Parallel robot control method and device, storage medium and parallel robot

Abstract

The invention provides a control method and device of a parallel robot, a storage medium and the parallel robot, and the control method of the parallel robot comprises the steps: constructing a novel sliding mode function which is a function for describing a global nonsingular fast terminal sliding mode surface; acquiring an equivalent control law corresponding to the novel sliding mode function; obtaining an approaching control law corresponding to the novel sliding mode function; and controlling the parallel robot based on the equivalent control law and the approaching control law. In the process, the global non-singular fast terminal sliding mode surface is adopted, so that the global convergence speed of terminal sliding mode control can be improved, and the problems of singularity of the terminal sliding mode surface and low global convergence speed of the terminal sliding mode surface are solved. Besides, by utilizing the designed equivalent control law and approaching control law, the problem of uncertain disturbance caused by perturbation of kinetic model parameters can be improved, and meanwhile, when an external load changes, good control performance can be ensured.

Description

Technical Field

[0001] The present invention relates to the field of control technology, and in particular to a control method, device, storage medium and parallel robot of a parallel robot. Background Art

[0002] The parallel robot, taking the Delta robot as an example, has a complex dynamic model, which has the characteristics of multi-input and multi-output, nonlinearity, and strong coupling.

[0003] In actual industrial production environments, Delta parallel robots are affected by unknown disturbances, internal friction, modeling errors and other uncertain factors, which makes it difficult to establish an accurate robot dynamics model and increases the difficulty of industrial robot control. Therefore, in order to ensure that the Delta robot has high-precision, strong robustness and fast convergence time trajectory tracking effect in complex working conditions, designing a controller with certain robustness has important theoretical and practical significance for the Delta parallel robot.

[0004] In view of the complex dynamic model and unpredictable industrial environment of the Delta parallel robot, in order to achieve better control performance of the Delta parallel robot, scholars have focused more on the research of dynamic control of industrial robots.

[0005] Among many dynamic control methods, the sliding mode control algorithm has little impact on model parameter perturbations, environmental influences and external disturbances, and makes the system highly robust.

[0006] In the related technical solutions, in order to solve the problem of singular points in terminal sliding mode control, a non-singular terminal sliding mode control is proposed. However, the above solution has the problems of slow convergence speed and jitter in the controller output. Summary of the invention

[0007] The present invention aims to at least solve the problem of singular points in terminal sliding mode control in the prior art or related art, and proposes a global non-singular fast terminal sliding mode control. However, the above scheme has the problems of slow convergence speed and jitter in the controller output.

[0008] To this end, a first aspect of the present invention is to provide a control method for a parallel robot.

[0009] A second aspect of the present invention is to provide a control device for a parallel robot.

[0010] A third aspect of the present invention provides a readable storage medium.

[0011] A fourth aspect of the present invention is to provide a parallel robot.

[0012] In view of this, according to the first aspect of the present invention, the present invention provides a control method for a parallel robot, including: constructing a new sliding mode function, which is a function that describes a global non-singular fast terminal sliding surface; obtaining an equivalent control law corresponding to the new sliding mode function; obtaining a convergence control law corresponding to the new sliding mode function; and controlling the parallel robot based on the equivalent control law and the convergence control law.

[0013] The present invention proposes a control method for a parallel robot. By running the control method for the parallel robot, a function describing a global non-singular fast terminal sliding mode surface, that is, a new sliding mode function, can be designed. After designing the function describing the global non-singular fast terminal sliding mode surface, an equivalent control law corresponding to the new sliding mode function and a convergence control law corresponding to the new sliding mode function are designed, and then the operation of the parallel robot is controlled according to the equivalent control law and the convergence control law.

[0014] In this process, the use of a global non-singular fast terminal sliding surface can improve the global convergence speed of the terminal sliding mode control and overcome the singularity of the terminal sliding surface and the slow global convergence speed of the terminal sliding surface.

[0015] In addition, the designed equivalent control law and approaching control law can improve the problem of uncertain disturbance caused by perturbations of dynamic model parameters. At the same time, good control performance can be ensured when external loads change.

[0016] In some technical solutions, optionally, the equivalent control law can be understood as the derivative of the new sliding mode function with respect to time without considering external interference When the parameters change, we get When , the equivalent control quantity output by the new sliding mode function.

[0017] In some technical solutions, optionally, the approaching control law represents a control law used to eliminate external disturbances when the dynamic model parameters are perturbed to generate uncertain disturbances or external loads occur.

[0018] In addition, the control method of the parallel robot proposed in the present invention also has the following additional technical features.

[0019] In some technical schemes, optionally, an approach control law corresponding to a new sliding mode function is obtained, specifically including: inputting the new sliding mode function and the derivative of the new sliding mode function with respect to time into a new sliding mode controller to obtain output variables output by the new sliding mode controller, wherein the new sliding mode controller is a controller constructed based on non-singular fast terminal sliding mode control and fuzzy theory; obtaining a gain coefficient; and obtaining an approach control law based on the output variables and gain coefficients output by the new sliding mode controller.

[0020] In the above technical solution, a serious drawback of sliding mode control is the problem of controller output jitter. To avoid this problem, fuzzy control is often combined with sliding mode control to reduce output jitter.

[0021] In the technical solution of the present invention, the approach control law is replaced by fuzzy output Eliminate controller output jitter. Non-singular fast terminal sliding mode control combined with fuzzy theory can not only ensure the stability of the system, but also significantly eliminate controller output jitter compared to traditional non-singular fast terminal sliding mode control.

[0022] In the above technical solution, the new sliding mode controller can be understood as a controller of fuzzy global fast non-singular terminal sliding mode control.

[0023] In the above technical solution, the gain coefficient is set, which can not only adjust the tracking accuracy of the control system, but also improve the speed of asymptotic stabilization of the system variables and shorten the convergence time.

[0024] In some technical solutions, optionally, ;in, The output variable representing the output of the new sliding mode controller is a function of relative time. represents the decision-making mechanism in the fuzzy system, represents a new sliding mode function, represents the derivative of the new sliding mode function with respect to time, and the decision-making mechanism in the fuzzy system is:

[0025]

[0026] in, , Fuzzy Sets , The fuzzy subsets in Fuzzy Set The fuzzy subsets in , The input , , m is the number of fuzzy inference rules, Indicates filter The symbol used when

[0027] In some technical solutions, optionally, obtaining the gain coefficient specifically includes: obtaining a system error function and an error change rate function, wherein the error change rate function is the derivative of the system error function with respect to time; inputting the system error function and the error change rate function into an adaptive fuzzy controller to obtain a gain coefficient output by the adaptive fuzzy controller; wherein the adaptive fuzzy controller is a relationship function that takes the system error function and the error change rate function as input and the gain coefficient as output.

[0028] In this technical solution, the gain coefficient is not always constant, but can be changed according to the system error function and the error change rate function. In this process, the control accuracy and response speed of the parallel robot can be improved.

[0029] In some technical solutions, optionally, ;in, yes , Fuzzy Sets , The fuzzy subset of Fuzzy Set fuzzy subset of . , The input , The fuzzy set of Output The fuzzy set of is the system error function, is the error change rate function, Represents adaptive The value after.

[0030] In some technical solutions, optionally, ;in, represents the gain factor, represents the output variable of the new sliding mode controller, represents the reaching control law.

[0031] In some technical schemes, optionally, the control method of the parallel robot also includes: initializing the selected parameters of the new sliding mode function; determining the optimization parameters of the sparrow optimization algorithm based on the selected parameters; initializing the number of discoverers, followers and alerters in the sparrow population; obtaining the performance indicators of the evaluation function output when the selected parameters are used as input; updating the positions of the discoverers, followers and alerters, and randomly performing early warning behaviors; and outputting the optimal selected parameters of the new sliding mode function when it is detected that the iteration stop conditions are met.

[0032] In this technical solution, the sparrow optimization algorithm can be used to find the optimal sliding surface parameters, thereby improving the system accuracy and robustness of the parallel robot and achieving effective trajectory tracking. Compared with the traditional sliding mode controller, it eliminates the controller output jitter and has higher tracking accuracy and faster response speed.

[0033] According to a second aspect of the present invention, the present invention provides a control device for a parallel robot, comprising: a construction unit for constructing a new sliding mode function, which is a function that describes a global non-singular fast terminal sliding surface; a first acquisition unit for acquiring an equivalent control law corresponding to the new sliding mode function; a second acquisition unit for acquiring a convergence control law corresponding to the new sliding mode function; and a control unit for controlling the parallel robot based on the equivalent control law and the convergence control law.

[0034] The present invention proposes a control device for a parallel robot, which can design a function that describes a global non-singular fast terminal sliding surface, that is, a new sliding mode function. After designing the function that describes the global non-singular fast terminal sliding surface, an equivalent control law corresponding to the new sliding mode function and a reaching control law corresponding to the new sliding mode function are designed, and then the operation of the parallel robot is controlled according to the equivalent control law and the reaching control law.

[0035] In this process, the use of a global non-singular fast terminal sliding surface can improve the global convergence speed of the terminal sliding mode control and overcome the singularity of the terminal sliding surface and the slow global convergence speed of the terminal sliding surface.

[0036] In addition, the designed equivalent control law and approaching control law can improve the problem of uncertain disturbance caused by perturbations of dynamic model parameters. At the same time, good control performance can be ensured when external loads change.

[0037] In some technical solutions, optionally, the equivalent control law can be understood as the derivative of the new sliding mode function with respect to time without considering external interference When the parameters change, we get When , the equivalent control quantity output by the new sliding mode function.

[0038] In some technical solutions, optionally, the approaching control law represents a control law used to eliminate external disturbances when the dynamic model parameters are perturbed to generate uncertain disturbances or external loads occur.

[0039] In addition, the control device of the parallel robot proposed in the present invention also has the following additional technical features.

[0040] In some technical schemes, optionally, the second acquisition unit is specifically used to: input the new sliding mode function and the derivative of the new sliding mode function with respect to time into the new sliding mode controller to obtain the output variables output by the new sliding mode controller, wherein the new sliding mode controller is a controller constructed based on non-singular fast terminal sliding mode control and fuzzy theory; obtain the gain coefficient; and obtain the approaching control law based on the output variables and gain coefficient output by the new sliding mode controller.

[0041] In the above technical solution, a serious drawback of sliding mode control is the problem of controller output jitter. To avoid this problem, fuzzy control is often combined with sliding mode control to reduce output jitter.

[0042] In the technical solution of the present invention, the approach control law is replaced by fuzzy output Eliminate controller output jitter. Non-singular fast terminal sliding mode control combined with fuzzy theory can not only ensure the stability of the system, but also significantly eliminate controller output jitter compared to traditional non-singular fast terminal sliding mode control.

[0043] In the above technical solution, the new sliding mode controller can be understood as a controller of fuzzy global fast non-singular terminal sliding mode control.

[0044] In the above technical solution, the gain coefficient is set, which can not only adjust the tracking accuracy of the control system, but also improve the speed of asymptotic stabilization of the system variables and shorten the convergence time.

[0045] In some technical solutions, optionally, ;in, The output variable representing the output of the new sliding mode controller is a function of relative time. represents the decision-making mechanism in the fuzzy system, represents a new sliding mode function, represents the derivative of the new sliding mode function with respect to time, and the decision-making mechanism in the fuzzy system is:

[0046]

[0047] in, , Fuzzy Sets , The fuzzy subsets in Fuzzy Set The fuzzy subsets in , The input , , m is the number of fuzzy inference rules, Indicates filter The symbol used when

[0048] In some technical schemes, optionally, the second acquisition unit is specifically used to: obtain the system error function and the error change rate function, the error change rate function is the derivative of the system error function with respect to time; input the system error function and the error change rate function into an adaptive fuzzy controller to obtain a gain coefficient output by the adaptive fuzzy controller; wherein the adaptive fuzzy controller is a relationship function that uses the system error function and the error change rate function as input and the gain coefficient as output.

[0049] In this technical solution, the gain coefficient is not always constant, but can be changed according to the system error function and the error change rate function. In this process, the control accuracy and response speed of the parallel robot can be improved.

[0050] In some technical solutions, optionally, ;in, yes , Fuzzy Sets , The fuzzy subset of Fuzzy Set fuzzy subset of . , The input , The fuzzy set of Output The fuzzy set of is the system error function, is the error change rate function, Represents adaptive The value after.

[0051] In some technical solutions, optionally, ;in, represents the gain factor, represents the output variable of the new sliding mode controller, represents the reaching control law.

[0052] In some technical schemes, optionally, the control unit is also used to: initialize the selection parameters of the new sliding mode function; determine the optimization parameters of the sparrow optimization algorithm based on the selected parameters; initialize the number of discoverers, followers and alerters in the sparrow population; obtain the performance indicators of the evaluation function output when the selected parameters are used as input; update the positions of the discoverers, followers and alerters, and randomly perform early warning behaviors; and output the optimal selection parameters of the new sliding mode function when it is detected that the iteration stop conditions are met.

[0053] In this technical solution, the sparrow optimization algorithm can be used to find the optimal sliding surface parameters, thereby improving the system accuracy and robustness of the parallel robot and achieving effective trajectory tracking. Compared with the traditional sliding mode controller, it eliminates the controller output jitter and has higher tracking accuracy and faster response speed.

[0054] According to a third aspect of the present invention, the present invention provides a readable storage medium, on which a program or instruction is stored, and when the program or instruction is executed by a processor, a control method of a parallel robot as described above is implemented.

[0055] According to a fourth aspect of the present invention, the present invention provides a parallel robot, comprising a processor and a memory, wherein the memory stores programs or instructions that can be executed on the processor, and when the programs or instructions are executed by the processor, the steps of the control method of the parallel robot as described above are implemented.

[0056] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0059] Figure 1 The schematic diagram of the traditional Delta parallel robot is shown;

[0060] Figure 2 A schematic flow chart of a control method for a parallel robot in an embodiment of the present invention is shown;

[0061] Figure 3 A schematic block diagram of the GFNTSMC control process in an embodiment of the present invention is shown;

[0062] Figure 4 The FGFNTSMC system structure diagram in the embodiment of the present invention is shown;

[0063] Figure 5 shows the FGFNTSMC controller input variables , output variables Schematic diagram of membership function and fuzzy relationship surface;

[0064] Figure 6 Shown is the AFGFNTSMC controller system structure diagram;

[0065] Figure 7 shows the AFGFNTSMC controller input variables , output variables Schematic diagram of membership function and fuzzy relationship surface;

[0066] Figure 8 The SSA-AFGFNTSMC optimization flow chart is shown;

[0067] Fig. 9 The control structure diagram of applying the SSA-AFGFNSMC controller to the Delta robot model is shown;

[0068] Fig.10It is the iteration curve of the sparrow algorithm.

[0069] Fig.11 A schematic diagram showing the trajectory tracking curve and driving torque of the driving joint 1 under different controllers;

[0070] Fig.12 A schematic diagram showing the trajectory tracking curve and driving torque of the driving joint 2 under different controllers;

[0071] Fig.13 A schematic diagram showing the trajectory tracking curve and driving torque of the driving joint 3 under different controllers;

[0072] Fig.14 A schematic diagram showing the trajectory of the Delta robot end effector;

[0073] Fig.15 shows a schematic diagram of the Delta robot experimental platform;

[0074] Fig.16 A schematic diagram showing the position data of the end effector of the Delta robot;

[0075] Fig.17 Shows Fig.16 A partial enlarged view of the

[0076] Fig.18 A schematic diagram showing the driving torque of each joint under different control strategies is shown;

[0077] Fig.19 A schematic block diagram of a control device for a parallel robot according to an embodiment of the present invention is shown;

[0078] Fig. 20 A schematic block diagram of a parallel robot in an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0080] In order to more clearly understand the above aspects, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0081] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0082] The control of the parallel robot mentioned in this application is explained below.

[0083] About kinematic analysis:

[0084] The structure of a high-speed parallel robot is generally a closed-loop kinematic chain structure. Figure 1 The structure diagram of the traditional Delta parallel robot is shown in Figure 1. Figure 1 As shown, and They represent the parallel robot The length of the active rod and the driven rod; and They are respectively represented as the radii of the static platform and the dynamic platform of the parallel robot; Indicates The angle between the active arm and the static platform is the rotation angle; Indicates Active rod and Coordinate system The active arm and the driven arm, and the driven arm and the end effector are all connected by ball joints. is the origin of the moving platform coordinate system, and is defined as the position of the end effector. For a single branch chain, the end effector position can be expressed as:

[0085]

[0086] General Represented by a vector and simplified:

[0087]

[0088] Simplified :

[0089]

[0090] in is the end effector moving speed, is the joint space angular velocity, The Jacobian matrix can be expressed as:

[0091]

[0092] Mode middle , ,in is the end effector position, is the rotation angle of the active arm.

[0093] About kinetic analysis:

[0094] For the Delta parallel robot, its dynamic characteristics can be reflected by the robot dynamics equation established by the virtual work principle. The dynamics equation can be expressed as:

[0095]

[0096] It is expressed as the robot's joint angular displacement, which is the driving joint angle in this paper.

[0097] is the robot inertia matrix

[0098] Represents centrifugal force and Coriolis force

[0099] Represents the gravity term

[0100] Represents friction

[0101] represents the interference term, and its upper bound is

[0102] Indicates the input torque signal

[0103] To simplify the torque contribution of the slave arm, one-third of the mass of each slave arm is added to the moving platform, and the remaining two-thirds of the mass is added to the end of the master arm. As for the influence of the gravity of the slave arm, one-half of the mass of each slave arm is distributed to the upper end of the slave arm, and the remaining half of the mass is distributed to the lower end of the slave arm.

[20] The inertia matrix, centrifugal force and gravity terms are expressed as:

[0104]

[0105] in , , It is expressed as:

[0106]

[0107] in is the mass of the driven arm, is the mass of the active arm, is the mass of the end effector, is the mass of the ball joint, is the inertia of the driving motor. To transform:

[0108]

[0109] in Represents the external load disturbance, nonlinear friction and unmodeled dynamics of the robot. :

[0110]

[0111] in, , , is the control input. The robot system parameters will change during the motion process. , , , On the system it means:

[0112]

[0113] where the parameter perturbations are all bounded; for example , and . The subscript and Indicates the lower and upper bounds of a parameter.

[0114] In one embodiment of the present application, Figure 2 As shown, a control method for a parallel robot is provided, comprising:

[0115] Step 201, constructing a new sliding mode function, where the new sliding mode function is a function that describes a global non-singular fast terminal sliding mode surface;

[0116] Step 202, obtaining an equivalent control law corresponding to the new sliding mode function;

[0117] Step 203, obtaining a reaching control law corresponding to the new sliding mode function;

[0118] Step 204: Control the parallel robot based on the equivalent control law and the approach control law.

[0119] The present invention proposes a control method for a parallel robot. By running the control method for the parallel robot, a function describing a global non-singular fast terminal sliding mode surface, that is, a new sliding mode function, can be designed. After designing the function describing the global non-singular fast terminal sliding mode surface, an equivalent control law corresponding to the new sliding mode function and a convergence control law corresponding to the new sliding mode function are designed, and then the operation of the parallel robot is controlled according to the equivalent control law and the convergence control law.

[0120] In this process, sliding mode control is to drive the state of the system onto the sliding surface and ensure that the system state point slides on the sliding surface, thereby ensuring that the system reaches a stable state. In the embodiment of the present application, a global non-singular fast terminal sliding surface is used to improve the global convergence speed of the terminal sliding mode control and overcome the singularity of the terminal sliding surface and the slow global convergence speed of the terminal sliding surface.

[0121] In addition, the designed equivalent control law and approaching control law can improve the problem of uncertain disturbance caused by perturbations of dynamic model parameters. At the same time, good control performance can be ensured when external loads change.

[0122] In some embodiments, optionally, the equivalent control law can be understood as the derivative of the new sliding mode function with respect to time without considering external interference When the parameters change, we get When , the equivalent control quantity output by the new sliding mode function.

[0123] In some embodiments, optionally, the approaching control law represents a control law for eliminating external disturbances when the dynamic model parameters are perturbed to generate uncertain disturbances or external loads occur.

[0124] Among them, the global non-singular fast terminal sliding surface, that is, the new sliding function is:

[0125]

[0126] in, , , and . is a symbolic function.

[0127] The absolute value part of the sliding surface is introduced to solve the When , the sliding surface and its derivative expressions have non-real number problems. When it is far from the equilibrium point, the nonlinear term can be ignored , then the sliding surface is approximately , to achieve rapid convergence to the equilibrium point; when the error When approaching the equilibrium point, the linear term can be ignored , then the sliding surface is approximately , to reach the equilibrium point within a fixed time.

[0128] The goal of the sliding mode control law is to force the system state to approach the sliding surface and slide to the equilibrium point on the sliding surface after reaching the sliding surface. Therefore, the sliding surface must be stable, that is, ; As time goes by, the error will gradually approach 0. Sliding surface Taking the derivative with respect to time t and substituting it into the equation we get:

[0129]

[0130] Without considering external interference When the parameters change, we get The output control quantity is called the equivalent control law, which is written as :

[0131]

[0132] When the dynamic model parameters are perturbed to produce uncertain disturbances or external loads occur, equivalent control cannot ensure good control performance. Therefore, an additional control rate needs to be designed to eliminate external disturbances. This control rate is called the approaching control law and is given by express.

[0133] In some embodiments, through Lyapunov function design, the Lyapunov function can be selected as:

[0134]

[0135] GFNTSMC control process Figure 3 As shown, the equivalent control law Combined with the reaching control law The stability of sliding mode control can be guaranteed. To obtain the approaching control law , the control law Bring-in The derivative of is:

[0136]

[0137] Simplified have to:

[0138]

[0139] To ensure , approaching control law Should be:

[0140]

[0141] Mode Selected satisfy , so the approach control law is selected The system can be brought to a stable state. Generally speaking, the approaching sliding mode control law Can be designed as:

[0142]

[0143] Mode middle The coefficient of the approaching control law, whose value mainly depends on the upper limit of the external interference; represents the sign function. However, in practical control applications, the upper bound of the external disturbance of the system is difficult to be accurately determined, so It is more complicated. The value is selected to be larger, because the sign function This will cause the approaching control law to produce large jitters, which will increase the energy consumption of the control and damage the system hardware. A small value may lead to system instability. In this study, fuzzy control is applied to make the control rate Approximating external disturbances and parameter perturbations makes the controller output continuous.

[0144] In some embodiments, optionally, obtaining a convergence control law corresponding to the new sliding mode function specifically includes: inputting the new sliding mode function and the derivative of the new sliding mode function with respect to time into a new sliding mode controller to obtain output variables output by the new sliding mode controller, wherein the new sliding mode controller is a controller constructed based on non-singular fast terminal sliding mode control and fuzzy theory; obtaining a gain coefficient; and obtaining a convergence control law based on the output variables and gain coefficients output by the new sliding mode controller.

[0145] A serious drawback in the above embodiments is the controller output jitter problem. To avoid this problem, fuzzy control is often combined with sliding mode control to reduce output jitter.

[0146] Among them, fuzzy control has replaced traditional technology in many fields. The most prominent advantage of fuzzy control is that it can express the degree of fuzziness in human thinking. When the mathematical model of the system is difficult to establish or the mathematical model is incomplete, using fuzzy control algorithm is one of the better ways.

[0147] In the embodiment of the present invention, the approach control law is replaced by fuzzy output. Eliminate controller output jitter. Non-singular fast terminal sliding mode control combined with fuzzy theory can not only ensure the stability of the system, but also significantly eliminate controller output jitter compared to traditional non-singular fast terminal sliding mode control.

[0148] In the above embodiment, the novel sliding mode controller can be understood as a controller of fuzzy global fast non-singular terminal sliding mode control.

[0149] In the above embodiment, the gain coefficient is set, which can not only adjust the tracking accuracy of the control system, but also improve the speed of asymptotic stabilization of the system variables and shorten the convergence time.

[0150] In some embodiments, optionally, ;in, represents the gain factor, represents the output variable of the new sliding mode controller, represents the reaching control law.

[0151] In some embodiments, optionally, ;in, The output variable representing the output of the new sliding mode controller is a function of relative time. represents the decision-making mechanism in the fuzzy system, represents a new sliding mode function, represents the derivative of the new sliding mode function with respect to time, and the decision-making mechanism in the fuzzy system is:

[0152]

[0153] in, , Fuzzy Sets , The fuzzy subsets in Fuzzy Set The fuzzy subsets in , The input , , m is the number of fuzzy inference rules, Indicates filter The symbol used when

[0154] Figure 4 The FGFNTSMC system structure diagram is shown in Figure 4 As shown, it includes the equivalent control output With fuzzy control output The fuzzy control part includes two input quantities and one output quantity. The input quantity is a sliding mode function. and the derivative of the sliding surface function , the output is approach control Regularization The fuzzy control output approaching control law can be expressed as:

[0155]

[0156] in, is the regularization coefficient of the output variable, is the output of the fuzzy controller, which mainly depends on and , the fuzzy controller is specifically represented by the input variable and To output variable The mapping between express:

[0157]

[0158] in Represents the decision-making mechanism in a fuzzy system. Figure 5 shows the FGFNTSMC controller input variables , output variables The schematic diagram of the membership function and the fuzzy relationship surface is as follows: Figure 5 As shown, and The membership function consists of seven parts, namely NB (negative large), NM (negative medium), NS (negative small), ZO (zero), PS (positive small), PM (positive medium), and PB (positive large). The design of fuzzy rules is mainly to meet the stability of the system, which mainly includes the control strategy defined by the rules between input and output. The heuristic statement in the fuzzy rule is defined as follows:

[0159]

[0160] in, , Fuzzy Sets , The fuzzy subset of Fuzzy Set fuzzy subset of . , The input , The fuzzy set of Output fuzzy set. Indicates the number of fuzzy inference rules. The central average defuzzification is used in the fuzzy inference process, and the FGFNTSMC fuzzy rules are shown in Table 1.

[0161] Table 1

[0162]

[0163] The FGFNTSMC control law can be expressed as:

[0164]

[0165] To prove the stability of FGFNTSMC, the Lyapunov function is selected as:

[0166]

[0167] By It can be seen that:

[0168]

[0169] in , will be Bring-in The derivative of is:

[0170]

[0171] General middle Bring in have to:

[0172]

[0173] Simplified have to:

[0174]

[0175] The regularized output range of the fuzzy system is ,but The following inequality is satisfied.

[0176]

[0177] General Bring in have to:

[0178]

[0179] Select , you can get hour, , that is, the stability of the system is proved.

[0180] In some embodiments, optionally, obtaining a gain coefficient specifically includes: obtaining a system error function and an error change rate function, wherein the error change rate function is the derivative of the system error function with respect to time; inputting the system error function and the error change rate function into an adaptive fuzzy controller to obtain a gain coefficient output by the adaptive fuzzy controller; wherein the adaptive fuzzy controller is a relationship function that uses the system error function and the error change rate function as inputs and the gain coefficient as output.

[0181] In this embodiment, the gain coefficient is not always constant, but can be changed according to the system error function and the error change rate function. In this process, the control accuracy and response speed of the parallel robot can be improved.

[0182] In some embodiments, optionally, ;in, yes , Fuzzy Sets , The fuzzy subset of Fuzzy Set fuzzy subset of . , The input , The fuzzy set of Output The fuzzy set of is the system error function, is the error change rate function, Represents adaptive The value after.

[0183] Specifically, the embodiment of the present invention can make the output gain based on FGFNSMC Adaptive adjustment. Figure 6 The AFGFNTSMC controller system structure diagram is shown, which adjusts the gain in real time. Improve the accuracy and response speed of FGFNTSMC controller. Figure 7 shows the AFGFNTSMC controller input variables , output variables Schematic diagram of membership function and fuzzy relationship surface, where The membership function is a triangular membership function, which consists of seven parts: NB (negative large), NM (negative medium), NS (negative small), ZO (zero), PS (positive small), PM (positive medium), and PB (positive large); The membership function is a triangular membership function and an S-shaped membership function, which consists of seven parts: VVS (extremely small), VS (medium and small), S (small), M (medium), B (large), VB (medium and large), and VVB (extremely large).

[0184] The adaptive fuzzy reasoning in AFGFNTSMC is expressed as:

[0185]

[0186] in , Fuzzy Sets , The fuzzy subset of Fuzzy Set fuzzy subset of . , The input , The fuzzy set of Output fuzzy set. represents the number of adaptive fuzzy inference rules. Center average defuzzification is used in the fuzzy inference process. The AFGFNTSMC fuzzy rules are shown in Table 2.

[0187] Table 2

[0188]

[0189] Among them, the fuzzy adaptive system output The value range is mainly determined by the upper limit of the robot's external disturbance Determined, if If the value is too large, the advantage is that it will accelerate the speed of the system state point approaching the sliding surface to a certain extent, increasing the robustness of the system. The disadvantage is that it will cause the initial output torque of the controller to be too large, increasing the energy consumption of the controller. Through multiple simulation experiments, the system performance and controller energy consumption are comprehensively compared and the value range is selected. Range is .

[0190] In some embodiments, optionally, the control method of the parallel robot also includes: initializing the selected parameters of the new sliding mode function; determining the optimization parameters of the sparrow optimization algorithm based on the selected parameters; initializing the number of discoverers, followers and alerters in the sparrow population; obtaining the performance indicators of the evaluation function output when the selected parameters are used as input; updating the positions of the discoverers, followers and alerters, and randomly performing early warning behaviors; and outputting the optimal selected parameters of the new sliding mode function when it is detected that the iteration stop conditions are met.

[0191] In this embodiment, the sparrow optimization algorithm can be used to find the optimal sliding surface parameters, thereby improving the system accuracy and robustness of the parallel robot and achieving effective trajectory tracking. Compared with the traditional sliding mode controller, it eliminates the controller output jitter and has higher tracking accuracy and faster response speed.

[0192] Specifically, the sparrow algorithm is a swarm intelligence optimization algorithm with the advantages of high convergence accuracy, fast convergence speed, and strong robustness. In the sparrow algorithm, sparrow individuals are divided into discoverers, followers, and alerters. The ones with higher energy in the population are called discoverers, the rest are followers, and the alerters account for 10% of all sparrows. In this section, the sparrow algorithm is used to find the optimal parameters of the sliding surface in AFGFNTSMC. Table 3 of this paper defines the following parameters:

[0193] Table 3

[0194]

[0195] Among them, the finder position in the sparrow algorithm Update formula:

[0196]

[0197] When the warning value When the warning value is When it is on, it means there are predators in the area and you need to fly to another safe area.

[0198] Follower Location renew:

[0199]

[0200] when , it means the follower has a high energy value and will continue to forage in this area. Otherwise, it will randomly fly to other areas to forage.

[0201] Updated awareness of dangerous sparrow locations :

[0202]

[0203] when , if the early warning person is not in the optimal position, he will escape to the vicinity of the optimal position; otherwise, if the early warning person is in the current optimal position, he will escape to a position near himself.

[0204] in, Figure 8 The SSA-AFGFNTSMC optimization flow chart is shown as Figure 8 As shown in the figure, the control structure diagram of applying SSA-AFGFNSMC controller to the Delta robot model is as follows Fig. 9 In order to make the global fast terminal sliding mode show better dynamic characteristics, this paper selects ITAE as the evaluation function to obtain the optimal parameters of the sliding surface The ITAE evaluation function can be expressed as:

[0205]

[0206] In the above embodiment, the above control methods are applied to the Delta parallel robot dynamics model to conduct simulation experiments and verify the performance of each controller. The parallel robot has three degrees of freedom ( ), which is expressed as the spatial position of the mass center of the end effector relative to the origin of the static platform coordinate system. In the present invention, the specific structure and dynamic parameters of the Delta parallel robot are shown in Table 4.

[0207] Table 4

[0208]

[0209] Among them, the global fast terminal sliding surface selection parameters are: , , , the fuzzy controller output proportional coefficient The optimization parameters in the sparrow optimization algorithm include . Initialize the number of sparrows to 20, the maximum number of iterations , alertness factor ,like Fig.10 As shown, Fig.10 It is the iteration curve of the sparrow algorithm.

[0210] The expected motion trajectory of the Delta robot end effector is:

[0211]

[0212] The values ​​of the sliding mode controller parameters mentioned above are shown in Table 5. Fig.11 , 12 1 and 13 are schematic diagrams of the trajectory tracking curves and driving torque of the driven joints 1, 2, and 3 under different controllers. Fig.11 , Fig.12 and Fig.13 and Fig.14 As shown in Table 5, the comprehensive evaluation performance of different controllers is compared, and the time mean square error (TISE) is used to evaluate the quality of the controller. TISE is expressed as:

[0213]

[0214] Table 5

[0215]

[0216] In order to further verify the control effect of SSA-AFGFNTSMC controller, the Fig.15 The Delta robot experimental platform shown in the figure is composed of a host computer, a control cabinet, a Delta robot and a 380V AC power supply.

[0217] Four control methods, namely GFNTSMC, FGFNTSMC, AFGFNTSMC and SSA-AFGFNTSMC, are applied to the Delta robot experimental platform. The position data of the Delta robot end effector are collected. Fig.16 As shown, Fig.17 for Fig.16 Partial enlargement.

[0218] Compared with GFNTSMC, the trajectory tracking accuracy of SSA-AFGFNTSMC is improved by 38.5%. The driving torque of each joint under the SSA-AFGFNTSMC control strategy is as follows: Fig.18 As shown, the SSA-AFGFNTSMC controller weakens the output jitter.

[0219] The dynamics modeling of the Delta parallel robot with external disturbances and dynamics modeling errors is proposed, and the SSA-AFGFNSMC controller is proposed. It is proved that the control strategy applied to the Delta parallel robot is stable under the Lyapunov stability theory. To verify the feasibility of the scheme, simulations and experiments are carried out. The experimental results show that the control method is used for the dynamic model control of the Delta parallel robot, which improves the tracking accuracy and weakens the controller output jitter, thereby enhancing the control performance of the Delta parallel robot. It ensures that the Delta parallel robot has good tracking accuracy and strong robustness in complex industrial environments.

[0220] In one embodiment, if Fig.19 As shown, the present invention provides a control device 1900 for a parallel robot, comprising: a construction unit 1902, used to construct a new sliding mode function, which is a function that describes a global non-singular fast terminal sliding surface; a first acquisition unit 1904, used to acquire an equivalent control law corresponding to the new sliding mode function; a second acquisition unit 1906, used for an approach control law corresponding to the new sliding mode function; and a control unit 1908, used to control the parallel robot based on the equivalent control law and the approach control law.

[0221] The present invention proposes a control device 1900 for a parallel robot, which can design a function that describes a global non-singular fast terminal sliding surface, that is, a new sliding mode function. After designing the function that describes the global non-singular fast terminal sliding surface, an equivalent control law corresponding to the new sliding mode function and a convergence control law corresponding to the new sliding mode function are designed, and then the operation of the parallel robot is controlled according to the equivalent control law and the convergence control law.

[0222] In this process, the use of a global non-singular fast terminal sliding surface can improve the global convergence speed of the terminal sliding mode control and overcome the singularity of the terminal sliding surface and the slow global convergence speed of the terminal sliding surface.

[0223] In addition, the designed equivalent control law and approaching control law can improve the problem of uncertain disturbance caused by perturbations of dynamic model parameters. At the same time, good control performance can be ensured when external loads change.

[0224] In some embodiments, optionally, the equivalent control law can be understood as the derivative of the new sliding mode function with respect to time without considering external interference When the parameters change, we get When , the equivalent control quantity output by the new sliding mode function.

[0225] In some embodiments, optionally, the approaching control law represents a control law for eliminating external disturbances when the dynamic model parameters are perturbed to generate uncertain disturbances or external loads occur.

[0226] In some embodiments, optionally, the second acquisition unit 1906 is specifically used to: input the new sliding mode function and the derivative of the new sliding mode function with respect to time into the new sliding mode controller to obtain output variables output by the new sliding mode controller, wherein the new sliding mode controller is a controller constructed based on non-singular fast terminal sliding mode control and fuzzy theory; obtain the gain coefficient; and obtain the approaching control law based on the output variables and gain coefficient output by the new sliding mode controller.

[0227] In the above embodiments, in the sliding mode control, a serious drawback is the controller output jitter problem. To avoid this problem, fuzzy control is often combined with sliding mode control to reduce output jitter.

[0228] In the embodiment of the present invention, the approach control law is replaced by fuzzy output. Eliminate controller output jitter. Non-singular fast terminal sliding mode control combined with fuzzy theory can not only ensure the stability of the system, but also significantly eliminate controller output jitter compared to traditional non-singular fast terminal sliding mode control.

[0229] In the above embodiment, the novel sliding mode controller can be understood as a controller of fuzzy global fast non-singular terminal sliding mode control.

[0230] In the above embodiment, the gain coefficient is set, which can not only adjust the tracking accuracy of the control system, but also improve the speed of asymptotic stabilization of the system variables and shorten the convergence time.

[0231] In some embodiments, optionally, ;in, The output variable representing the output of the new sliding mode controller is a function of relative time. represents the decision-making mechanism in the fuzzy system, represents a new sliding mode function, represents the derivative of the new sliding mode function with respect to time, and the decision-making mechanism in the fuzzy system is:

[0232]

[0233] in, , Fuzzy Sets , The fuzzy subsets in Fuzzy Set The fuzzy subsets in , The input , , m is the number of fuzzy inference rules, Indicates filter The symbol used when

[0234] In some embodiments, optionally, the second acquisition unit 1906 is specifically used to: obtain a system error function and an error change rate function, wherein the error change rate function is the derivative of the system error function with respect to time; input the system error function and the error change rate function into an adaptive fuzzy controller to obtain a gain coefficient output by the adaptive fuzzy controller; wherein the adaptive fuzzy controller is a relationship function that uses the system error function and the error change rate function as input and the gain coefficient as output.

[0235] In this embodiment, the gain coefficient is not always constant, but can be changed according to the system error function and the error change rate function. In this process, the control accuracy and response speed of the parallel robot can be improved.

[0236] In some embodiments, optionally, ;in, yes , Fuzzy Sets , The fuzzy subset of Fuzzy Set fuzzy subset of . , The input , The fuzzy set of Output The fuzzy set of is the system error function, is the error change rate function, Represents adaptive The value after.

[0237] In some embodiments, optionally, ;in, represents the gain factor, represents the output variable of the new sliding mode controller, represents the reaching control law.

[0238] In some embodiments, optionally, the control unit 1908 is also used to: initialize the selection parameters of the new sliding mode function; determine the optimization parameters of the sparrow optimization algorithm based on the selected parameters; initialize the number of discoverers, followers and alerters in the sparrow population; obtain the performance indicators of the evaluation function output when the selected parameters are used as input; update the positions of the discoverers, followers and alerters, and randomly perform early warning behaviors; and output the optimal selection parameters of the new sliding mode function when it is detected that the iteration stop conditions are met.

[0239] In this embodiment, the sparrow optimization algorithm can be used to find the optimal sliding surface parameters, thereby improving the system accuracy and robustness of the parallel robot and achieving effective trajectory tracking. Compared with the traditional sliding mode controller, it eliminates the controller output jitter and has higher tracking accuracy and faster response speed.

[0240] In some embodiments, the present invention provides a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, the control method of the parallel robot as described above is implemented.

[0241] In some embodiments, Fig. 20 As shown, the present invention provides a parallel robot 2000, including a processor 2002 and a memory 2004, wherein the memory 2004 stores programs or instructions that can be executed on the processor 2002, and when the programs or instructions are executed by the processor 2002, the steps of the control method of the parallel robot as described above are implemented.

[0242] Among them, the memory 2004 can be used to store software programs and various data. The memory 2004 mainly includes a first storage area for storing programs or instructions and a second storage area for storing data, wherein the first storage area can store an operating system, an application program or instruction required for at least one function (such as a sound playback function, an image playback function, etc.), etc. In addition, the memory 2004 may include a volatile memory or a non-volatile memory, or the memory may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), a static random access memory (SRAM), a dynamic random access memory (DRAM), a synchronous dynamic random access memory (SDRAM), a double data rate synchronous dynamic random access memory (DDRSDRAM), an enhanced synchronous dynamic random access memory (ESDRAM), a synchronous link dynamic random access memory (SLDRAM) and a direct memory bus random access memory (DRRAM). The memory in the embodiments of the present application includes but is not limited to these and any other suitable types of memory.

[0243] In the text description of the present invention, it is understood that, except for explicit provisions and limitations, the terms "installation", "connection" and "connection" should be understood in a broad sense. For example, it can be fixed connection, detachable connection, or integral connection; it can be mechanical structure connection or electrical connection; it can be direct connection between the two, or indirect connection between the two through an intermediate medium, or it can be internal communication between two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0244] In the claims, specification and drawings of the present invention, the description of the terms "one embodiment", "some embodiments", "specific embodiments" and the like means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In the claims, specification and drawings of the present invention, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0245] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A control method for a parallel robot, characterized in that: include: Constructing a new sliding mode function, wherein the new sliding mode function is a function describing a global non-singular fast terminal sliding mode surface; Obtaining an equivalent control law corresponding to the novel sliding mode function; Obtaining a reaching control law corresponding to the novel sliding mode function; The parallel robot is controlled based on the equivalent control law and the approach control law.

2. The control method of the parallel robot according to claim 1, characterized in that: The obtaining of the approaching control law corresponding to the novel sliding mode function specifically includes: Inputting the novel sliding mode function and the derivative of the novel sliding mode function with respect to time into a novel sliding mode controller to obtain an output variable output by the novel sliding mode controller, wherein the novel sliding mode controller is a controller constructed based on non-singular fast terminal sliding mode control and fuzzy theory; Get the gain coefficient; A control law is obtained based on the output variable output by the novel sliding mode controller and the gain coefficient.

3. The control method of the parallel robot according to claim 2, characterized in that: in, The output variable representing the output of the new sliding mode controller is a function of relative time. represents the decision-making mechanism in the fuzzy system, represents a new sliding mode function, represents the derivative of the new sliding mode function with respect to time. The decision-making mechanism in the fuzzy system is: in, , Fuzzy Sets , The fuzzy subsets in Fuzzy Set The fuzzy subsets in , The input , , m is the number of fuzzy inference rules, Indicates filter The symbol used when 4. The control method of the parallel robot according to claim 2, characterized in that: The obtaining of the gain coefficient specifically includes: Obtaining a system error function and an error change rate function, wherein the error change rate function is a derivative of the system error function with respect to time; Inputting the system error function and the error change rate function into an adaptive fuzzy controller to obtain the gain coefficient output by the adaptive fuzzy controller; The adaptive fuzzy controller is a relational function that takes the system error function and the error change rate function as inputs and takes the gain coefficient as output.

5. The control method of the parallel robot according to claim 4, characterized in that: in, yes , Fuzzy Sets , The fuzzy subset of Fuzzy Set fuzzy subset of . , The input , The fuzzy set of Output The fuzzy set of is the system error function, is the error change rate function, Represents adaptive The value after.

6. The control method of the parallel robot according to claim 2, characterized in that: in, represents the gain coefficient, represents the output variable of the novel sliding mode controller, represents the reaching control law.

7. The control method of the parallel robot according to any one of claims 1 to 6, characterized in that: The control method of the parallel robot also includes: Initializing the selected parameters of the novel sliding mode function; Determine the optimization parameters of the sparrow optimization algorithm based on the selected parameters; Initialize the number of discoverers, followers, and alerters in the sparrow population; Obtaining a performance indicator output by the evaluation function when the selected parameter is used as input; Classifying the discoverer, the follower and the alerter in the sparrow optimization algorithm based on the performance index output by the evaluation function; updating the positions of the discoverer, the follower and the alerter, and randomly performing early warning actions; When it is detected that the iteration stopping condition is met, the optimal selected parameters of the novel sliding mode function are output.

8. A control device for a parallel robot, characterized in that: include: A construction unit, used for constructing a novel sliding mode function, wherein the novel sliding mode function is a function describing a global non-singular fast terminal sliding mode surface; A first acquisition unit, used for acquiring an equivalent control law corresponding to the novel sliding mode function; The second acquisition unit is used for approaching the control law corresponding to the new sliding mode function; A control unit is used to control the parallel robot based on the equivalent control law and the approach control law.

9. A readable storage medium, characterized in that: The readable storage medium stores programs or instructions, and when the programs or instructions are executed by the processor, the control method of the parallel robot according to any one of claims 1 to 7 is implemented.

10. A parallel robot, characterized in that: The method comprises a processor and a memory, wherein the memory stores programs or instructions that can be run on the processor, and when the programs or instructions are executed by the processor, the steps of the control method of the parallel robot as claimed in any one of claims 1 to 7 are implemented.

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