A fixed-time anti-interference control method and system for operating a robot

Abstract

The present invention proposes a fixed-time anti-interference control method and system for an operating robot, which belongs to the field of robot control technology; by constructing a dynamic model of the operating robot, the dynamic model is converted into a second-order dynamic model by using coordinate transformation; based on the first-order system virtual control signal, a robust and precise differentiator is designed and a second-order system tracking trajectory reference signal and a tracking error signal of the operating robot are obtained; a first virtual compensation signal is designed for the first-order system and a first error equation is constructed, and a first-order system virtual fixed-time control strategy is designed according to the first error equation; a second virtual compensation signal is designed for the second-order system and a second error equation is constructed; an ELM network is constructed, and a second-order system fixed-time control strategy is designed according to the second error equation and the output signal of the ELM network. The present invention can ensure that trajectory tracking is not affected by nonlinear dynamics and realize trajectory tracking within a fixed time.

Description

Technical Field

[0001] The present invention belongs to the technical field of robot control, and in particular relates to a fixed-time anti-interference control method and system for operating a robot. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] Manipulators are robotic systems composed of multiple joints and links, and are widely used in complex industrial fields such as intelligent manufacturing, industrial production, and logistics and transportation. The task of manipulators is usually to make the movement path of the robotic arm move along a given reference trajectory, that is, to achieve predetermined trajectory tracking. Due to the complexity of the working environment, the manipulator system is inevitably affected by nonlinear unknown dynamics such as internal model uncertainty and external environmental interference. This type of nonlinear dynamics is related to the system state, and some nonlinear dynamics, known as mismatched interference, are insensitive to control force, and it is difficult to design a control strategy to compensate. Therefore, it is of great application significance to ensure that the manipulator maintains trajectory tracking under nonlinear unknown dynamics.

[0004] In recent years, many scholars have proposed many traditional advanced control strategies to deal with the trajectory tracking problem of manipulating robots under disturbance and uncertainty, including adaptive control, sliding mode control, robust control, etc. However, these traditional advanced control algorithms are only effective under strict assumptions of the manipulating robot, such as bounded disturbance norm and parameterizable unknown dynamics. In order to effectively solve the more general nonlinear dynamics of the system, intelligent learning technologies such as neural networks and fuzzy logic have been widely studied and applied by scholars due to their excellent online learning capabilities. As a special neural network, extreme learning machine (ELM) learning technology has the characteristics of random allocation of activation functions and input weights. Compared with traditional neural network learning algorithms, it has the advantage of fast response speed. Therefore, in recent years, ELM learning technology has been used to solve the nonlinear dynamics of the system. However, the extreme learning machine technology proposed at present still requires a lot of learning and adjustment of output weight parameters, consumes a lot of computing resources, and the sharp increase in computing power makes the control time performance unable to be guaranteed.

[0005] As an important transient performance indicator, trajectory tracking error convergence time is one of the important indicators to consider the quality of the operating robot system. At present, the research on fixed-time control can achieve the convergence of dynamic systems within a fixed time under any initial state, but considering the general unknown nonlinear dynamics and limited computing resources, it is still difficult to achieve trajectory tracking within a fixed time. Summary of the invention

[0006] In order to overcome the above-mentioned deficiencies of the prior art, the present invention provides a fixed-time anti-interference control method and system for an operating robot, which can ensure that the trajectory tracking of the operating robot is not affected by nonlinear dynamics and realize trajectory tracking within a fixed time.

[0007] To achieve the above objectives, one or more embodiments of the present invention provide the following technical solutions:

[0008] A first aspect of the present invention provides a fixed-time anti-interference control method for operating a robot.

[0009] A fixed-time anti-interference control method for operating a robot, comprising:

[0010] According to the dynamic characteristics of the operating robot, a dynamic model of the operating robot is constructed; by using coordinate transformation, the dynamic model is converted into a second-order dynamic model, and the second-order dynamic model is represented as a first-order system and a second-order system;

[0011] A robust and precise differentiator is designed based on the virtual control signal of the first-order system, the reference signal of the first-order system tracking trajectory is set, and the reference signal of the second-order system tracking trajectory is obtained through the robust and precise differentiator;

[0012] Acquire a tracking error signal of the operating robot according to the dynamic model, a first-order system tracking trajectory reference signal and a second-order system tracking trajectory reference signal;

[0013] A first virtual compensation signal is designed for the first-order system, a first error equation is constructed based on the first virtual compensation signal and the tracking error signal, and a virtual fixed-time control strategy for the first-order system is designed according to the first error equation; a second virtual compensation signal is designed for the second-order system, a second error equation is constructed based on the second virtual compensation signal and the tracking error signal; an ELM network is constructed, and a fixed-time control strategy for the second-order system is designed according to the second error equation and the output signal of the ELM network.

[0014] Furthermore, according to the dynamic characteristics of the operating robot, a dynamic model of the operating robot is constructed through the Euler-Lagrange dynamic equation.

[0015] Furthermore, a second-order system tracking trajectory reference signal is obtained through a robust and precise differentiator, specifically: the virtual control signal of the first-order system is input into the robust and precise differentiator, and an accurate estimate of the speed state of the operating robot is obtained through fixed-time filtering of the robust and precise differentiator, and it is used as the second-order system tracking trajectory reference signal.

[0016] Furthermore, the MLP method is used to adjust the unified weight estimation value of the ELM network.

[0017] Furthermore, the influence of mismatched disturbance of the first-order system is eliminated based on the first-order system virtual fixed-time control strategy, and the influence of disturbance of the second-order system is eliminated based on the second-order system fixed-time control strategy.

[0018] Furthermore, the tracking error signal of the operating robot includes a trajectory tracking error signal and a speed tracking error signal.

[0019] Furthermore, the fixed-time anti-interference control method also includes: constructing a Lyapunov function, and performing theoretical verification of the stability of the control method according to the Lyapunov stability theorem, the fixed-time principle, and the system error and compensation control equation.

[0020] A second aspect of the present invention provides a fixed-time anti-interference control system for operating a robot.

[0021] A fixed-time anti-interference control system for operating a robot, comprising:

[0022] The model building module is configured to: build a dynamic model of the operating robot according to the dynamic characteristics of the operating robot; transform the dynamic model into a second-order dynamic model by using coordinate transformation, wherein the second-order dynamic model is represented as a first-order system and a second-order system;

[0023] The robust and precise differentiator design module is configured to: design a robust and precise differentiator based on a virtual control signal of a first-order system, set a first-order system tracking trajectory reference signal, and obtain a second-order system tracking trajectory reference signal through the robust and precise differentiator;

[0024] A tracking error signal acquisition module is configured to: acquire a tracking error signal of the operating robot according to the dynamic model, a first-order system tracking trajectory reference signal and a second-order system tracking trajectory reference signal;

[0025] The fixed-time control strategy generation module is configured to: design a first virtual compensation signal for a first-order system, construct a first error equation based on the first virtual compensation signal and a tracking error signal, and design a virtual fixed-time control strategy for the first-order system according to the first error equation; design a second virtual compensation signal for a second-order system, construct a second error equation based on the second virtual compensation signal and the tracking error signal; construct an ELM network, and design a fixed-time control strategy for the second-order system according to the second error equation and the output signal of the ELM network.

[0026] A third aspect of the present invention provides a computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the steps of a fixed-time anti-interference control method for operating a robot as described in the first aspect of the present invention.

[0027] The fourth aspect of the present invention provides an electronic device, including a memory, a processor, and a program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps in a fixed-time anti-interference control method for operating a robot as described in the first aspect of the present invention are implemented.

[0028] One or more of the above technical solutions have the following beneficial effects:

[0029] The present invention uses a robust and precise differentiator to design a multi-layer virtual control strategy, so that the operating robot can handle the first-order system mismatch anti-interference problem; and uses the nonlinear approximation ability of the ELM network to simulate nonlinear unknown dynamics. The fixed-time control strategy designed based on the robust and precise differentiator and the ELM network can effectively make the control process of the operating robot unaffected by the initial state of the system and nonlinear disturbances, and enable the operating robot to track the predetermined trajectory within a calculable fixed time.

[0030] The present invention utilizes the MLP method to adjust the unified weight estimation value of the ELM network, avoids adjusting all parameter values ​​of the ELM network, reduces the amount of calculation, saves calculation resources, and has practical significance.

[0031] Advantages of additional aspects of the present invention will be given in part in the following description, and in part will become obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0033] Figure 1 This is a flow chart of a fixed-time anti-interference control method for operating a robot in Embodiment 1 of the present invention.

[0034] Figure 2 Schematic diagram of the position trajectory and reference trajectory of the operating robot in Embodiment 1 of the present invention.

[0035] Figure 3 Schematic diagram of the speed trajectory and reference speed of the operating robot in Embodiment 1 of the present invention.

[0036] Figure 4 Schematic diagram of trajectory tracking error of an operating robot in Embodiment 1 of the present invention.

[0037] Figure 5 This is a virtual control input response diagram in the first embodiment of the present invention.

[0038] Figure 6 This is a diagram of the actual control input response of the operating robot in the first embodiment of the present invention.

[0039] Figure 7 This is a diagram for adaptively adjusting control parameters of an operating robot in Embodiment 1 of the present invention. DETAILED DESCRIPTION

[0040] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0041] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present invention. To facilitate understanding of the embodiments of the present invention, the following terms are explained:

[0042] 1) Manipulator: refers to a robot system composed of multiple joints and connecting rods; the manipulator in the present invention is a multi-arm manipulator, and the actual object is a multi-joint industrial robot.

[0043] 2) Anti-interference control: refers to the design of control strategies to compensate for the influence of unknown nonlinear dynamics, including external disturbances and internal uncertainties of the system, to ensure system stability;

[0044] 3) Fixed time control: refers to the control strategy that can make the system reach stability before a fixed time under any initial state of the system;

[0045] 4) Robust and precise differentiator: It is a fixed-time filter that can make the differentiator state converge to the input signal of the differentiator within a fixed time;

[0046] 5) Adaptive control: refers to the process of online estimation and adjustment of system control parameters so that the control system can adapt to changes such as internal system dynamics or external disturbances;

[0047] 6) Extreme learning machine (ELM): It is a single hidden layer neural network. Its main feature is that the weights and bias coefficients of the hidden layer nodes can be randomly generated and will not be adjusted after generation. The only thing that needs to be determined is the weight coefficient of the output layer.

[0048] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0049] The overall idea proposed by the present invention is as follows: The present invention provides a fixed-time anti-interference control method and system for operating a robot under unknown nonlinear dynamics. First, a multi-layer virtual control strategy is designed using a robust and precise differentiator to compensate for mismatched interference; then, the nonlinear approximation capability of the ELM network is used to simulate nonlinear unknown dynamics. Based on the ELM network architecture, a minimum learning parameter (MLP) method is proposed. By adjusting the minimum network parameters and using the minimum computing resources, the impact of nonlinear dynamics on the system is effectively evaluated. Finally, the present invention constructs a new fixed-time anti-interference control strategy to ensure that trajectory tracking is not affected by nonlinear dynamics and realizes trajectory tracking within a fixed time.

[0050] Embodiment 1

[0051] This embodiment discloses a fixed-time anti-interference control method for operating a robot.

[0052] like Figure 1 As shown, a fixed-time anti-interference control method for an operating robot is provided. When the operating robot operates in an unknown nonlinear dynamic environment, a multi-layer adaptive anti-interference controller is established through a robust precise differentiator and an ELM network, so that the operating robot can track a preset position trajectory. The method specifically includes the following steps:

[0053] Step S1, constructing a dynamic model of the operating robot according to the dynamic characteristics of the operating robot; using coordinate transformation, converting the dynamic model into a second-order dynamic model, the second-order dynamic model is represented as a first-order system and a second-order system, and then describing mismatched disturbances and nonlinear dynamics;

[0054] Step S2, designing a robust and precise differentiator based on the virtual control signal of the first-order system, setting the first-order system tracking trajectory reference signal and obtaining the second-order system tracking trajectory reference signal through the robust and precise differentiator;

[0055] Step S3, acquiring a tracking error signal of the operating robot according to the dynamic model, a first-order system tracking trajectory reference signal, and a second-order system tracking trajectory reference signal;

[0056] Step S4, designing a first virtual compensation signal for the first-order system, constructing a first error equation based on the first virtual compensation signal and the tracking error signal, and designing a first-order system virtual fixed-time control strategy according to the first error equation;

[0057] Step S5, design a second virtual compensation signal for the second-order system, and construct a second error equation based on the second virtual compensation signal and the tracking error signal; construct an ELM network, and design a second-order system fixed-time control strategy according to the second error equation and the output signal of the ELM network.

[0058] To facilitate understanding of the embodiments of the present invention, further detailed explanation and description are given below for each step:

[0059] In step S1, according to the dynamic characteristics of the operating robot, the Euler-Lagrange dynamic equation is used to construct The dynamic model of the operating robot with a manipulator is established as follows:

[0060] ;

[0061] in, Respectively represent the angular position, velocity, and acceleration state of the robot's operating arm. is a positive definite symmetric inertia matrix; Represents the Coriolis and centrifugal force vectors; is the generalized force vector; is the viscous friction coefficient; is an unknown bounded external disturbance that satisfies the condition ,in is an unknown positive constant. Represents the generalized control force vector of external input, which is determined by the actual control input and uncertain unknown dynamics Composition, which can be described as Without loss of generality, the dynamic Satisfy status related conditions ,in , and is an unknown positive constant.

[0062] By using coordinate transformation, the dynamic model of the operating robot is converted into a second-order dynamic model. The first-order system and the second-order system are expressed as follows:

[0063] ;

[0064] in, and denote the first-order system and the second-order system, respectively; , ; is the unknown mismatch disturbance caused by the sensor measurement error, satisfying , is an unknown constant; is an unknown nonlinear dynamic and ; is the inverse matrix of the inertia matrix.

[0065] In step S2, the relationship between the virtual control signal of the first-order system and the speed state of the operating robot is established to achieve accurate estimation of the speed state of the operating robot within a fixed time, which is used as the trajectory reference signal of the second-order system. This is specifically achieved through the following steps:

[0066] Step S2-1: Definition To operate the robot to the desired position trajectory, the virtual control signal of the first-order system is defined as , this virtual control signal will be specifically designed in step S4. Based on the virtual control signal, the following robust precise differentiator is designed:

[0067] ;

[0068] in, and They are and The derivative of and The dynamic equations of represents the deviation of the first output state of the robust exact differentiator from the virtual control signal of the first-order system, and is a deviation The equation is used to ensure the deviation Can converge within a fixed time; , and It is a positive constant used to adjust the speed of convergence; and is the output state of the robust exact differentiator; is a symbolic function used to design an equation corresponding to a signal so that the signal converges within a fixed time. The specific definition is:

[0069] ;

[0070] This robust and accurate differentiator can ensure that the output of the robust and accurate differentiator Converges in a fixed time, the virtual control signal is defined as .

[0071] Step S2-2: Define output status , ;in, represents an accurate estimate of the velocity state of the operating robot, represents an accurate estimate of the acceleration state of the operating robot. Then the designed virtual control signal The robot velocity state is accurately estimated by using a consistent robust exact differentiator and fixed-time filtering. , and use it as the trajectory reference signal of the second-order system.

[0072] In step S3, a tracking error signal of the operating robot is obtained according to the robot dynamics model, the first-order system tracking trajectory reference signal, and the second-order system tracking trajectory reference signal output by the robust precise differentiator; wherein the tracking error signal includes a trajectory tracking error signal and a velocity tracking error signal.

[0073] Specifically, the tracking error signal is defined as ,in, , ; and Represent the trajectory tracking error signal and the velocity tracking error signal respectively, and They are and The transposed matrix of Represents the desired position trajectory of the operating robot.

[0074] In step S4, a first virtual compensation signal is designed for the first-order system, a first error equation is constructed based on the first virtual compensation signal and the trajectory tracking error signal of the operating robot, and a first-order virtual fixed-time control strategy is designed based on the first error equation; the first-order virtual fixed-time control strategy is used to eliminate the influence of mismatch disturbances of the first-order system and ensure the stability of the first-order system. Step S4 can be implemented by the following process:

[0075] Step S4-1: a first virtual compensation signal designed for a first-order system for:

[0076] ;

[0077] in, is the first virtual compensation signal The derivative of The dynamic equation of Indicates the first virtual compensation signal The initial value of and represent a first virtual compensation signal and a second virtual compensation signal respectively; is a normal number, is the virtual compensation signal of the second-order subsystem to be designed.

[0078] Step S4-2: construct a first error equation for the trajectory tracking error signal of the operating robot, namely:

[0079] ;

[0080] in, represents the first error equation, represents the trajectory error signal, represents a first virtual compensation signal.

[0081] Step S4-3: design a virtual fixed time control strategy based on the first error equation, that is:

[0082] ;

[0083] in, , is a positive adjustment parameter, , , , , It is an adaptive signal The equation is used to ensure the adaptive signal can converge within a fixed time. ; As unknown positive parameter The estimated value of , its adaptive law satisfies the following formula:

[0084] ;

[0085] in, Represents the adaptation rate of the adaptive signal in the first-order system.

[0086] Step S4-4: Definition ;in, Represents the adaptive estimation signal right Estimated error. According to the adaptive law in step S4-3, the error system of the adaptive parameters is .

[0087] In step S5, a second virtual compensation signal is designed for the second-order system, and a second error equation is constructed based on the second virtual compensation signal and the tracking error signal; an ELM network is constructed and the nonlinear dynamic influence of the second-order system is estimated, and a unified weight estimate of the ELM is obtained using MLP. A fixed-time control strategy for the second-order system is designed using the constructed second error equation and the output signal of the ELM network. The second-order system fixed-time control strategy is used to eliminate the disturbance influence of the second-order system and ensure the stability of the second-order system. Step S5 can be implemented by the following process:

[0088] Step S5-1: According to the system described in step S1 (first-order system, second-order system), is the unknown term of the system, including unknown system dynamics, friction, etc., and the nonlinear dynamics can be approximated by the following ELM network:

[0089] ;

[0090] in, Represents the nonlinear dynamics under the ELM network; is the activation function of the constructed ELM network, is the output weight of the ELM network, Represents the approximation error and satisfies , is an unknown positive constant. At this time, let For nonlinear dynamic and interference signals The difference signal, ,but It can be expressed as:

[0091] ;

[0092] In order to reduce the computational complexity, the present invention introduces the MLP technology and defines the function ,in, , . Select the activation function as ,in and Represent the input weight and the hidden layer bias respectively, we know It is bounded, so we know is also bounded, then Satisfies the following relationship:

[0093] ;in, express and The maximum value of .

[0094] Step S5-2: The second virtual compensation signal designed for the second-order system is:

[0095] ;

[0096] in, Represents the second virtual compensation signal The derivative of The dynamic equation of express The initial value of Is a normal number.

[0097] Step S5-3: Construct the second error equation for the second-order system of the operating robot ;

[0098] in, represents the second error equation, represents the speed error signal, represents the second virtual compensation signal.

[0099] Step S5-4, designing a virtual fixed time control strategy based on the second error equation and the output signal of the ELM network is shown in the following formula:

[0100] ;

[0101] in, , is a positive adjustment parameter, , , , , It is an adaptive signal The equation is used to ensure the adaptive signal can converge within a fixed time. , is an unknown positive parameter The estimated value of , and its adaptive law satisfies the following formula:

[0102] ;

[0103] in, Represents the adaptation rate of the adaptive signal in the second-order system.

[0104] Step S5-5: design a fixed time control strategy based on the second error equation and the output signal of the ELM network, namely:

[0105] ;

[0106] in, , It is an adaptive signal and The equation is used to ensure the adaptive signal and Can converge within a fixed time; , , and is an unknown positive parameter and The estimated value of , and its adaptive law satisfies the following formula:

[0107] ;

[0108] ;

[0109] S5-6. Definition , , The adaptive estimation signal , , and constant parameters , , The deviation signal, , , According to the adaptive law in step S5-5, the error system of the adaptive parameters is , , ,in , , Deviation signal , , The derivative of is the dynamic equation of the deviation signal.

[0110] At this point, the fixed-time control strategy suitable for the operating robot is designed. In specific applications, the position and speed signals of the operating robot can be collected by sensors to the data acquisition card, and then uploaded to the controller (computer); the computer gives control instructions through the operating robot control strategy output by the above embodiment (specifically, it can be output control torque, which is then sent to the operating robot's actuator - stepper motor in the form of instructions, thereby performing control operations).

[0111] Furthermore, the fixed-time anti-interference control method for operating a robot further includes: constructing a Lyapunov function, and performing stability theory verification of the control method according to the Lyapunov stability theorem, the fixed-time principle, the system error, and the system error and compensation control equations in steps S3 to S5. Specifically, the stability theory verification can be achieved by following the steps S6-1 to S6-5:

[0112] Step S6-1: construct a first Lyapunov function for the first error signal defined in steps S4-2 and S4-4 of the first-order system ,Right now:

[0113] ;

[0114] According to the first virtual compensation signal in step S4-1 and the first virtual fixed time control strategy in step S4-3, the derivative of the Lyapunov function can be obtained:

[0115] ;

[0116] when , and according to the design of the adaptive law in S4-3:

[0117] ;

[0118] Step S6-2: construct a second Lyapunov function for the second error signal defined in the second-order system S5-3 and S5-6 ,Right now:

[0119] ;

[0120] According to the second virtual compensation signal in step S5-2 and the second virtual fixed time control strategy in step S5-4, the Lyapunov function can be obtained:

[0121] ;

[0122] when , and according to the design of the adaptive law in step S5-4:

[0123] ;

[0124] Step S6-3: To theoretically verify the effectiveness and stability of the controller, a Lyapunov function is constructed for the second virtual compensation signal. as follows:

[0125] ;

[0126] in, According to the second virtual compensation signal equation designed in step S5-1 and step S5-2, the derivative of the above Lyapunov function can be obtained:

[0127] ;

[0128] According to the designed robust exact differentiator, we know that it can be achieved in a fixed time ,in, is a small positive constant. Therefore, we can get:

[0129] ;

[0130] in, , ; From the above formula, we can finally get is a bounded signal, whose bound is .

[0131] Step S6-4: According to the adaptive system error defined in steps S4-4 and S5-6, , , , In addition, according to the inequality and ,in is a positive number, it can be seen that the inequality in step S6-2 can be further written as;

[0132] ;

[0133] in, , , .

[0134] According to the fixed time control theory, the error term after compensation can be obtained ( ) converges to a small neighborhood near zero in a fixed time, and the convergence time is:

[0135] ;

[0136] Step S6-5: From steps S6-3 and S6-4, it is known that they are all bounded signals, and it can be known that the error is also bounded. Then it can be obtained that the controller can control the system to reduce the error to a minimum value within a fixed time, and at the same time the control time of the system operation can be calculated.

[0137] Furthermore, the present invention also verifies the effectiveness of the method of the present invention by actual operation through computer simulation software MATLAB, and the actual operation verification process is as follows:

[0138] This example uses computer software MATLAB to verify the control method of the present invention. The parameters related to each step in the simulation are selected as follows:

[0139] Specifically, the operating robot parameters in step S1 are selected as follows:

[0140] ;

[0141] ;

[0142] ;

[0143] in, , , , . , , . External disturbance , The mismatch disturbance is , the uncertain dynamics are The initial value of the joint position is set to , .

[0144] In step S2, the desired trajectory is selected as:

[0145] ;

[0146] The robust exact differentiator parameters are: , , .

[0147] In step S4 and step S5, the compensation and control parameters are selected as follows:

[0148]

[0149] By selecting the above parameters, the operating robot will track the reference trajectory signal before 4.4s.

[0150] In step S5, the activation function of the ELM network is selected as , the number of hidden layer neurons is neurons, parameters and Respectively and Random selection within the interval.

[0151] Result description:

[0152] like Figure 2 , Figure 3 As shown, the operating robot in this embodiment can track the predetermined trajectory and operate stably under unknown nonlinear dynamics.

[0153] like Figure 4 As shown, the control method adopted for operating the robot in this embodiment has good robustness and stability against the influence of unknown nonlinear dynamics, so that the robot can track the desired position trajectory before 4.4s.

[0154] like Figure 5 , Figure 6 As shown, in this embodiment, the virtual control input and actual control input of the operating robot can compensate for the influence of mismatched disturbances and general nonlinear dynamics.

[0155] like Figure 7As shown, the adaptive parameter estimation of the operating robot in this embodiment makes the system more adaptable to disturbances, and the parameter estimation value can also reach a stable state.

[0156] Based on the above verification, it can be known that the present invention uses a fixed-time control method, so that the operating robot can be unaffected by the initial state of the system and reach a stable operating state faster, thereby ensuring the steady-state performance of the operating robot. The present invention uses the properties of the differentiator to design a compensation signal, which can compensate for the influence of mismatched disturbances on the operating robot. In addition, the present invention uses the ELM framework of MLP technology to estimate the influence of general unknown nonlinear dynamics with minimal computing resources, avoiding the problem of computational explosion caused by adjusting the output weights of all ELM networks. At the same time, the present invention adopts an adaptive control strategy to enable the system to operate stably in general disturbances with state-related conditions; at the same time, the controller parameters change with the environment, so that the control method of the present invention can enable the operating robot to reach a stable operating state in a fixed time in different environments.

[0157] Embodiment 2

[0158] This embodiment discloses a fixed-time anti-interference control system for operating a robot, comprising:

[0159] The model building module is configured to: build a dynamic model of the operating robot according to the dynamic characteristics of the operating robot; transform the dynamic model into a second-order dynamic model by using coordinate transformation, wherein the second-order dynamic model is represented as a first-order system and a second-order system;

[0160] The robust and precise differentiator design module is configured to: design a robust and precise differentiator based on a virtual control signal of a first-order system, set a first-order system tracking trajectory reference signal, and obtain a second-order system tracking trajectory reference signal through the robust and precise differentiator;

[0161] A tracking error signal acquisition module is configured to: acquire a tracking error signal of the operating robot according to the dynamic model, a first-order system tracking trajectory reference signal and a second-order system tracking trajectory reference signal;

[0162] The fixed-time control strategy generation module is configured to: design a first virtual compensation signal for a first-order system, construct a first error equation based on the first virtual compensation signal and a tracking error signal, and design a virtual fixed-time control strategy for the first-order system according to the first error equation; design a second virtual compensation signal for a second-order system, construct a second error equation based on the second virtual compensation signal and the tracking error signal; construct an ELM network, and design a fixed-time control strategy for the second-order system according to the second error equation and the output signal of the ELM network.

[0163] Embodiment 3

[0164] The purpose of this embodiment is to provide a computer-readable storage medium.

[0165] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in a fixed-time anti-interference control method for operating a robot as described in the first embodiment of the present disclosure.

[0166] Embodiment 4

[0167] The purpose of this embodiment is to provide an electronic device.

[0168] An electronic device includes a memory, a processor, and a program stored in the memory and executable on the processor. When the processor executes the program, the steps in a fixed-time anti-interference control method for operating a robot as described in Embodiment 1 of the present disclosure are implemented.

[0169] The steps involved in the apparatuses of the above embodiments 2, 3 and 4 correspond to the method embodiment 1, and the specific implementation methods can refer to the relevant description part of embodiment 1. The term "computer-readable storage medium" should be understood as a single medium or multiple media including one or more instruction sets; it should also be understood to include any medium that can store, encode or carry an instruction set for execution by a processor and enable the processor to execute any method in the present invention.

[0170] Those skilled in the art should understand that the modules or steps of the present invention described above can be implemented by a general-purpose computer device, or alternatively, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. The present invention is not limited to any specific combination of hardware and software.

[0171] Although the above describes the specific implementation mode of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without creative work are still within the scope of protection of the present invention.

Claims

1. A fixed-time anti-interference control method for operating a robot, characterized in that: include: According to the dynamic characteristics of the operating robot, a dynamic model of the operating robot is constructed; The dynamic model is converted into a second-order dynamic model by using coordinate transformation, wherein the second-order dynamic model is represented as a first-order system and a second-order system; A robust and precise differentiator is designed based on the virtual control signal of the first-order system, the reference signal of the first-order system tracking trajectory is set, and the reference signal of the second-order system tracking trajectory is obtained through the robust and precise differentiator; Acquire a tracking error signal of the operating robot according to the dynamic model, a first-order system tracking trajectory reference signal and a second-order system tracking trajectory reference signal; A first virtual compensation signal is designed for the first-order system, a first error equation is constructed based on the first virtual compensation signal and the tracking error signal, and a virtual fixed-time control strategy for the first-order system is designed according to the first error equation; a second virtual compensation signal is designed for the second-order system, a second error equation is constructed based on the second virtual compensation signal and the tracking error signal; an ELM network is constructed, and a fixed-time control strategy for the second-order system is designed according to the second error equation and the output signal of the ELM network; wherein the ELM network is an extreme learning machine network.

2. A fixed-time anti-interference control method for operating a robot as claimed in claim 1, characterized in that: According to the dynamic characteristics of the operating robot, the dynamic model of the operating robot is constructed through the Euler-Lagrange dynamic equation.

3. A fixed-time anti-interference control method for operating a robot as claimed in claim 1, characterized in that: The second-order system tracking trajectory reference signal is obtained through a robust and precise differentiator. Specifically, the virtual control signal of the first-order system is input into the robust and precise differentiator, and an accurate estimation of the speed state of the operating robot is obtained through fixed-time filtering of the robust and precise differentiator, and it is used as the second-order system tracking trajectory reference signal.

4. A fixed-time anti-interference control method for operating a robot as claimed in claim 1, characterized in that: The MLP method is used to adjust the unified weight estimation value of the ELM network; wherein the MLP method is the minimum learning parameter method, and the ELM network is the extreme learning machine network.

5. A fixed-time anti-interference control method for operating a robot as claimed in claim 1, characterized in that: Based on the first-order system virtual fixed-time control strategy, the influence of mismatch disturbance of the first-order system is eliminated, and based on the second-order system fixed-time control strategy, the influence of disturbance of the second-order system is eliminated.

6. A fixed-time anti-interference control method for operating a robot as claimed in claim 1, characterized in that: The tracking error signal of the operating robot includes a trajectory tracking error signal and a speed tracking error signal.

7. A fixed-time anti-interference control method for operating a robot as claimed in claim 1, characterized in that: Also includes: The Lyapunov function is constructed, and the stability theory of the control method is verified based on the Lyapunov stability theorem, the fixed time principle, and the system error and compensation control equation.

8. A fixed-time anti-interference control system for operating a robot, characterized in that: include: The model building module is configured to: build a dynamic model of the operating robot according to the dynamic characteristics of the operating robot; The dynamic model is converted into a second-order dynamic model by using coordinate transformation, wherein the second-order dynamic model is represented as a first-order system and a second-order system; The robust and precise differentiator design module is configured to: design a robust and precise differentiator based on a virtual control signal of a first-order system, set a first-order system tracking trajectory reference signal, and obtain a second-order system tracking trajectory reference signal through the robust and precise differentiator; A tracking error signal acquisition module is configured to: acquire a tracking error signal of the operating robot according to the dynamic model, a first-order system tracking trajectory reference signal and a second-order system tracking trajectory reference signal; The fixed-time control strategy generation module is configured to: design a first virtual compensation signal for a first-order system, construct a first error equation based on the first virtual compensation signal and a tracking error signal, and design a virtual fixed-time control strategy for the first-order system according to the first error equation; design a second virtual compensation signal for a second-order system, and construct a second error equation based on the second virtual compensation signal and the tracking error signal; construct an ELM network, and design a fixed-time control strategy for the second-order system according to the second error equation and the output signal of the ELM network; wherein the ELM network is an extreme learning machine network.

9. A computer-readable storage medium having a program stored thereon, characterized in that: When the program is executed by a processor, the steps of a fixed-time anti-interference control method for operating a robot as described in any one of claims 1 to 7 are implemented.

10. An electronic device comprising a memory, a processor, and a program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps in the fixed-time anti-interference control method for operating a robot as described in any one of claims 1-7 are implemented.

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