Finite-Time Control Method for Unknown Discrete Manipulators Based on Event-Triggered Mechanism

Abstract

The present application provides a finite-time control method for an unknown discrete manipulator based on an event-triggering mechanism, belonging to the field of automatic control. The method includes: obtaining a virtual control signal according to state parameters; determining whether the virtual control signal is greater than or equal to a trigger signal threshold through a first event trigger, and if so, performing an operation on the actual control signal at the previous moment and the virtual control signal to obtain the actual control signal at the current moment; transmitting the actual control signal at the current moment to a zero-order hold through a second event trigger; transmitting the actual control signal at the current moment to an actuator through the zero-order hold, and controlling the manipulator through the actuator to obtain a control deviation, and the control deviation is less than or equal to a control deviation threshold. By setting a finite-time controller and an event trigger, the present application improves the efficiency of converging to an expected reference trajectory in a finite time while meeting the requirements of network bandwidth resources.

Description

Technical Field

[0001] This application relates to the field of automatic control, and particularly to a finite-time control method for an unknown discrete manipulator based on an event-triggering mechanism. Background Art

[0002] Complex mechanical systems often exhibit many non-linear characteristics. Modern control systems have put forward higher requirements for performance. Therefore, non-linear control theory has been widely and deeply studied. However, the traditional finite-time control theory for continuous systems cannot be directly applied to discrete non-linear systems because the calculation method of the Lyapunov function for discrete systems is difference instead of derivative, and there is a causal contradiction problem in the controller design of high-order discrete systems. In a network control environment, the network bandwidth resource requirements between the controller and the actuator need to be reduced. Therefore, while reducing the communication frequency, it is necessary to make the output of the system converge to the desired reference trajectory in finite time. Summary of the Invention

[0003] To solve the above technical problems, an embodiment of this application provides a finite-time control method for an unknown discrete manipulator based on an event-triggering mechanism.

[0004] In a first aspect, an embodiment of this application provides a finite-time control method for an unknown discrete manipulator based on an event-triggering mechanism, and the method includes:

[0005] S100. Obtain the state parameters of the finite-time controller through a sensor and a buffer;

[0006] S200. According to the state parameters, use a neural network to update the state parameters of the virtual controller and the filter in the finite-time controller in order from the th order to the th order to obtain a virtual control signal;

[0007] S300. Determine whether the virtual control signal is greater than or equal to a trigger signal threshold through a first event trigger. If so, perform an operation on the actual control signal at the previous moment and the virtual control signal to obtain the actual control signal at the current moment;

[0008] S400. Transmit the actual control signal at the current moment to a zero-order hold through a second event trigger;

[0009] S500. Transmit the actual control signal at the current moment to an actuator through the zero-order hold, and control the manipulator through the actuator to obtain a control deviation, and the control deviation is less than or equal to a control deviation threshold.

[0010] In an embodiment, the method further includes:

[0011] If the control deviation is greater than the control deviation threshold, then loop and execute S100 - S500 repeatedly until the control deviation is less than or equal to the control deviation threshold.

[0012] In one embodiment, according to the state parameters, the neural network is used to update the state parameters of the virtual controller and the filter in the finite-time controller in order from the -th order to the -th order to obtain a virtual control signal, including:

[0013] S201. Construct a -th order filter error model according to the input signal of the -th order virtual controller and the output signal of the -th order filter. The input signal of the -th order virtual controller and the output signal of the -th order filter, construct a -th order filter error model. ; ;

[0014] S202. Construct a -th order tracking error model according to the -th order filter error model, the unknown dynamic signal, and the state signal of the control object at the -th moment. The -th order filter error model, the unknown dynamic signal, and the state signal of the control object at the -th moment, construct a -th order tracking error model. ;

[0015] S203. Use the neural network to approximate the unknown dynamic signal to the -th order actual control signal to obtain the approximation weight. ;

[0016] S204. Construct a -th order virtual controller model according to the approximation weight and the -th order tracking error model. The approximation weight and the -th order tracking error model, construct a -th order virtual controller model. ;

[0017] S205. Repeat S201 - S204 until finally obtaining the -th order virtual controller model and the -th order tracking error model, and design the virtual control signal according to the -th order virtual controller model and the -th order tracking error model. The -th order virtual controller model and the -th order tracking error model, and design the virtual control signal according to the -th order virtual controller model and the -th order tracking error model. The -th order virtual controller model and the -th order tracking error model, and design the virtual control signal according to the -th order virtual controller model and the -th order tracking error model.

[0018] In one embodiment, constructing the -th order tracking error model according to the -th order filter error model, the unknown dynamic signal, and the state signal of the control object at the -th moment includes: The -th order filter error model, the unknown dynamic signal, and the state signal of the control object at the -th moment, construct a -th order tracking error model, including: ;

[0019] Construct a -th order tracking error model according to formula (1). ;

[0020] Formula (1): ;

[0021] Where is the output of the -order tracking error model, is the input of the -order virtual controller model to be constructed, is the output of the -order filter error model, is the output of the error tracking model at the -order time, is the state signal of the -order controlled object, is the output of the -order filter signal, is the -order unknown dynamic signal.

[0022] In one embodiment, determining whether the virtual control signal is greater than or equal to the trigger signal threshold by the first event trigger includes:

[0023] Determining whether the virtual control signal is greater than or equal to the trigger signal threshold according to formula (2);

[0024] Formula (2): ;

[0025] where is the virtual control signal output by the first event trigger, is the trigger signal threshold, is the virtual control signal input to the first event trigger.

[0026] In one embodiment, transmitting the actual control signal at the current time to the zero-order hold by the second event trigger, the method further includes:

[0027] Transmitting the actual control signal at the current time to the buffer.

[0028] In one embodiment, the method further includes:

[0029] Setting the virtual controller parameters and the exponential order parameters;

[0030] Setting the initial value of the actual controller;

[0031] Setting the filter parameters and the initial value;

[0032] Setting the initial weights of the neural network.

[0033] In a second aspect, an unknown discrete manipulator finite-time control device based on an event-triggering mechanism provided by an embodiment of the present application includes:

[0034] An acquisition module, configured to acquire state parameters of a finite-time controller through a sensor and a buffer;

[0035] An update module, configured to, according to the state parameters, adopt a neural network to sequentially update state parameters of a virtual controller and a filter in the finite-time controller from the th order to the th order to obtain a virtual control signal;

[0036] An operation module, configured to determine, through a first event trigger, whether the virtual control signal is greater than or equal to a trigger signal threshold. If so, perform an operation on the actual control signal at the previous moment and the virtual control signal to obtain the actual control signal at the current moment;

[0037] A first transmission module, configured to transmit the actual control signal at the current moment to a zero-order hold through a second event trigger;

[0038] A second transmission module, configured to transmit the actual control signal at the current moment to an actuator through the zero-order hold, and control a robotic arm through the actuator to obtain a control deviation, where the control deviation is less than or equal to a control deviation threshold.

[0039] In a third aspect, an embodiment of the present application provides an electronic device, including a memory and a processor. The memory is configured to store a computer program, and the computer program, when running on the processor, executes the finite-time control method for an unknown discrete robotic arm based on an event trigger mechanism provided in the first aspect.

[0040] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program. The computer program, when running on a processor, executes the finite-time control method for an unknown discrete robotic arm based on an event trigger mechanism provided in the first aspect.

[0041] For the finite-time control method for an unknown discrete robotic arm based on an event trigger mechanism provided in the present application above, S100: Acquire state parameters of a finite-time controller through a sensor and a buffer; S200: According to the state parameters, adopt a neural network from the th order to the Update the state parameters of the virtual controller and the filter in the finite-time controller in order to obtain a virtual control signal; S300. Determine whether the virtual control signal is greater than or equal to a trigger signal threshold through a first event trigger. If so, perform an operation on the actual control signal at the previous moment and the virtual control signal to obtain the actual control signal at the current moment; S400. Transmit the actual control signal at the current moment to a zero-order hold through a second event trigger; S500. Transmit the actual control signal at the current moment to the actuator through the zero-order hold, and control the robotic arm through the actuator to obtain a control deviation, where the control deviation is less than or equal to a control deviation threshold. By setting a finite-time controller and event triggers, the present application improves the ability to converge to a desired reference trajectory in a finite time while meeting the requirements of network bandwidth resources. Description of the Drawings

[0042] To more clearly illustrate the technical solutions of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present application and should not be regarded as limiting the protection scope of the present application. In each drawing, similar components are numbered similarly.

[0043] Figure 1 Fig. shows a flowchart of a finite-time control method for an unknown discrete robotic arm based on an event-triggering mechanism provided by an embodiment of the present application;

[0044] Figure 2 Fig. shows an overall block diagram of a finite-time control system for an unknown discrete robotic arm based on an event-triggering mechanism provided by an embodiment of the present application;

[0045] Figure 3 Fig. shows another flowchart of a finite-time control method for an unknown discrete robotic arm based on an event-triggering mechanism provided by an embodiment of the present application;

[0046] Figure 4 Fig. shows an overall block diagram of a finite-time controller provided by an embodiment of the present application;

[0047] Figure 5 Fig. shows a structural diagram of a finite-time control device for an unknown discrete robotic arm based on an event-triggering mechanism provided by an embodiment of the present application;

[0048] Reference Signs: 500 - Finite-time control device for an unknown discrete robotic arm based on an event-triggering mechanism, 501 - Acquisition module, 502 - Update module, 503 - Operation module, 504 - First transmission module, 505 - Second transmission module. Detailed Embodiments

[0049] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments.

[0050] Generally, the components of the embodiments of the present application described and illustrated herein can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the present application claimed, but merely represents selected embodiments of the present application. All other embodiments obtained by those skilled in the art based on the embodiments of the present application without creative efforts fall within the scope of protection of the present application.

[0051] Hereinafter, the terms "including", "having" and their cognates that can be used in various embodiments of the present application are only intended to represent specific features, numbers, steps, operations, elements, components or combinations of the foregoing items, and should not be construed as first excluding the existence of one or more other features, numbers, steps, operations, elements, components or combinations of the foregoing items or increasing the possibility of one or more features, numbers, steps, operations, elements, components or combinations of the foregoing items.

[0052] In addition, the terms "first", "second", "third", etc. are only used for distinguishing descriptions and cannot be understood as indicating or implying relative importance.

[0053] Unless otherwise defined, all terms (including technical terms and scientific terms) used herein have the same meaning as commonly understood by those of ordinary skill in the art to which various embodiments of the present application belong. The terms (such as those defined in a commonly used dictionary) will be interpreted as having the same meaning as the contextual meaning in the relevant technical field and will not be interpreted as having an idealized meaning or being overly formal, unless clearly defined in various embodiments of the present application.

[0054] Embodiment 1

[0055] The embodiment of the present application provides a finite-time control method for an unknown discrete manipulator based on an event-triggering mechanism.

[0056] See Figure 1 , the finite-time control method for an unknown discrete manipulator based on an event-triggering mechanism includes S100 - S500:

[0057] S100. Obtain the state signal of the current control object through a sensor, and obtain the actual control signal output by the actuator at the previous moment through a buffer.

[0058] In this embodiment, see Figure 2, which is the overall block diagram of the closed-loop control system. The state parameters include the state signal of the robotic arm (actuator) and the actual control signal of the robotic arm at the previous moment. The state signal of the robotic arm is obtained through sensors. , and the actual control signal at the previous moment stored in it is obtained through the buffer. .

[0059] S200. According to the state parameters, the neural network is used to update the state parameters of the virtual controller and the filter in the finite-time controller in sequence from the -th order to the -th order to obtain the virtual control signal.

[0060] See Figure 3 . In an embodiment, the S200 includes S201 - S205:

[0061] S201. According to the input signal of the -th order virtual controller and the output signal of the -th order filter, construct the -th order filter error model. .

[0062] In this embodiment, see Figure 4 . For the construction of the finite-time controller, when designing the finite-time controller, the output signal of the filter, , the neural network parameters and the output signal of the virtual controller are updated in sequence from the 1st order to the -th order, and then the virtual control signal is obtained.

[0063] Exemplarily, first, it is necessary to construct the error model of the filter at the 1st order (i.e., ) in the finite-time controller. The initial filter error model is defined by the formula , where is the output of the virtual controller to be designed at the 1st order, which is also the input of the filter, and is the output signal of the 1st order filter. When it is necessary to define the filter error model of the -th order, the filter error model of the -th order is expressed as , where is the virtual controller in the -th order to be designed, and is defined as the output signal of the -th order filter.

[0064] S202. According to the The first-order filter error model, the unknown dynamic signal, and the state signal of the controlled object at the moment are used to construct the -order tracking error model.

[0065] In this embodiment, it is also necessary to construct a first-order error signal model according to the formula . Among them, is the first-order error model, i.e., the tracking error, is the actual trajectory, is the reference trajectory. Through the formula: the filter parameters at the next moment ( ) of the output signal of the first-order filter are updated. According to the transformed tracking error model of the system and , the error tracking model at the first-order moment is obtained: .

[0066] In one embodiment, the S202 includes: constructing a -order tracking error model according to formula (1); formula (1): ; where is the output of the -order tracking error model, is the input of the -order virtual controller model to be constructed, is the output of the -order filter error model, is the -order -moment error tracking model, is the state signal of the -order controlled object, is the output of the -order filter signal, is the -order unknown dynamic signal.

[0067] In this embodiment, when the error model is updated to the -order, that is, , it is necessary to define a -order tracking error model according to the formula . According to the transformed tracking error model of the system: , the error tracking model at the next moment ( ) of the -order is obtained, that is, .

[0068] S203: Use a neural network to approximate the unknown dynamic signal to the -order actual control signal to obtain the approximation weight.

[0069] In this embodiment, due to the universal approximation theorem of neural networks, neural networks are used to approximate unknown dynamic signals. The universal approximation theorem means that the radial basis neural network is widely used to approximate unknown non-linear functions because it is relatively easy to implement. Its principle is that for any smooth function defined within a bounded and compact set , it can be approximated by a radial basis function neural network. The ideal approximation form is: , where is the optimal approximation weight, m is the number of neurons, and the ideal form of the neural network weight is written as . is the input vector of the neural network, is the ideal approximation error, , is a positive constant.

[0070] Furthermore, neural networks are used to approximate unknown dynamics. From the definition of the universal approximation theorem of the above neural networks, the unknown term can be expressed as , where is the ideal weight of the neural network to approximate the unknown term, is the approximation error. Since the ideal weight is unknown, the estimated value is used. The weight error satisfies , is the estimated weight (approximation weight).

[0071] S204: Construct a th-order virtual controller model according to the approximation weight and the th-order tracking error model.

[0072] In this embodiment, when it is the first order, according to the approximation weight and the th-order tracking error model, the virtual controller model is designed as , where , are positive terms, satisfies , , and and are positive odd numbers. Therefore , the reference trajectory is known information, that is is known at the th moment.

[0073] Optionally, substitute the first-order virtual controller model into the first-order th moment error tracking model: , finally, the transformation expression of the error tracking model at the next moment can be obtained, which can be expressed as , that is The transformed error tracking model at the moment is to prove that the currently designed virtual control signal satisfies finite-time stability. The adaptive update law of its neural network is designed as , where .

[0074] Optionally, when the th order, a neural network is used to approximate the unknown dynamics , where is the ideal weight of the neural network at the i th step, is the approximation error. Since the ideal weight is unknown, an estimated value is used for approximation during the operation. The weight error satisfies , is the estimated weight. Design the virtual controller , where 、 are positive terms to be designed, satisfies , , and q and l are positive odd numbers. Substitute the virtual controller model into the error tracking model at the next moment of the th order ( ), that is When is obtained, The transformed error model at the moment is to prove that the currently designed virtual control signal satisfies finite-time stability.

[0075] S205: Repeat S201 - S204, and finally obtain the virtual controller model of the th order and the tracking error model of the th order, and design the virtual control signal according to the virtual controller model of the th order and the tracking error model of the th order.

[0076] In this embodiment, after repeating S201 - S204, according to the definition of the transformed system model and error tracking model, it can be obtained that , according to the definition of the transformed control signal, it is obtained that , design the virtual control signal as , where 、 are positive terms to be designed, p satisfies , will be at then The first-order controller design is given later, so it is known information during the step design process. From the analysis of the event-triggering mechanism, the error dynamics can be recorded as: , where is called the given event-triggering threshold.

[0077] S300. Determine whether the virtual control signal is greater than or equal to the trigger signal threshold through the first event trigger. If so, calculate the actual control signal at the current moment by operating on the actual control signal at the previous moment and the virtual control signal.

[0078] In this embodiment, after obtaining the virtual control signal , determine whether the virtual control signal is greater than or equal to the trigger signal threshold , then calculate the actual control signal at the current moment by operating on the actual control signal at the previous moment and the virtual control signal. Among them, the actual control signal at the current moment is obtained by using the event-triggering mechanism (Event triggering mechanism, ETM) , that is, in the updated calculation process , it is default to trigger at time 0 is taken from the buffer, and finally the actual control signal is transmitted to the buffer. There is a communication network between the controller and the actuator. At the trigger moment, the signal of the controller is updated to the zero-order holder (Zero-order holder, ZOH), and the control information at the previous trigger moment is maintained at other moments.

[0079] Optionally, introduce the virtual control signal , and the design goal is to design the virtual control signal, and then obtain the input signal after the event-triggering mechanism . What the controller outputs at time is . For the convenience of analyzing the event-triggering mechanism, define the trigger error , is the actual control signal at the current moment after adding the virtual control signal, is the actual control signal at the previous moment output by the actuator. Define the trigger threshold according to the trigger error, is called the given event-triggering threshold. According to the system model after transformation, obtain the definition of the event-triggering sequence . The event-triggering condition shows that only when the virtual control signal is greater than the trigger threshold, the control signal of the actuator will be updated. When When this occurs, the actuator still maintains the control signal at the previous trigger moment, and the information of the zero-order hold remains unchanged.

[0080] In one embodiment, determining whether the virtual control signal is greater than or equal to the trigger signal threshold by the first event trigger includes: determining whether the virtual control signal is greater than or equal to the trigger signal threshold according to formula (2); formula (2): ; where is the virtual control signal output through the first event trigger, is the trigger signal threshold, is the virtual control signal input to the first event trigger.

[0081] In this embodiment, for the convenience of analysis, the sub-formula can be further written from the equivalent relationship as , . Obviously, there is a time-varying parameter such that .

[0082] S400. Transmit the actual control signal at the current moment to the zero-order hold through the second event trigger.

[0083] In this embodiment, by the second trigger mechanism, if the difference exceeds the trigger condition, the control input can be updated and transmitted to the ZOH. That is, define a monotonically increasing sequence of moments , as the trigger moments, = 0, that is, the default 0 moment is the initial trigger moment. The control information is only transmitted to the actuator at the trigger moment. At other moments, the actuator still uses the control information at the previous trigger moment. The actual operation method is to use a zero-order hold in front of the controller to hold the information transmitted at the previous trigger moment. When this moment is the trigger moment, the controller information is transmitted through the network to update the information in the zero-order hold.

[0084] S500. Transmit the actual control signal at the current moment to the actuator through the zero-order hold, and control the robotic arm through the actuator to obtain a control deviation, and the control deviation is less than or equal to the control deviation threshold.

[0085] In this embodiment, referring to Figure 2 , for the system model of the robotic arm, considering the situation where most of the system's non-linear functions are unknown in real life, design the controller , and finally ensure that the output of the closed-loop system can converge to the desired reference trajectory in a finite time, and the state of the controlled object is obtained through the sensor, is a known bounded smooth reference trajectory, and a virtual input signal is designed through a finite-time controller , and the superposition is obtained and input into the buffer, and the output in the buffer is transmitted back to the controlled system through the actuator, and the closed-loop control system is started based on the initial value . If the current error model of the th order is less than the control deviation, that is , the loop is terminated.

[0086] In one embodiment, the method further includes: if the control deviation is greater than the control deviation threshold, the loop of S100-S500 is repeatedly executed until the control deviation is less than or equal to the control deviation threshold.

[0087] In this embodiment, the control deviation , that is, the output value of the tracking error model at the current moment is greater than the control deviation threshold, then continue to obtain the state parameters through the sensor and the buffer, and input the state parameters into the finite-time controller. The finite-time controller updates the parameters through the steps of S201-S205 until the control deviation is less than or equal to the control deviation threshold.

[0088] In one embodiment, the method further includes: transmitting the actual control signal at the current moment to the zero-order hold through the second event trigger, and the method further includes: transmitting the actual control signal at the current moment to the buffer.

[0089] In this embodiment, when the second event trigger transmits the actual control signal at the current moment to the zero-order hold, the control signal at the current moment is transmitted to the buffer to provide the actual control signal for the parameter update of the next order.

[0090] In one embodiment, the method further includes: setting the virtual controller parameters and the exponential order parameters; setting the initial value of the actual controller; setting the filter parameters and the initial value; setting the initial weights of the neural network.

[0091] In this embodiment, set the virtual controller parameters , the virtual controller exponential order parameters p , the filter parameters , the initial value , the initial value of the actual controller , set the initial weights of the neural network , set , the reference trajectory , the event trigger threshold , the deviation .

[0092] The finite-time control method for an unknown discrete manipulator based on an event-triggering mechanism provided in this embodiment, S100, obtains the state parameters of the finite-time controller through sensors and buffers; S200, according to the state parameters, uses a neural network to update the state parameters of the virtual controller and the filter in the finite-time controller in sequence from the th order to the th order to obtain a virtual control signal; S300, uses a first event trigger to determine whether the virtual control signal is greater than or equal to the trigger signal threshold. If so, calculates the actual control signal at the current moment by operating on the actual control signal at the previous moment and the virtual control signal; S400, transmits the actual control signal at the current moment to a zero-order hold through a second event trigger; S500, transmits the actual control signal at the current moment to the actuator through the zero-order hold, and controls the manipulator through the actuator to obtain a control deviation, and the control deviation is less than or equal to the control deviation threshold. This application improves the convergence to the desired reference trajectory in a finite time while meeting the network bandwidth resource requirements by setting a finite-time controller and event triggers.

[0093] Embodiment 2

[0094] In addition, an embodiment of this application provides a finite-time control device for an unknown discrete manipulator based on an event-triggering mechanism, which is applied to an electronic device.

[0095] As Figure 5 shown, the finite-time control device 500 for an unknown discrete manipulator based on an event-triggering mechanism includes:

[0096] An acquisition module 501, configured to obtain the state parameters of the finite-time controller through sensors and buffers;

[0097] An update module 502, configured to update the state parameters of the virtual controller and the filter in the finite-time controller in sequence from the th order to the th order according to the state parameters to obtain a virtual control signal;

[0098] An operation module 503, configured to use a first event trigger to determine whether the virtual control signal is greater than or equal to the trigger signal threshold. If so, calculates the actual control signal at the current moment by operating on the actual control signal at the previous moment and the virtual control signal;

[0099] A first transmission module 504, configured to transmit the actual control signal at the current moment to a zero-order hold through a second event trigger;

[0100] The second transmission module 505 is configured to transmit the actual control signal at the current moment to the actuator through the zero-order holder, and control the robotic arm through the actuator to obtain a control deviation, where the control deviation is less than or equal to a control deviation threshold.

[0101] Optionally, the second transmission module 505 is further configured to determine that if the control deviation is greater than the control deviation threshold, then loop and execute S100 - S500 repeatedly until the control deviation is less than or equal to the control deviation threshold.

[0102] Optionally, the update module 502 is further configured to: S201. Construct a -order filter error model according to the input signal of the -order virtual controller and the output signal of the -order filter; ; S202. Construct a -order tracking error model according to the -order filter error model, the unknown dynamic signal, and the state signal of the control object at the -th moment; S203. Approximate the unknown dynamic signal to the -order actual control signal by using a neural network to obtain an approximation weight; S204. Construct a -order virtual controller model according to the approximation weight and the -order tracking error model; S205. Repeat S201 - S204 until finally obtaining a -order virtual controller model and a -order tracking error model, and design the virtual control signal according to the -order virtual controller model and the -order tracking error model.

[0103] Optionally, the update module 502 is further configured to construct a -order tracking error model according to formula (1);

[0104] Formula (1): ;

[0105] Where is the output of the -order tracking error model, is the input of the -order virtual controller model to be constructed, is the output of the -order filter error model, is the output of the -order -th moment error tracking model, is the The state signal of the controlled object of the output of the order filter signal,

[0106] The operation module 503 is further configured to determine whether the virtual control signal is greater than or equal to the trigger signal threshold according to formula (2);

[0107] Formula (2): ;

[0108] Wherein, is the virtual control signal output by the first event trigger, is the trigger signal threshold, is the virtual control signal input to the first event trigger.

[0109] The first transmission module 504 is further configured to transmit the actual control signal at the current moment to the buffer.

[0110] The unknown discrete manipulator finite-time control device 500 based on the event-triggering mechanism further includes a setting module for setting the virtual controller parameters and the exponential order parameters; setting the initial value of the actual controller; setting the filter parameters and the initial value; setting the initial weights of the neural network.

[0111] The unknown discrete manipulator finite-time control device 500 based on the event-triggering mechanism provided in this embodiment can implement the unknown discrete manipulator finite-time control method based on the event-triggering mechanism provided in Embodiment 1. To avoid repetition, it will not be elaborated here.

[0112] The unknown discrete manipulator finite-time control device based on the event-triggering mechanism provided in this embodiment obtains the state parameters of the finite-time controller through the sensor and the buffer; according to the state parameters, uses the neural network from the order to the ​​Update the state parameters of the virtual controller and the filter in the finite-time controller in sequence to obtain a virtual control signal; determine whether the virtual control signal is greater than or equal to a trigger signal threshold through a first event trigger, and if so, perform an operation on the actual control signal at the previous moment and the virtual control signal to obtain the actual control signal at the current moment; transmit the actual control signal at the current moment to a zero-order hold through a second event trigger; transmit the actual control signal at the current moment to the actuator through the zero-order hold, and control the robotic arm through the actuator to obtain a control deviation, where the control deviation is less than or equal to a control deviation threshold. By setting a finite-time controller and event triggers, the present application improves the ability to converge to a desired reference trajectory in a finite time while meeting the requirements of network bandwidth resources.

[0113] Embodiment 3

[0114] In addition, an embodiment of the present application provides an electronic device, including a memory and a processor, where the memory stores a computer program, and when the computer program runs on the processor, it executes the finite-time control method for an unknown discrete robotic arm based on an event-triggering mechanism provided in Embodiment 1.

[0115] The electronic device provided in the embodiment of the present invention can execute the steps of the finite-time control method for an unknown discrete robotic arm based on an event-triggering mechanism provided in Embodiment 1 of the above method. To avoid repetition, it will not be elaborated here.

[0116] Embodiment 4

[0117] The present application also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the finite-time control method for an unknown discrete robotic arm based on an event-triggering mechanism provided in Embodiment 1.

[0118] In this embodiment, the computer-readable storage medium can be a read-only memory (ROM for short), a random access memory (RAM for short), a magnetic disk, or an optical disc, etc.

[0119] The computer-readable storage medium provided in this embodiment can implement the finite-time control method for an unknown discrete robotic arm based on an event-triggering mechanism provided in Embodiment 1. To avoid repetition, it will not be elaborated here.

[0120] It should be noted that in this text, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, such that a process, method, article or terminal including a series of elements not only includes those elements but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or terminal. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of additional identical elements in the process, method, article or terminal including such element.

[0121] From the description of the above embodiments, those skilled in the art can clearly understand that the above-described example methods can be implemented by means of software plus a necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation. Based on such an understanding, the technical solution of the present application, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disc) and includes several instructions for causing a terminal (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in various embodiments of the present application.

[0122] The embodiments of the present application have been described above in conjunction with the accompanying drawings. However, the present application is not limited to the above specific embodiments. The above specific embodiments are merely illustrative and not restrictive. Those of ordinary skill in the art, under the inspiration of the present application and without departing from the purpose of the present application and the scope protected by the claims, can also make many forms, all of which fall within the protection scope of the present application.

Claims

1. A finite-time control method for an unknown discrete manipulator based on an event-triggered mechanism, characterized in that: The method comprises: S100, obtaining state parameters of a finite time controller through a sensor and a buffer; S200, according to the state parameters, using a neural network to sequentially update the state parameters of the virtual controller and the filter in the finite time controller from the 1st order to the nth order to obtain a virtual control signal; S300, determining whether the virtual control signal is greater than or equal to a trigger signal threshold through a first event trigger, and if so, calculating the actual control signal at the previous moment and the virtual control signal to obtain the actual control signal at the current moment; S400, transmitting the actual control signal at the current moment to the zero-order holder through the second event trigger; S500, transmitting the actual control signal at the current moment to the actuator through the zero-order holder, and controlling the robot arm through the actuator to obtain a control deviation, where the control deviation is less than or equal to a control deviation threshold.

2. The method according to claim 1, characterized in that The method further comprises: If the control deviation is greater than the control deviation threshold, S100 - S500 are repeatedly executed in a loop until the control deviation is less than or equal to the control deviation threshold.

3. The method according to claim 1, characterized in that According to the state parameter, a neural network is used to obtain Stage to Stage The state parameters of the virtual controller and the filter in the finite time controller are updated in sequence to obtain a virtual control signal, including: S201, according to The input signal of the first virtual controller and the The output signal of the first-order filter is constructed Order filter error model, ; S202, according to the The error model of the first-order filter, the unknown dynamic signal and the first-order control object Time status signal, build the Order tracking error model; S203, using a neural network to approximate the unknown dynamic signal The actual control signal of order is used to obtain the approximation weight; S204, according to the approximation weight and the The first-order tracking error model is constructed First-order virtual controller model; S205, repeat S201-S204, and finally obtain First-order virtual controller model and first The tracking error model is The first virtual controller model and the The virtual control signal is designed by an order tracking error model.

4. The method according to claim 3, characterized in that According to the said The error model of the first-order filter, the unknown dynamic signal and the first-order control object Time status signal, build the The tracking error model consists of: According to formula (1), the Order tracking error model; Formula (1): ; in, For the The output of the order tracking error model, To be constructed The input of the virtual controller model is For the The output of the error model of the order filter, For the Step The error tracking model output at time, For the The state signal of the controlled object of the order, For the The output signal of the filter is For the Unknown dynamic signal.

5. The method according to claim 1, characterized in that The determining, by using a first event trigger, whether the virtual control signal is greater than or equal to a trigger signal threshold comprises: According to formula (2), determine whether the virtual control signal is greater than or equal to the trigger signal threshold; Formula (2): ; in, is a virtual control signal output by the first event trigger, is the trigger signal threshold, A virtual control signal for inputting the first event trigger.

6. The method according to claim 1, characterized in that The method of transmitting the actual control signal at the current moment to the zero-order holder through the second event trigger includes: The actual control signal at the current moment is transmitted to the buffer.

7. The method according to claim 1, characterized in that The method further comprises: Setting the virtual controller parameters and exponential order parameters; Set the actual controller initial value; Setting the filter parameters and initial values; Set the initial weights of the neural network.

8. A finite time control device for an unknown discrete manipulator based on an event trigger mechanism, characterized in that: The device comprises: An acquisition module, used for acquiring state parameters of the finite time controller through sensors and buffers; An updating module is used to update the state parameters using a neural network from the Stage to Stage updating the state parameters of the virtual controller and the filter in the finite time controller in order to obtain a virtual control signal; A calculation module, used for determining whether the virtual control signal is greater than or equal to a trigger signal threshold through a first event trigger, and if so, calculating the actual control signal at the previous moment and the virtual control signal to obtain the actual control signal at the current moment; A first transmission module, used for transmitting the actual control signal at the current moment to the zero-order holder through a second event trigger; The second transmission module is used to transmit the actual control signal at the current moment to the actuator through the zero-order holder, and control the robot arm through the actuator to obtain a control deviation, and the control deviation is less than or equal to a control deviation threshold.

9. An electronic device, characterized in that: It comprises a memory and a processor, wherein the memory stores a computer program, and when the computer program is run on the processor, the unknown discrete robot arm finite time control method based on an event triggering mechanism as claimed in any one of claims 1 to 7 is executed.

10. A computer-readable storage medium, characterized in that: It stores a computer program, which, when running on a processor, executes the unknown discrete robot arm finite time control method based on an event trigger mechanism as described in any one of claims 1 to 7.

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