Variable stiffness actuator force-stiffness closed-loop control method and system based on force feedback

Abstract

The invention belongs to the field of robot control, and discloses a variable stiffness actuator force-stiffness closed-loop control method and system based on force feedback, and the method comprises a nonlinear PID control method, a stiffness closed-loop control method and a parameter setting method and process. Through the nonlinear PID control method based on the hyperbolic secant structure, the force control precision of a highly nonlinear system and in a complex disturbance environment can be effectively improved; according to the rigidity closed-loop control method based on force feedback, the system rigidity characteristics in different rigidity environments are effectively adjusted, and the system stability and the force control precision are improved; according to the parameter setting method of the control system based on the heuristic algorithm and the comprehensive target, various parameters of the control system can be effectively set, and the stability of the system is ensured. Due to the comprehensive technical characteristic, the control method has flexibility, high efficiency and adaptability in the technical field of robots.

Description

Technical Field

[0001] The present invention belongs to but is not limited to the field of robot control, and in particular relates to a force-stiffness closed-loop control method and system of a variable stiffness actuator based on force feedback. Background Art

[0002] With the increasing demand for surface processing accuracy of complex curved parts, such as aircraft engine blades, precision molds, and complex medical devices, grinding and polishing technology is facing higher and higher requirements. The traditional manual polishing method, which relies on manual operation, has problems such as high work intensity, poor repeatability, low processing efficiency, and difficult to control precision, and can no longer meet the needs of modern high-precision processing. Therefore, automated robotic grinding and polishing systems have gradually become a key direction of research and application. As the complexity of the processed shapes increases, the research on variable stiffness actuators at the end of grinding and polishing robots has gradually increased. Among them, force-stiffness feedback control technology is considered to be the key to improving the processing accuracy of robotic grinding and polishing systems. However, there are still many technical problems in the existing robotic grinding and polishing systems in terms of force control and stiffness control that need to be solved.

[0003] Dynamic matching problem of force-stiffness response: During complex surface machining, the robot end effector needs to maintain a stable contact force with the workpiece surface. However, due to the complex shape of the workpiece surface and large changes in environmental stiffness, traditional stiffness control methods cannot respond to these changes in real time, resulting in unstable contact force, which in turn affects the grinding and polishing quality. Existing force control systems mostly rely on the adjustment of static parameters and lack an adaptive adjustment mechanism for dynamic environmental stiffness.

[0004] Large error in environmental stiffness estimation: The current closed-loop stiffness control method relies on the robot grinding and polishing system to accurately estimate the environmental stiffness. However, there are a lot of noise interference and nonlinear factors in the processing environment, and the error in environmental stiffness estimation is large, which affects the accuracy of force feedback control. At the same time, although the variable stiffness actuator can adjust its own stiffness within a certain range, it cannot perceive and estimate the stiffness changes of the external environment in real time, resulting in limited performance of force-stiffness matching control.

[0005] Nonlinear response problem of control strategy: When dealing with complex surface processing, existing linear and nonlinear PID control methods have great limitations in control performance in the face of multi-source disturbances and nonlinear factors. In order to improve processing accuracy, the robotic grinding and polishing system requires a control strategy that can adapt to complex environments and has stronger nonlinear response capabilities to achieve precise adjustment of force feedback and stiffness control. Summary of the invention

[0006] In view of the problems existing in the prior art, the present invention provides a force-stiffness closed-loop control method and system for a variable stiffness actuator based on force feedback.

[0007] The present invention is implemented in this way: a variable stiffness actuator force-stiffness closed-loop control method based on force feedback, the method includes a nonlinear PID control method, a stiffness closed-loop control method and a parameter setting method and process.

[0008] Furthermore, the nonlinear PID control method is obtained by constructing a nonlinear gain function and multiplying the nonlinear gain function with a linear PID gain coefficient, which is expressed as:

[0009]

[0010] Where e is the error, which is expressed as the difference between the sensor measurement value and the expected value, K P Indicates the linear PID proportional coefficient, K I Indicates the linear PID integral coefficient, K D Indicates the linear PID differential coefficient, H P (e) represents the nonlinear gain function of the proportional coefficient and is related to the error, H I (e) represents the nonlinear gain function of the integral coefficient and is related to the error, H D (e) represents the nonlinear gain function of the differential coefficient and is related to the error, uc represents the desired output of the controller, and t is the time.

[0011] Further, the nonlinear gain function includes two forms, A and B, both of which are constructed by hyperbolic tangent function;

[0012] The expression of the A-type nonlinear gain function is H A (e) = 1 + h 11 (1-sech(h 12 e)), H A (e) is the value of the nonlinear gain function; h 11 and h 12 is the nonlinear parameter of the type I nonlinear gain function; e is the error, which is expressed as the difference between the sensor measurement value and the expected value;

[0013] Type A nonlinear gain function, when the feedback error e tends to 0, the nonlinear gain function tends to 1; when the feedback error e tends to infinity, the nonlinear gain function tends to 1+h 11 ;

[0014] The nonlinear gain function of type B is H B (e) is the value of the nonlinear gain function; h 21 and h 22 is the nonlinear parameter of the B-type nonlinear gain function; e is the error, which is expressed as the difference between the sensor measurement value and the expected value;

[0015] Type B nonlinear gain function, when the feedback error e tends to 0, the nonlinear gain function tends to 1; when the feedback error e tends to infinity, the nonlinear gain function tends to

[0016] By combining type A and type B nonlinear gain functions, a variety of nonlinear gain functions can be obtained; the nonlinear gain functions in the order of proportion, integration, and differentiation can be expressed as nine combinations of AAA, AAB, ABA, ABB, BAA, BAB, ABB, BBA, and BBB.

[0017] Furthermore, the stiffness closed-loop control method is a closed-loop stiffness feedback rate constructed based on force feedback, tangent function, and exponential function, and the stiffness of the system is changed by controlling the variable stiffness module through force feedback;

[0018] The closed-loop stiffness feedback rate discrete time domain expression:

[0019]

[0020] Among them, k vs (t) represents the expected stiffness at time t, k vs (t-1) represents the desired stiffness at time t, T is the discrete period of the control system, Δe represents the forward difference of the error e at time t, α1, α2 and α3 are the adjustment parameters of the stiffness control rate, and e is the error, which is expressed as the difference between the sensor measurement value and the expected value;

[0021] The closed-loop stiffness feedback rate is based on Evaluate the current rate of change of the force error; the hyperbolic tangent function replaces the sign function to reduce the chatter in the stiffness control caused by the force error noise interference; if the force error change rate is positive, the stiffness is increased to reduce the damping ratio and response time, thereby enhancing the system's ability to quickly adjust the force error. If the force error change rate is negative, the stiffness is reduced to increase the damping ratio and improve system stability, thereby enhancing the system's anti-interference performance;

[0022] The magnitude of the stiffness change depends on the exponential decay function (1-exp(α3e f (t))); As the absolute error changes, larger force errors lead to larger adjustments, while smaller errors produce smoother changes.

[0023] Further, the parameter setting method and process are as follows:

[0024] S1: Identified by the control system

[0025] S2: Linear parameter identification of nonlinear PID: K P , K I , K D

[0026] S3: Nonlinear gain function parameter identification of nonlinear PID: H P (e), H I (e), H D (e)

[0027] S4: Identification of stiffness control law parameters: α1, α2 and α3

[0028] The parameters of the controlled system in S1 can be identified by using the sweep frequency method or the pseudo-random sequence method to obtain the transfer function of the system;

[0029] The linear parameter identification in S2 can be carried out by the empirical accumulation method, the Ziegler-Nichols method, and the Cohen-Coon method;

[0030] The nonlinear gain function parameter identification in S3 is implemented by a heuristic optimization algorithm, such as a particle swarm algorithm or a genetic algorithm, but the algorithm optimization target is limited to the weighted sum of the output of the controlled system and the variance of the nonlinear PID control output as the objective function, and its expression is:

[0031] Where α and β are weights, and J is the optimization target; It is the variance of the output of the controlled system, which is used to judge the stability of the system output; The variance of the nonlinear PID control output is used to evaluate the anti-disturbance capability of the nonlinear PID control system. When the controlled system is relatively stable and the external disturbance is relatively small, a larger α value can be set. When the external disturbance is relatively large, a larger β value can be set.

[0032] Furthermore, in the process of parameter setting of the heuristic optimization algorithm in S3, the output expected value of the controlled system is set to 0, a random disturbance is set in the output of the controlled system, and the stiffness control law is not applied;

[0033] The stiffness control law parameter identification in S4 is implemented by a heuristic optimization algorithm, such as a particle swarm algorithm or a genetic algorithm, but the algorithm optimization target is limited to the weighted sum of the output of the controlled system and the variance of the output of the control system as the objective function, and its expression is:

[0034] Where α and β are weights, and J is the optimization target; It is the variance of the output of the controlled system, which is used to judge the stability of the system output; is the variance of the nonlinear PID control output, σ s 2 The stiffness control rate controls the output variance and is used to evaluate the anti-disturbance capability of the entire control system;

[0035] During the parameter setting process of the heuristic optimization algorithm in S4, the expected value of the output of the controlled system is set to 0, and a random disturbance is set in the output of the controlled system.

[0036] Another object of the present invention is to provide a variable stiffness actuator force-stiffness closed-loop control system based on force feedback based on the variable stiffness actuator force-stiffness closed-loop control method based on force feedback, the system comprising:

[0037] A nonlinear PID module is obtained by constructing a nonlinear gain function and multiplying the nonlinear gain function with a linear PID gain coefficient;

[0038] The stiffness control rate module is based on the closed-loop stiffness feedback rate constructed by force feedback, tangent function and exponential function. It controls the variable stiffness module through force feedback to change the system stiffness.

[0039] Parameter setting module, controlled system identification; linear parameter identification of nonlinear PID: K P , K I , K D ; Nonlinear gain function parameter identification of nonlinear PID: H P (e), H I (e), H D (e); Identification of stiffness control law parameters: α1, α2 and α3.

[0040] Another object of the present invention is to provide a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the variable stiffness actuator force-stiffness closed-loop control method based on force feedback.

[0041] Another object of the present invention is to provide a computer-readable storage medium storing a computer program, which, when executed by a processor, enables the processor to execute the steps of the variable stiffness actuator force-stiffness closed-loop control method based on force feedback.

[0042] Another object of the present invention is to provide an information data processing terminal, which is used to implement the variable stiffness actuator force-stiffness closed-loop control system based on force feedback.

[0043] In combination with the above technical solutions and the technical problems solved, the advantages and positive effects of the technical solutions to be protected by the present invention are as follows:

[0044] 1. Adaptive force-stiffness adjustment: By combining nonlinear PID control based on hyperbolic secant function and stiffness feedback adjustment, the system can adaptively respond to the dynamic environment stiffness changes in complex surface processing and achieve high-precision force control adjustment. This method effectively solves the problem of delayed response of force-stiffness matching in the existing technology and improves processing accuracy and efficiency.

[0045] 2. Enhanced anti-disturbance capability: The present invention uses heuristic optimization algorithms (such as particle swarm optimization or genetic algorithm) to adjust the control system parameters to ensure that the system output and the nonlinear PID output variance are optimally weighted, and that the complex disturbances and nonlinear factors can be accurately responded to. At the same time, through the combination of multiple nonlinear gain functions, this method has strong adaptive capability and anti-disturbance performance.

[0046] 3. Smooth adjustment of stiffness control: Through the design of stiffness closed-loop feedback rate, this system can smoothly adjust stiffness in the face of environmental noise to avoid vibration problems caused by noise. In particular, when the force error change rate is positive, the stiffness can be quickly increased to improve the response speed of the system, and when the error change rate is negative, the system will enhance stability by reducing stiffness, significantly improving the defects of insufficient system stability and flexibility in the existing technology.

[0047] 4. Flexibility of dual-mode nonlinear PID: The combination of type A and type B nonlinear gain functions provides a rich variety of nonlinear adjustment methods (such as nine combinations such as AAA, AAB, ABA, etc.), which can flexibly adjust the system according to different processing tasks, and enhance the adaptability under various complex working conditions. Especially in the fields of aerospace, precision mold manufacturing and medical equipment. Its flexible parameter setting method and efficient closed-loop control method can not only improve the processing accuracy of a single workpiece, but also significantly improve the batch processing efficiency of complex curved surface parts.

[0048] 5. Scalable optimization control: Particle swarm optimization and genetic algorithm are used to adjust PID parameters and stiffness control laws, which can quickly find the optimal control parameter combination according to different processing tasks and working conditions, and improve system response speed and control accuracy. At the same time, in a noisy environment, this system can appropriately enhance the anti-disturbance ability by adjusting the variance weight, and has a significant adaptive adjustment advantage.

[0049] Second, the expected benefits and commercial value of the technical solution of the present invention after transformation are:

[0050] By introducing nonlinear PID control and stiffness closed-loop control methods, the accuracy of force-stiffness matching can be significantly improved, and the stable contact force of the robot end in complex surface processing can be achieved, reducing surface defects and processing errors. It is particularly suitable for the surface processing needs of high-precision complex surface parts such as aircraft engine blades and complex medical equipment, which helps to improve product performance and reliability.

[0051] The proposed control strategy can adapt to changes in stiffness in complex environments, effectively deal with multi-source disturbances and nonlinear factors, and reduce the impact of external environmental changes on processing quality. It shows strong adaptability in the processing of workpieces of different materials, shapes and hardness, expanding the application scenarios of the system. The high robustness and stability of the system further reduce the incidence of equipment failures and improve the overall economic benefits. It meets the increasingly stringent demands for processing quality in fields such as aerospace, medical devices and high-end molds, and promotes the industrialization and upgrading of robot grinding and polishing system technology.

[0052] The technical solution of the present invention fills the technical gap in the industry at home and abroad: compared with the traditional static force control system, the present invention improves the system's ability to respond to external disturbances through real-time force-stiffness closed-loop control. The existing technology mostly relies on linear PID control, which cannot respond quickly to nonlinear disturbances and environmental stiffness changes in complex surface processing. The present invention combines nonlinear PID and heuristic algorithms to enable the system to maintain high precision and high efficiency under complex processing conditions, filling the technical gap at home and abroad.

[0053] The technical solution of the present invention solves the technical problems that people have been eager to solve but have never been able to solve successfully:

[0054] 1. Real-time dynamic matching of force-stiffness: In the grinding and polishing of complex curved parts, the traditional system cannot adjust the stiffness and contact force of the end effector in real time and stably, resulting in limited processing accuracy. The present invention successfully realizes adaptive dynamic adjustment of the external environment stiffness through the combination of force feedback closed-loop control and hyperbolic secant nonlinear PID, ensuring the stability of the contact force during the processing.

[0055] 2. Difficulty in estimating environmental stiffness: Due to noise interference and nonlinear factors, existing methods have large errors in estimating environmental stiffness, which affects the accuracy of force control. The stiffness closed-loop feedback rate proposed in this invention is based on the dynamic change of force error, and uses a hyperbolic secant function to reduce control vibration caused by noise, directly avoiding the estimation of environmental stiffness and solving this long-standing bottleneck problem.

[0056] 3. Balance between anti-disturbance capability and response speed: Traditional PID control methods are difficult to balance the rapid response and stability of the system when facing multi-source disturbances. The present invention implements a stiffness adjustment strategy driven by the force error change rate by constructing type A and type B hyperbolic secant nonlinear gain functions, so that the system achieves a good balance between anti-disturbance capability and rapid response.

[0057] 4. Efficient tuning of multivariable control parameters: The present invention uses a heuristic optimization algorithm (such as a particle swarm algorithm or a genetic algorithm) to tune multiple parameters of the control system, and uses weighted variance as the optimization target to ensure a high degree of consistency between the control output and the actual output of the system. This tuning method solves the problem of the difficulty of simultaneously optimizing multi-parameter systems in the prior art, making the control system more adaptable and robust.

[0058] The present invention overcomes multiple technical biases of traditional technologies in robot grinding and polishing systems by introducing a force-stiffness closed-loop control method, including insufficient stiffness adaptability in dynamic environments, low tolerance to noise interference, lack of nonlinear response capability, limited actuator stiffness adjustment range, and insufficient optimization capability in complex environments. By introducing a hyperbolic tangent nonlinear gain function and combining a heuristic algorithm for parameter setting, the stiffness feedback rate design is optimized to achieve rapid adaptation to changes in stiffness in the external environment, robust suppression of noise interference, and coordinated optimization of system performance under multi-source disturbances, thereby significantly improving the force-stiffness matching performance and anti-interference capability in complex surface processing, providing innovative technical support for high-precision and high-efficiency robot grinding and polishing.

[0059] Third, the existing technical problems solved by the technical solution of the present invention are:

[0060] In existing variable stiffness actuator systems, control systems often face problems such as inaccurate stiffness adjustment, slow response speed, system instability, and inability to adaptively adjust under dynamic load conditions. These problems will affect the performance of the actuator, especially in complex environments where the force and stiffness adjustment accuracy of the actuator is high. Traditional PID control methods, which do not take nonlinear factors into account, are prone to error accumulation, system response lag, and cannot work stably when the load changes. In addition, traditional stiffness control methods do not fully utilize force feedback for real-time stiffness adjustment, resulting in inaccurate coordination between system stiffness and force output in actual applications, and inability to achieve accurate force-stiffness closed-loop control.

[0061] Significant technical advancements of the present invention:

[0062] The force-stiffness closed-loop control method of the variable stiffness actuator based on force feedback proposed in the present invention significantly improves the performance of the variable stiffness actuator by introducing a nonlinear PID control method, a stiffness closed-loop control method and a parameter setting method. The specific improvements are reflected in the following aspects:

[0063] 1. Introducing nonlinear gain function: The present invention introduces a nonlinear gain function in PID control. Through the A-type and B-type gain functions of the hyperbolic tangent function, the gain coefficients of the proportion, integration, and differentiation can be dynamically adjusted to optimize the control accuracy. Especially in the case of large errors or small errors, the system response is smoother, avoiding the problems of overshoot and oscillation caused by traditional PID control when the error is large.

[0064] 2. Force feedback and stiffness adjustment closed-loop control: The stiffness closed-loop control method of the present invention is based on closed-loop stiffness feedback rate constructed by force feedback and nonlinear function, so that the stiffness of the actuator can be adjusted in real time, closely coordinated with the force output, and ensures the stability and response speed of the variable stiffness actuator under different load conditions. Through the stiffness feedback control in the discrete time domain, the system can more accurately adjust the desired stiffness and optimize the force-stiffness control characteristics of the actuator.

[0065] 3. Multi-parameter adjustment control strategy: The present invention can flexibly adjust the response speed and stability of the system in practical applications by adjusting multiple parameters of the stiffness control rate, including proportional, integral and differential control coefficients, so that the system can adapt to changing load environments, thereby providing efficient and stable force and stiffness control in a wide range of application scenarios.

[0066] Through these technological advances, the present invention can provide a more accurate, rapid and stable force-stiffness closed-loop control solution, significantly improving the performance of variable stiffness actuators in complex environments, and is suitable for mechanical control systems with high-precision requirements, such as robotics, intelligent manufacturing and other fields. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 The present invention provides a characteristic schematic diagram of a type A nonlinear gain function;

[0068] Figure 2 The present invention provides a characteristic schematic diagram of a B-type nonlinear gain function;

[0069] Figure 3 The present invention provides a characteristic schematic diagram of the stiffness control rate;

[0070] Figure 4 It is a schematic diagram of the setting process provided by the present invention;

[0071] Figure 5 It is a schematic diagram of nonlinear PID control parameter setting provided by the present invention;

[0072] Figure 6 It is a schematic diagram of setting the stiffness control rate parameters provided by the present invention;

[0073] Figure 7 It is a schematic diagram of the force control error simulation effect of the force-stiffness control provided by the present invention;

[0074] Figure 8 It is a schematic diagram of simulation of speed change of a controlled system of force-stiffness control provided by the present invention;

[0075] Fig. 9 It is a schematic diagram of a force-stiffness closed-loop control system of a variable stiffness actuator based on force feedback provided by the present invention;

[0076] Fig.10 It is a system diagram of the method verification equipment provided by the present invention;

[0077] Fig.11 It is a surface accuracy diagram after the method provided by the present invention is applied to robot grinding and polishing;

[0078] Fig.12 This is a comparison chart of force control accuracy after the method provided by the present invention is applied to robot grinding and polishing. DETAILED DESCRIPTION

[0079] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0080] Example 1: Robot arm force-stiffness control system

[0081] In industrial robots, especially in high-precision assembly and processing applications, the robot arm needs to accurately control the force and stiffness applied to the object. Traditional robot control systems usually achieve force control by fixing the stiffness, but in some complex tasks, the accuracy and real-time response capabilities of stiffness control cannot meet the requirements, especially when handling fragile, flexible or complex-shaped objects.

[0082] In this case, the variable stiffness actuator force-stiffness closed-loop control method based on force feedback provided by the present invention is applied to the control system of the robot arm. By integrating the nonlinear PID control method and the closed-loop stiffness feedback rate, the system can automatically adjust the force and stiffness according to the requirements of the task and perform real-time optimization during execution.

[0083] The specific steps include:

[0084] 1. The robot arm uses built-in sensors to measure the contact force and deformation of the object in real time.

[0085] 2. The motion of the robot arm is regulated by a nonlinear PID control method, and force and stiffness are precisely controlled, so that the robot can adjust force output according to the flexibility and shape changes of the object, avoiding excessive force and damage to the object.

[0086] 3. The stiffness feedback control system combines real-time force feedback to dynamically adjust the stiffness of the actuator to ensure that the robot can adapt to load changes under different operating conditions and ensure the stability and accuracy of the operation process.

[0087] This embodiment greatly improves the adaptability and accuracy of the robot under complex operations by precisely controlling force and rigidity, effectively reduces the risk of injury during the production process, and improves production efficiency and product quality.

[0088] Example 2: Intelligent rehabilitation treatment equipment

[0089] In rehabilitation medicine, traditional rehabilitation equipment cannot automatically adjust force and stiffness according to individual differences and changes in the condition of patients, resulting in limited treatment effects and difficulty in real-time feedback adjustment. Especially for patients with spinal and joint injuries, the force and stiffness during the rehabilitation process need to be flexibly adjusted according to the patient's recovery progress to avoid excessive load or inappropriate force.

[0090] The variable stiffness actuator force-stiffness closed-loop control method based on force feedback of the present invention is applied to intelligent rehabilitation treatment equipment. The equipment realizes precise adjustment and real-time feedback during the treatment process by integrating nonlinear PID control and stiffness closed-loop control technology. The specific steps include:

[0091] 1. The device sets the initial force and stiffness according to the patient's recovery condition and treatment plan, and uses sensors to monitor the patient's joint or spine responses in real time, such as force and angle changes.

[0092] 2. The nonlinear PID control method adjusts the output force of the device according to the monitored error, and optimizes the proportional, integral and differential coefficients by adjusting the nonlinear gain function to ensure that the force output matches the patient's needs.

[0093] 3. Stiffness closed-loop control system uses real-time feedback to adjust the stiffness of the actuator to adapt to the dynamic needs of the patient, ensuring the balance of force and stiffness during treatment and avoiding excessive stress or discomfort to the patient.

[0094] This embodiment provides personalized and precise rehabilitation treatment by precisely controlling the force and stiffness during the treatment process, improves the patient's rehabilitation effect, reduces discomfort during the recovery process, and can significantly improve treatment efficiency and effect. It is particularly suitable for rehabilitation treatment of the spine, joints, etc.

[0095] An embodiment of the present invention provides a force-stiffness closed-loop control method and system for a variable stiffness actuator based on force feedback, including a nonlinear PID control method, a stiffness closed-loop control method, and a parameter setting method and process.

[0096] The nonlinear PID control method and the stiffness closed-loop control method constitute a force-stiffness control system. The controlled system consists of a thrust generating mechanism and a variable stiffness mechanism, which output force together.

[0097] The nonlinear PID control method is obtained by constructing a nonlinear gain function and multiplying the nonlinear gain function with the linear PID gain coefficient, which is expressed as

[0098]

[0099] Where e is the error, which is expressed as the difference between the sensor measurement value and the expected value, K P Indicates the linear PID proportional coefficient, K I Indicates the linear PID integral coefficient, K D Indicates the linear PID differential coefficient, H P (e) represents the nonlinear gain function of the proportional coefficient and is related to the error, H I (e) represents the nonlinear gain function of the integral coefficient and is related to the error, H D (e) represents the nonlinear gain function of the differential coefficient and is related to the error, uc represents the output expectation of the controller, and t is the time;

[0100] The nonlinear gain function includes two forms, A and B, both of which are constructed by hyperbolic tangent function;

[0101] The A-type nonlinear gain function expression is: A (e) = 1 + h 11 (1-sech(h 12 e)), H A (e) is the value of the nonlinear gain function; h 11 and h 12 is the nonlinear parameter of the type I nonlinear gain function; e is the error, which is expressed as the difference between the sensor measurement value and the expected value;

[0102] like Figure 1 As shown, the A-type nonlinear gain function, when the feedback error e tends to 0, the nonlinear gain function tends to 1; when the feedback error e tends to infinity, the nonlinear gain function tends to 1+h 11 ;

[0103] The nonlinear gain function of type B is H B (e) is the value of the nonlinear gain function; h 21 and h 22is the nonlinear parameter of the B-type nonlinear gain function; e is the error, which is expressed as the difference between the sensor measurement value and the expected value;

[0104] like Figure 2 As shown, the B-type nonlinear gain function, when the feedback error e tends to 0, the nonlinear gain function tends to 1; when the feedback error e tends to infinity, the nonlinear gain function tends to

[0105] By combining type A and type B nonlinear gain functions, a variety of nonlinear gain functions can be obtained; the nonlinear gain functions in the order of proportion, integration, and differentiation can be expressed as nine combinations of AAA, AAB, ABA, ABB, BAA, BAB, ABB, BBA, and BBB;

[0106] A typical example of nonlinear PID combination can be expressed as:

[0107]

[0108] The stiffness closed-loop control method is a closed-loop stiffness feedback rate constructed based on force feedback, tangent function, and exponential function, and changes the system stiffness by controlling the variable stiffness module through force feedback;

[0109] The closed-loop stiffness feedback rate discrete time domain expression:

[0110]

[0111] Among them, k vs (t) represents the expected stiffness at time t, k vs (t-1) represents the expected stiffness at time t, T is the discrete period of the control system, Δe represents the forward difference of the error e at time t, α1, α2 and α3 are the adjustment parameters of the stiffness control rate, and e is the error, which is expressed as the difference between the sensor measurement value and the expected value.

[0112] like Figure 3 As shown, the closed-loop stiffness feedback rate is based on The current rate of change of the force error is evaluated; the hyperbolic tangent function replaces the sign function to reduce the chatter in the stiffness control caused by the force error noise disturbance;

[0113] If the force error change rate is positive, the stiffness is increased to reduce the damping ratio and response time, thereby enhancing the system's ability to quickly adjust the force error. If the force error change rate is negative, the stiffness is reduced to increase the damping ratio and improve system stability, thereby enhancing the system's anti-interference performance;

[0114] like Figure 3 As shown in Figure 2, the magnitude of the stiffness change depends on the exponential decay function (1-exp(α3|e f(t)|)); As the absolute error changes, larger force errors lead to larger adjustments, while smaller errors produce smoother changes.

[0115] like Figure 4 As shown, the parameter setting method and process are as follows:

[0116] S1 is recognized by the control system

[0117] Linear parameter identification of S2 nonlinear PID: K P , K I , K D

[0118] Nonlinear gain function parameter identification of S3 nonlinear PID: H P (e), H I (e), H D (e)

[0119] S4 stiffness control law parameter identification: α1, α2 and α3

[0120] Furthermore, the parameters of the controlled system in S1 can be identified by using the sweep frequency method and pseudo-random sequence method to obtain the transfer function of the system;

[0121] The linear parameter identification in S2 can be done by empirical accumulation method, Ziegler-Nichols method, and Cohen-Coon method;

[0122] like Figure 5 As shown in Figure 1, the nonlinear gain function parameter identification in S3 is implemented using the particle swarm optimization algorithm. Other algorithms such as genetic algorithms can also be used, but the algorithm optimization target is limited to the weighted variance of the output of the controlled system and the output of the nonlinear PID control as the objective function, and its expression is:

[0123] Where α and β are weights, and J is the optimization target; It is the variance of the output of the controlled system, which is used to judge the stability of the system output; The variance of the nonlinear PID control output is used to evaluate the anti-disturbance capability of the nonlinear PID control system. When the controlled system is relatively stable and the external disturbance is relatively small, a larger α value can be set. When the external disturbance is relatively large, a larger β value can be set.

[0124] like Figure 5 As shown, during the parameter tuning process of the heuristic optimization algorithm in S3, the expected value of the controlled system output is set to 0, a random disturbance is set in the output of the controlled system, and the stiffness control law is not applied; the parameter group corresponding to the minimum value of the objective function is selected as the tuning parameter result.

[0125] like Figure 6As shown in Figure 2, the stiffness control law parameter identification in S4 is implemented using a heuristic optimization algorithm, such as a particle swarm algorithm and a genetic algorithm. However, the algorithm optimization goal is limited to the weighted sum of the output of the controlled system and the variance of the control system output as the objective function, and its expression is:

[0126] Where α and β are weights, and J is the optimization target; It is the variance of the output of the controlled system, which is used to judge the stability of the system output; is the variance of the nonlinear PID control output, The stiffness control rate controls the output variance and is used to evaluate the anti-disturbance capability of the entire control system;

[0127] like Figure 6 As shown, in the process of parameter tuning of the heuristic optimization algorithm in S4, the expected value of the output of the controlled system is set to 0, and a random disturbance is set in the output of the controlled system; the parameter group corresponding to the minimum value of the objective function is selected as the tuning parameter result.

[0128] like Figure 7 As shown in the figure, the force control error of the method proposed in this paper is reduced in a strong disturbance environment. A force-stiffness closed-loop control method and system of a variable stiffness actuator based on force feedback can effectively improve the force control accuracy, and as Figure 8 As shown, the speed fluctuation of the actuator (controlled system) has dropped dramatically, which means that the control stability of the system has become better.

[0129] like Fig. 9 As shown, an embodiment of the present invention provides a variable stiffness actuator force-stiffness closed-loop control system based on force feedback based on the variable stiffness actuator force-stiffness closed-loop control method based on force feedback, the system comprising:

[0130] A nonlinear PID module is obtained by constructing a nonlinear gain function and multiplying the nonlinear gain function with a linear PID gain coefficient;

[0131] The stiffness control rate module is based on the closed-loop stiffness feedback rate constructed by force feedback, tangent function and exponential function. It controls the variable stiffness module through force feedback to change the system stiffness.

[0132] Parameter setting module, controlled system identification; linear parameter identification of nonlinear PID: K P , K I , K D ; Nonlinear gain function parameter identification of nonlinear PID: H P (e), H I (e), H D (e); Identification of stiffness control law parameters: α1, α2 and α3.

[0133] An embodiment of the present invention provides a computer device, which includes a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the variable stiffness actuator force-stiffness closed-loop control method based on force feedback.

[0134] An embodiment of the present invention provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the processor executes the steps of the variable stiffness actuator force-stiffness closed-loop control method based on force feedback.

[0135] An embodiment of the present invention provides an information data processing terminal, which is used to implement the variable stiffness actuator force-stiffness closed-loop control system based on force feedback.

[0136] The present invention has been successfully verified on a robot grinding and polishing experimental platform. The device system is shown in the figure below through experimental verification on curved thin-walled parts.

[0137] Fig.10 It is a system diagram of the method verification equipment provided by the present invention.

[0138] As shown in the figure below, the experimental result diagram provided by the present invention shows that compared with PID control, the average absolute force error, maximum absolute force error, material removal error and surface roughness of the method of the present invention are reduced by 69.78%, 64.6%, 70.8% and 65.49% respectively.

[0139] Fig.11 It is a surface accuracy map after the method provided by the present invention is applied to robot grinding and polishing.

[0140] Fig.12 This is a comparison chart of force control accuracy after the method provided by the present invention is applied to robot grinding and polishing.

[0141] It should be noted that the embodiments of the present invention can be implemented by hardware, software, or a combination of software and hardware. The hardware part can be implemented using dedicated logic; the software part can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated design hardware. It can be understood by a person of ordinary skill in the art that the above-mentioned devices and methods can be implemented using computer executable instructions and / or contained in a processor control code, such as a carrier medium such as a disk, CD or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. Such code is provided on the carrier medium. The device and its modules of the present invention can be implemented by hardware circuits such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, etc., or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., can also be implemented by software executed by various types of processors, and can also be implemented by a combination of the above-mentioned hardware circuits and software, such as firmware.

[0142] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any modifications, equivalent substitutions and improvements made by any technician familiar with the technical field within the technical scope disclosed by the present invention and within the spirit and principle of the present invention should be covered by the protection scope of the present invention.

Claims

1. A force-stiffness closed-loop control method for a variable stiffness actuator based on force feedback, characterized in that: The method includes a nonlinear PID control method, a stiffness closed-loop control method and a parameter setting method and process.

2. The variable stiffness actuator force-stiffness closed-loop control method based on force feedback according to claim 1 is characterized in that: The nonlinear PID control method is obtained by constructing a nonlinear gain function and multiplying the nonlinear gain function with a linear PID gain coefficient, which is expressed as: Where e is the error, which is expressed as the difference between the sensor measurement value and the expected value, K P Indicates the linear PID proportional coefficient, K I Indicates the linear PID integral coefficient, K D Indicates the linear PID differential coefficient, H P (e) represents the nonlinear gain function of the proportional coefficient and is related to the error, H I (e) represents the nonlinear gain function of the integral coefficient and is related to the error, H D (e) represents the nonlinear gain function of the differential coefficient and is related to the error, uc represents the desired output of the controller, and t is the time.

3. The variable stiffness actuator force-stiffness closed-loop control method based on force feedback as claimed in claim 2, characterized in that: The nonlinear gain function includes two forms, A and B, both of which are constructed by hyperbolic tangent function; The expression of the A-type nonlinear gain function is H A (e) = 1 + h 11 (1-sech(h 12 e)), H A (e) is the value of the nonlinear gain function; h 11 and h 12 is the nonlinear parameter of the type I nonlinear gain function; e is the error, expressed as the difference between the sensor measurement value and the expected value; Type A nonlinear gain function, when the feedback error e tends to 0, the nonlinear gain function tends to 1; When the feedback error e tends to infinity, the nonlinear gain function tends to 1+h 11 ; The nonlinear gain function of type B is H B (e) is the value of the nonlinear gain function; h 21 and h 22 is the nonlinear parameter of the B-type nonlinear gain function; e is the error, expressed as the difference between the sensor measurement value and the expected value; Type B nonlinear gain function, when the feedback error e tends to 0, the nonlinear gain function tends to 1; When the feedback error e tends to infinity, the nonlinear gain function tends to By combining type A and type B nonlinear gain functions, a variety of nonlinear gain functions can be obtained; the nonlinear gain functions in the order of proportion, integration, and differentiation can be expressed as nine combinations of AAA, AAB, ABA, ABB, BAA, BAB, ABB, BBA, and BBB.

4. The variable stiffness actuator force-stiffness closed-loop control method based on force feedback as claimed in claim 1, characterized in that: The stiffness closed-loop control method is a closed-loop stiffness feedback rate constructed based on force feedback, tangent function, and exponential function, and changes the system stiffness by controlling the variable stiffness module through force feedback; The closed-loop stiffness feedback rate discrete time domain expression: Among them, k vs (t) represents the expected stiffness at time t, k vs (t-1) represents the desired stiffness at time t, T is the discrete period of the control system, Δe represents the forward difference of the error e at time t, α1, α2 and α3 are the adjustment parameters of the stiffness control rate, and e is the error, which is expressed as the difference between the sensor measurement value and the expected value; The closed-loop stiffness feedback rate is based on Evaluate the current rate of change of the force error; the hyperbolic tangent function replaces the sign function to reduce the chatter in the stiffness control caused by the force error noise interference; if the force error change rate is positive, the stiffness is increased to reduce the damping ratio and response time, thereby enhancing the system's ability to quickly adjust the force error. If the force error change rate is negative, the stiffness is reduced to increase the damping ratio and improve system stability, thereby enhancing the system's anti-interference performance; The magnitude of the stiffness change depends on the exponential decay function (1-exp(α3|e f (t)|)); As the absolute error changes, larger force errors lead to larger adjustments, while smaller errors produce smoother changes.

5. The variable stiffness actuator force-stiffness closed-loop control method based on force feedback as claimed in claim 1, characterized in that: The parameter setting method and process are as follows: S1: Identified by the control system S2: Linear parameter identification of nonlinear PID: K P , K I , K D S3: Nonlinear gain function parameter identification of nonlinear PID: H P (e), H I (e), H D (e) S4: Identification of stiffness control law parameters: α1, α2 and α3 The parameters of the controlled system in S1 can be identified by using the sweep frequency method or the pseudo-random sequence method to obtain the transfer function of the system; The linear parameter identification in S2 can be carried out by the empirical accumulation method, the Ziegler-Nichols method, and the Cohen-Coon method; The nonlinear gain function parameter identification in S3 is implemented by a heuristic optimization algorithm, such as a particle swarm algorithm or a genetic algorithm, but the algorithm optimization target is limited to the weighted sum of the output of the controlled system and the variance of the nonlinear PID control output as the objective function, and its expression is: Where α and β are weights, and J is the optimization target; It is the variance of the output of the controlled system, which is used to judge the stability of the system output; The variance of the nonlinear PID control output is used to evaluate the anti-disturbance capability of the nonlinear PID control system. When the controlled system is relatively stable and the external disturbance is relatively small, a larger α value can be set. When the external disturbance is relatively large, a larger β value can be set.

6. The variable stiffness actuator force-stiffness closed-loop control method based on force feedback as claimed in claim 5, characterized in that: In the process of parameter setting of the heuristic optimization algorithm in S3, the expected value of the output of the controlled system is set to 0, a random disturbance is set in the output of the controlled system, and the stiffness control law is not applied; The stiffness control law parameter identification in S4 is implemented by a heuristic optimization algorithm, such as a particle swarm algorithm or a genetic algorithm, but the algorithm optimization target is limited to the weighted sum of the output of the controlled system and the variance of the output of the control system as the objective function, and its expression is: Where α and β are weights, and J is the optimization target; It is the variance of the output of the controlled system, which is used to judge the stability of the system output; is the variance of the nonlinear PID control output, The stiffness control rate controls the output variance and is used to evaluate the anti-disturbance capability of the entire control system; During the parameter setting process of the heuristic optimization algorithm in S4, the expected value of the output of the controlled system is set to 0, and a random disturbance is set in the output of the controlled system.

7. A variable stiffness actuator force-stiffness closed-loop control system based on force feedback based on the variable stiffness actuator force-stiffness closed-loop control method based on force feedback as claimed in claims 1-6, characterized in that: The system includes: A nonlinear PID module is obtained by constructing a nonlinear gain function and multiplying the nonlinear gain function with a linear PID gain coefficient; The stiffness control rate module is based on the closed-loop stiffness feedback rate constructed by force feedback, tangent function and exponential function. It controls the variable stiffness module through force feedback to change the system stiffness. Parameter setting module, controlled system identification; linear parameter identification of nonlinear PID: K P , K I , K D ; Nonlinear gain function parameter identification of nonlinear PID: H P (e), H I (e), H D (e); Identification of stiffness control law parameters: α1, α2 and α3.

8. A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the force-stiffness closed-loop control method of a variable stiffness actuator based on force feedback as described in any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor executes the steps of the force-stiffness closed-loop control method of a variable stiffness actuator based on force feedback as claimed in any one of claims 1 to 6.

10. An information data processing terminal, characterized in that: The information data processing terminal is used to implement the variable stiffness actuator force-stiffness closed-loop control system based on force feedback as described in claim 7.

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