j = 1, 2\) is the continuous switching logic function

[0113]

[0114] where \(P\) j is the continuous disturbance action indicator designed in Equation (5), and \(\iota\) j > 0 is a small constant.

[0115] To reduce the update frequency of the controller, save energy or communication costs, the following switching threshold event-triggering mechanism is adopted:

[0116]

[0117] where \(p(t)=w(t)-u(t)\), \(w(t)\) is the event-triggering intermediate signal for switching between two situations, and \(p(t)\) is the measurement error. \(0 < \beta < 1\), \(l_1\) and \(l_2\) are positive design parameters. \(\mu\) is the switching boundary.

[0118] When the condition |u(t)| < μ holds, the event-triggered intermediate signal w(t) is expressed as:

[0119]

[0120] where τ and are positive design parameters satisfying If t ∈ [t k , t k+1 ), the control signal u(t) will maintain the previous value w(t k ). Otherwise, when the condition is triggered, the control signal will be updated to w(t k+1 ).

[0121] When |u(t)| > μ, the event-triggered intermediate signal w(t) is expressed as:

[0122]

[0123] where τ and are positive design constants,

[0124] The following is the theoretical derivation:

[0125] For the flexible rehabilitation robot system (2), satisfying the conditions of Assumption 1, the control method composed of the command filter (7), the filtering error compensation mechanism (8), and the controllers (10) and (11), and the switching threshold event-triggered strategy (12) can ensure that the tracking error converges to an arbitrarily small neighborhood of the zero point.

[0126] Before presenting the proof process, the following lemma is given:

[0127] Lemma 1: For any real number x, there exists ε > 0 such that holds.

[0128] Lemma 2: For any real number x and positive number ι > 0, the inequality holds

[0129]

[0130] Proof:

[0131] Step 1: According to formula (9), the first compensated tracking error s1 = z1 - η1, and its derivative can be obtained as:

[0132]

[0133] Since x2 = z2 + α 2f , it can be obtained that:

[0134]

[0135] Substituting the first subsystem in the filtering error compensation system (8) into Equation (16), we can obtain

[0136]

[0137] Substituting the virtual control signal α2 in Equation (10) into Equation (17), we can obtain

[0138]

[0139] Construct the Lyapunov function

[0140]

[0141] Taking the derivative of it and considering Equations (9) and (18), we can obtain:

[0142]

[0143] Step 2: According to Equation (9), s2 = z2 - η2, and its derivative can be obtained as:

[0144]

[0145] Since x3 = z3 + α 3f , we can obtain:

[0146]

[0147] Substituting the second subsystem in Equation (8) into (22), we can obtain:

[0148]

[0149] Substituting the virtual control signal α3 in Equation (10) into Equation (23), we can obtain:

[0150]

[0151] Construct the Lyapunov function:

[0152]

[0153] Considering Equations (9) and (24), the derivative of V2 can be obtained as:

[0154]

[0155] Considering that the inequality |H(P1)| < 1 holds and using the Young's inequality, we can obtain:

[0156]

[0157] Substituting Equation (27) into Equation (26), we get:

[0158]

[0159] Step 3: Considering Equation (9), the derivative of s3 is obtained as:

[0160]

[0161] Since x4 = z4 + α 4f , we get:

[0162]

[0163] Substituting the third subsystem in the filtering error compensation system (8) into Equation (30), we get:

[0164]

[0165] Substituting the virtual control signal α4 in Equation (10) into Equation (31), we get:

[0166]

[0167] Construct the Lyapunov function:

[0168]

[0169] Considering Equation (9), (32), the derivative of V3 can be obtained as

[0170]

[0171] Step 4: Considering Equation (9), the derivative of s4 is obtained as:

[0172]

[0173] Construct the Lyapunov function:

[0174]

[0175] Considering Equation (9), (35), the derivative of V4 can be obtained as:

[0176]

[0177] For the term s2b2u in Equation (37), the following two cases are discussed:

[0178] Case 1: When the condition |u(t)| < μ holds. From Equation (12), for the inequality holds. Therefore, there exist two time-varying functions and Make the following inequality hold:

[0179]

[0180] Therefore, it can be obtained that:

[0181]

[0182] According to Young's inequality and Lemma 1, s4bu can be rewritten as:

[0183]

[0184] Note: Because So take the minimum value.

[0185] Case 2: If |u(t)| > μ, under this condition, we consider a time-varying function satisfying and For time t ∈ [t k , t k+1 ), there is

[0186]

[0187] Therefore, s4bu can be rewritten as:

[0188]

[0189] To sum up, it can be obtained that:

[0190] s4bu ≤ bs4α5 + 0.557bh (43)

[0191] Substitute Equation (43) into (37), it can be obtained that:

[0192]

[0193] Substitute the virtual control rate α5 into Equation (44), it can be obtained that:

[0194]

[0195] Considering the inequality |H(P2)| < 1 holds, using Young's inequality, it can be obtained that:

[0196]

[0197] Substitute Equation (46) into (45), it can be obtained that:

[0198]

[0199] According to the properties of the command filter, there exists a constant Δ j >0, j = 2, 3, 4, such that η1(α 2f −α2) ≤ |η1|Δ2, η2b1(α 3f −α3) ≤ |η2|Δ3, η3(α 4f −α4) ≤ |η3|Δ4. Then, using Young's inequality:

[0200]

[0201] Substituting Equation (48) into Equation (47), we get:

[0202]

[0203] where

[0204]

[0205] According to Equation (49), we have:

[0206]

[0207] In the formula, exp(·) is the exponential function.

[0208] According to Equation (50), we get:

[0209]

[0210] This shows that:

[0211]

[0212] According to Equation (9), z i = s i + η i , and the tracking error will converge to the following region:

[0213]

[0214] In the formula, Θ and are positive constants. Thus, the proof process is completed.

[0215] Figure 2 shows the tracking results under the control method adopted in this embodiment. It can be seen from the figure that the system state remains bounded and the control torque is within a reasonable range. To further verify the superiority of the control method in this embodiment, a traditional command filtering control method is selected as the comparative control algorithm.

[0216] To ensure the fairness of comparison, the parameters of the two control algorithms are kept consistent. Simulation experiments are carried out on a single-joint flexible rehabilitation robot, and the tracking errors are compared and analyzed. The results are as Figure 3 shown. The simulation results show that the control method of this embodiment is superior to the comparative control method in terms of tracking accuracy and stability, verifying its excellent control performance.

[0217] In addition, Figure 4 shows the results of the adjacent event triggering time intervals of the control method adopted in this embodiment. It can be seen from the figure that the control rate is sent in an event-triggered manner, effectively avoiding the problem of excessive bandwidth occupation that may be caused by too high a data transmission frequency.

[0218] Embodiment 2

[0219] A robust event-triggered command filtering control system for a flexible rehabilitation robot, as Figure 5 shown, includes:

[0220] A model construction module for constructing the dynamic equation of a flexible rehabilitation robot driven by a flexible actuator, and establishing a state space model according to the dynamic equation;

[0221] A disturbance observation module for designing a disturbance observer according to the state space model and estimating the existing matching and mismatching disturbances by using the disturbance observer;

[0222] A disturbance action indicator module for designing a continuous disturbance action indicator according to the current motion information and disturbance observation results to distinguish the beneficial and harmful disturbances generated in the disturbance estimation results for the tracking control target;

[0223] A control module for designing a controller based on the disturbance observer and the disturbance action indicator. The controller adopts a relative threshold event-triggered strategy, and uses the controller to control the flexible rehabilitation robot so that it can stably converge to the desired trajectory under disturbances.

[0224] It can be understood that the above-mentioned various units / modules can be separately or all combined into one or several other units / modules to form, or some of them can be further split into multiple smaller units with functional division to form, which can achieve the same operation without affecting the realization of the technical effects of the embodiments of the present application.

[0225] The above modules of this system are divided based on logical functions. In actual applications, the function of one module can also be realized by multiple modules, or the functions of multiple modules are realized by one module.

[0226] Similarly, in other embodiments of the present application, the system may also include other units / modules. In practical applications, these functions can also be assisted by other units and can be realized through the cooperation of multiple units.

[0227] According to another embodiment of the present application, the system described in this embodiment can be constructed by running a computer program (including program code) that can execute the steps involved in the corresponding method described in Embodiment 1 on a general computing device such as a computer including processing elements and storage elements such as a Central Processing Unit (CPU), a Random Access Memory (RAM), and a Read Only Memory (ROM), and the method of Embodiment 1 can be implemented. The computer program can be recorded on a computer-readable recording medium, loaded into the above computing device through the computer-readable recording medium, and run therein.

[0228] Embodiment 3

[0229] This implementation provides an electronic device, as Figure 6 shown, the electronic device includes a processor 1001, a communication interface 1002, and a computer-readable storage medium 1003. Among them, the processor 1001, the communication interface 1002, and the computer-readable storage medium 1003 can be connected through a bus or other means.

[0230] Among them, the communication interface 1002 is used to receive and send data. The computer-readable storage medium 1003 can be stored in the memory of the electronic device. The computer-readable storage medium 1003 is used to store a computer program. The computer program includes program instructions. The processor 1001 is used to execute the program instructions stored in the computer-readable storage medium 1003.

[0231] The processor 1001 (or CPU (Central Processing Unit, central processor)) is the computing core and control core of the electronic device, and is adapted to implement one or more instructions. Specifically, it is adapted to load and execute one or more instructions to implement the corresponding method flow or corresponding function.

[0232] The processor 1001 is configured to execute the following process:

[0233] Construct the dynamic equation of a flexible rehabilitation robot driven by a flexible actuator, and establish a state space model according to the dynamic equation;

[0234] According to the state space model, design a disturbance observer, and use the disturbance observer to estimate the existing matching and mismatching disturbances;

[0235] According to the current motion information and the disturbance observation results, a continuous disturbance action indicator is designed to distinguish between the beneficial disturbances and harmful disturbances in the disturbance estimation results that affect the tracking control target;

[0236] Based on the disturbance observer and the disturbance action indicator, a controller is designed. The controller adopts a switching threshold event-triggering strategy, and the controller is used to control the flexible rehabilitation robot so that it can stably converge to the desired trajectory under disturbances.

[0237] Or the processes of steps S1 - S4 in Embodiment 1 are implemented, which will not be elaborated here.

[0238] Embodiment 4:

[0239] This implementation provides a computer-readable storage medium (Memory). The computer-readable storage medium is a memory device in an electronic device for storing programs and data. It can be understood that the computer-readable storage medium here can include both the built-in storage medium in the electronic device and, of course, the extended storage medium supported by the electronic device. The computer-readable storage medium provides a storage space, and this storage space stores the processing system of the electronic device.

[0240] Moreover, one or more instructions suitable for being loaded and executed by the processor are also stored in this storage space. These instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory; optionally, it can also be at least one computer-readable storage medium located far from the aforementioned processor.

[0241] In one embodiment, one or more instructions are stored in the computer-readable storage medium; the one or more instructions stored in the computer-readable storage medium are loaded and executed by the processor to implement the following process:

[0242] Construct the dynamic equation of the flexible rehabilitation robot driven by a flexible actuator, and establish a state space model according to the dynamic equation;

[0243] According to the state space model, design a disturbance observer, and use the disturbance observer to estimate the existing matched and unmatched disturbances;

[0244] According to the current motion information and the disturbance observation results, design a continuous disturbance action indicator to distinguish between the beneficial disturbances and harmful disturbances in the disturbance estimation results that affect the tracking control target;

[0245] Based on a disturbance observer and a disturbance effect indicator, a controller is designed. The controller adopts a switching threshold event-triggering strategy, and the controller is used to control a flexible rehabilitation robot so that it can stably converge to a desired trajectory under disturbances.

[0246] Or the process of steps S1 - S4 in Embodiment 1 is not described herein again.

[0247] Embodiment 5:

[0248] This implementation provides a computer program product or a computer program. The computer program product or the computer program includes computer instructions, and the computer instructions are stored in a computer-readable storage medium. A processor of an electronic device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the electronic device performs the following process:

[0249] Construct the dynamic equation of a flexible rehabilitation robot driven by a flexible actuator, and establish a state space model according to the dynamic equation;

[0250] Design a disturbance observer according to the state space model, and use the disturbance observer to estimate the existing matched and unmatched disturbances;

[0251] Design a continuous disturbance effect indicator according to the current motion information and the disturbance observation result to distinguish the beneficial disturbances and harmful disturbances generated in the disturbance estimation result for the tracking control target;

[0252] Based on the disturbance observer and the disturbance effect indicator, design a controller. The controller adopts a switching threshold event-triggering strategy, and the controller is used to control a flexible rehabilitation robot so that it can stably converge to a desired trajectory under disturbances.

[0253] Or the process of steps S1 - S4 in Embodiment 1 is not described herein again.

[0254] The above are only the preferred embodiments of the present invention and are not used to limit the present invention. For those skilled in the art, various changes and modifications can be made to the present invention. Any modifications, equivalent replacements, improvements, etc. made by those skilled in the art without creative efforts within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A robust event-triggered command filtering control method for a flexible rehabilitation robot, characterized in that, Including the following steps: Construct the dynamic equation of a flexible rehabilitation robot driven by a flexible actuator, and establish a state space model according to the dynamic equation; Design a disturbance observer according to the state space model, and use the disturbance observer to estimate the existing matched and unmatched disturbances; Design a continuous disturbance action indicator according to the current motion information and disturbance observation results to distinguish the beneficial and harmful disturbances in the disturbance estimation results that affect the tracking control target; Based on the disturbance observer and the disturbance action indicator, design a controller. The controller adopts a switching threshold event-triggering strategy, and use the controller to control the flexible rehabilitation robot so that it can stably converge to the desired trajectory under disturbances.

2. The robust event-triggered command filtering control method for a flexible rehabilitation robot according to claim 1, wherein The process of constructing the dynamic equation of a flexible rehabilitation robot driven by a flexible actuator includes: The flexible rehabilitation robot system includes a motor, an elastic element, and a load. The dynamic equation of the flexible rehabilitation robot system is: where q and θ are the position signals on the load side and the motor side respectively, M(q) is the load inertia, is the Coriolis force, K is the stiffness of the elastic element, J is the motor inertia, B is the motor damping, G(q) represents the system gravity, d1 and d2 are the external disturbances acting on the load side and the motor side respectively, and u is the control torque.

3. A robust event-triggered command filtering control method for a flexible rehabilitation robot as described in claim 1, characterized in that, According to the kinetic equation, the process of establishing the state-space model includes defining the system state x1 = q, x3 = θ - q and The system state-space model is described as: where, b1 = M -1 (x1)K, f1 = -M -1 (x1)(C(x1,x2)x2 + G(x1) + Kx1 - d1) is the lumped mismatched perturbation of the system, b2 = J -1 , f2 = -J -1 Kx3 + J -1 (d2 - B(x4 + x2)) is regarded as the lumped matched perturbation of the system.

4. A robust event-triggered command filtering control method for a flexible rehabilitation robot according to claim 1, characterized in that, The process of designing a disturbance observer according to the state space model and using the disturbance observer to estimate the existing matched and unmatched disturbances includes: The disturbance observer is: where x if (i = 2, 3, 4) and u f represent the filtered signals of x i and u respectively, and the initial values are set as x if (0) = 0 and τ f (0) = 0; is the estimation of the disturbance f j , χ and are the adjustment parameters of the disturbance observer.

5. A robust event-triggered command filtering control method for a flexible rehabilitation robot according to claim 1, characterized in that, The process of designing a continuous disturbance action indicator according to the current motion information and disturbance observation results to distinguish the beneficial and harmful disturbances in the disturbance estimation results that affect the tracking control target includes, The continuous disturbance action indicator is: where ε j and are small positive constants, s2 and s4 are compensation tracking errors, which are related to the system motion state and will be specifically defined later; if then P1 >

0. The disturbance action indicator represents the consistency between the states s2, s4 and the disturbance estimate ; when P1 < 0, the disturbance characteristic is beneficial, when P1 > 0 the disturbance characteristic is harmful, and P = 0 indicates that the disturbance characteristic has no effect on the system.

6. A robust event-triggered command filtering control method for a flexible rehabilitation robot as described in claim 1, characterized in that, The process of designing a controller based on the disturbance observer and the disturbance action indicator includes: Apply the backstepping technique to design the following coordinate transformation: z1 = x1 - q d z j = x j - α jf , j = 2, 3, 4 where q d is the desired trajectory in the rehabilitation training task, z i , i = 1, 2, 3, 4, is the system tracking error, and α jf is the output of the following command filter: α jf (0)＝α j (0) where τ i > 0 is the filter coefficient, and α j is the virtual control signal; To compensate for the filtering error α jf -α j , a compensation signal η i (i = 1, 2, 3, 4) is as follows: where k i > 0 is a design parameter, and η i (0) = 0; Based on the coordinate transformation z i and the compensation signal η i , the compensation tracking error is defined as: s i = z i -η i , i = 1, 2, 3, 4 The controller is designed as: where H(P j ), j = 1, 2, is a continuous switching logic function: where P j is the designed continuous disturbance action indicator, and ι j > 0 is a small constant.

7. A robust event-triggered command filtering control method for a flexible rehabilitation robot as described in claim 1, characterized in that, The switching threshold event-triggering strategy adopted by the controller is: Where p(t)=w(t)-u(t), w(t) is the event-triggering intermediate signal for switching between two cases, p(t) is the measurement error, 0<β<1, l1 and l2 are positive design parameters, and μ is the switching boundary; When the condition |u(t)|<μ holds, the event-triggering intermediate signal w(t) is expressed as: where τ and are positive design parameters that satisfy If t ∈ [t k , t k+1 ), the control signal u(t) will hold the previous value w(t k ), otherwise, when the condition |p(t)| ≥ β|u(t)| + l1 is triggered, the control signal will be updated to w(t k+1 ); When |u(t)|>μ, the event-triggering intermediate signal w(t) is expressed as: where τ and are positive design constants, 8. A robust event-triggered command filtering control system for a flexible rehabilitation robot, characterized in that, Including: A model construction module for constructing the dynamic equation of a flexible rehabilitation robot driven by a flexible actuator and establishing a state space model according to the dynamic equation; A disturbance observation module for designing a disturbance observer according to the state space model and using the disturbance observer to estimate the existing matched and unmatched disturbances; A disturbance action indicator module for designing a continuous disturbance action indicator according to the current motion information and disturbance observation results to distinguish the beneficial and harmful disturbances in the disturbance estimation results that affect the tracking control target; A control module for designing a controller based on the disturbance observer and the disturbance action indicator. The controller adopts a switching threshold event-triggering strategy, and use the controller to control the flexible rehabilitation robot so that it can stably converge to the desired trajectory under disturbances.

9. A computer-readable storage medium, characterized in that, For storing computer instructions, when the computer instructions are executed by a processor, the steps in the method described in any one of claims 1-7 are completed.

10. An electronic device, characterized in that, Comprising a memory and a processor, as well as computer instructions stored on the memory and running on the processor, when the computer instructions are run by the processor, the steps in the method according to any one of claims 1-7 are completed.

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