Unknown discrete mechanical arm finite time control method based on event triggering mechanism

Abstract

The invention provides an unknown discrete mechanical arm finite time control method based on an event trigger mechanism, and belongs to the field of automatic control. The method comprises the following steps: obtaining a virtual control signal according to a state parameter; judging whether the virtual control signal is greater than or equal to a trigger signal threshold value or not through the first event trigger, and if yes, calculating the actual control signal at the previous moment and the virtual control signal to obtain an actual control signal at the current moment; transmitting an actual control signal at the current moment to a zero-order retainer through a second event trigger; and the actual control signal at the current moment is transmitted to the actuator through the zero-order retainer, the mechanical arm is controlled through the actuator, control deviation is obtained, and the control deviation is smaller than or equal to a control deviation threshold value. According to the method and the device, the finite time controller and the event trigger are arranged, so that the efficiency of converging to the expected reference trajectory within the finite time is improved under the condition that the network bandwidth resource requirement is met.

Description

Technical Field

[0001] The present application relates to the field of automatic control, and in particular to a finite-time control method for an unknown discrete robotic arm based on an event triggering mechanism. Background Art

[0002] Complex mechanical systems often exhibit many nonlinear characteristics. Modern control systems have higher requirements for performance, so nonlinear control theory has been widely and deeply studied. However, the traditional finite-time control theory of continuous systems cannot be directly applied to discrete nonlinear systems because the Lyapunov function of discrete systems is calculated by difference rather than differentiation, and the controller design of high-order discrete systems has causal contradictions. 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 system output converge to the desired reference trajectory in a finite time. Summary of the invention

[0003] In order to solve the above technical problems, an embodiment of the present application provides a finite-time control method for an unknown discrete robotic arm based on an event trigger mechanism.

[0004] In a first aspect, an embodiment of the present application provides a finite-time control method for an unknown discrete robotic arm based on an event trigger mechanism, the method comprising: S100, obtaining state parameters of a finite time controller through a sensor and a buffer; S200, according to the state parameter, 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; 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.

[0005] In one embodiment, 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.

[0006] In one embodiment, the state parameter is based on the neural network from the 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.

[0007] In one embodiment, the 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.

[0008] In one embodiment, determining whether the virtual control signal is greater than or equal to a trigger signal threshold by using a first event trigger includes: 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.

[0009] In one embodiment, the actual control signal at the current moment is transmitted to the zero-order holder through the second event trigger, and the method further includes: The actual control signal at the current moment is transmitted to the buffer.

[0010] In one embodiment, 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.

[0011] In a second aspect, an embodiment of the present application provides a finite time control device for an unknown discrete robotic arm based on an event trigger mechanism, the device comprising: 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.

[0012] In a third aspect, an embodiment of the present application provides an electronic device comprising a memory and a processor, wherein the memory is used to store a computer program, and when the computer program is run by the processor, the method for finite-time control of an unknown discrete robotic arm based on an event trigger mechanism provided in the first aspect is executed.

[0013] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium storing a computer program, which, when running on a processor, executes the unknown discrete robotic arm finite-time control method based on an event trigger mechanism provided in the first aspect.

[0014] The above-mentioned unknown discrete manipulator finite time control method based on event trigger mechanism provided by the present application comprises: S100, obtaining state parameters of the finite time controller through sensors and buffers; S200, using a neural network to obtain the state parameters from the first 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; S300, through the first event trigger, it is determined whether the virtual control signal is greater than or equal to the trigger signal threshold. If so, the actual control signal at the previous moment and the virtual control signal are operated to obtain the actual control signal at the current moment; S400, through the second event trigger, the actual control signal at the current moment is transmitted to the zero-order holder; S500, through the zero-order holder, the actual control signal at the current moment is transmitted to the actuator, and the robot arm is controlled by the actuator to obtain a control deviation, and the control deviation is less than or equal to the control deviation threshold. The present application improves the convergence to the expected reference trajectory in a limited time while meeting the network bandwidth resource requirements by setting a finite time controller and an event trigger. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solution of the present application, the following is a brief introduction to the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present application and should not be regarded as limiting the scope of protection of the present application. In each of the drawings, similar components are numbered similarly.

[0016] Figure 1 A schematic diagram of a flow chart of a finite-time control method for an unknown discrete robotic arm based on an event triggering mechanism provided in an embodiment of the present application is shown; Figure 2The overall block diagram of the unknown discrete manipulator finite time control system based on the event trigger mechanism provided by the embodiment of the present application is shown; Figure 3 Another schematic diagram of the process of the unknown discrete mechanical arm finite time control method based on the event trigger mechanism provided by the embodiment of the present application is shown; Figure 4 The overall block diagram of the finite time controller provided by the embodiment of the present application is shown; Figure 5 A schematic diagram of the structure of a finite-time control device for an unknown discrete mechanical arm based on an event triggering mechanism provided in an embodiment of the present application is shown; Icons: 500 - finite time control device of unknown discrete robot arm based on event triggering mechanism, 501 - acquisition module, 502 - update module, 503 - operation module, 504 - first transmission module, 505 - second transmission module. DETAILED DESCRIPTION

[0017] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments.

[0018] The components of the embodiments of the present application generally described and shown in the drawings herein may be arranged and designed in various configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0019] Hereinafter, the terms "including", "having" and their cognates, which may be used in various embodiments of the present application, are intended only to indicate specific features, numbers, steps, operations, elements, components, or combinations of the foregoing items, and should not be understood as first excluding the existence of one or more other features, numbers, steps, operations, elements, components, or combinations of the foregoing items or adding the possibility of one or more features, numbers, steps, operations, elements, components, or combinations of the foregoing items.

[0020] Furthermore, the terms “first”, “second”, “third”, etc. are merely used for distinguishing descriptions and are not to be understood as indicating or implying relative importance.

[0021] Unless otherwise defined, all terms (including technical terms and scientific terms) used herein have the same meanings as those generally understood by those skilled in the art to which the various embodiments of the present application belong. The terms (such as those defined in generally used dictionaries) will be interpreted as having the same meanings as the contextual meanings in the relevant technical field and will not be interpreted as having idealized meanings or overly formal meanings unless clearly defined in the various embodiments of the present application.

[0022] Example 1 An embodiment of the present application provides a finite-time control method for an unknown discrete robotic arm based on an event triggering mechanism.

[0023] See also Figure 1 , the unknown discrete manipulator finite time control method based on event trigger mechanism includes S100-S500: S100, obtaining a state signal of a currently controlled object through a sensor, and obtaining an actual control signal output by an actuator at a previous moment through a buffer.

[0024] In this embodiment, see Figure 2 , is the overall block diagram of the closed-loop control system. The state parameters include the state signal of the robot arm (actuator), the actual control signal of the robot arm at the last moment, and the state signal of the robot arm obtained by the sensor. , obtain the actual control signal stored in the buffer at the previous moment .

[0025] S200, according to the state parameter, using a neural network from the 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.

[0026] See also Figure 3 In one embodiment, the S200 includes S201-S205: 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, .

[0027] In this embodiment, see Figure 4 , is the construction of the finite time controller. When designing a finite time controller, from the first order to the The output signal of the filter is updated in sequence , neural network parameters And the output signal of the virtual controller , and then get the virtual control signal .

[0028] For example, we first need to construct the first order (i.e. ) when the filter error model is calculated by the formula Define the initial filter error model, where is the output of the first-order virtual controller to be designed, that is, the input of the filter, is the output signal of the first-order filter. When the filter error model of the first order is The error model of the filter is expressed as ,in To be designed The virtual controller in the stage, Defined as The output signal of the filter.

[0029] 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 Tracking error model.

[0030] In this embodiment, it is also necessary to use the formula Construct a first-order error signal model, where is the first-order error model, i.e., tracking error. is the actual trajectory, For the reference trajectory, the formula is: For the next moment of the first-order filter output signal ( ) filter parameters are updated according to the tracking error model after system transformation and Get the first order Error tracking model at time: .

[0031] In one embodiment, S202 includes: constructing the first 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 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.

[0032] In this embodiment, when the error model is updated to Stage time, that is When Definition The tracking error model of the order is based on the tracking error model after system transformation: , get the The next moment ( ), that is, .

[0033] S203: Using a neural network to approximate the unknown dynamic signal The actual control signal of order is used to obtain the approximation weight.

[0034] In this embodiment, neural network is used to approximate unknown dynamic signals because of the existence of a universal approximation theorem for neural networks. The universal approximation theorem refers to the fact that radial basis neural networks are widely used to approximate unknown nonlinear functions because they are easy to implement. The principle is that for any defined in a bounded compact set, Smooth function inside , can be approximated by radial basis function neural network, and the ideal approximation form is: ,in, 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 normal number.

[0035] Furthermore, a neural network is used to approximate the unknown dynamics. According to the definition of the universal approximation theorem of the neural network, the unknown term can be expressed as ,in The ideal weights for the neural network to approximate the unknown terms, is the approximation error. Since the ideal weight is unknown, an estimated value is used The weight error satisfies , is the estimated weight (approximation weight).

[0036] S204: Based on the approximation weight and the The first-order tracking error model is constructed First-order virtual controller model.

[0037] In this embodiment, when the first order, according to the approximation weight The virtual controller model is designed as ,in , is a positive number, satisfy , ,and and is a positive odd number, so , the reference trajectory is known information, that is, In the The moment is known.

[0038] Optionally, substitute the first-order virtual controller model into the first-order Error tracking model at time: , and finally the transformation expression of the error tracking model at the next moment is obtained, which can be expressed as ,Right now The error tracking model after the transformation of time is to prove that the virtual control signal of the current design satisfies the finite time stability. The adaptive update law of the neural network is designed as , where .

[0039] Optional, when When the order is reached, a neural network is used to approximate the unknown dynamics ,in For the i The ideal weights of the neural network, To approximate the error, since the ideal weight is unknown, an estimated value is used during the operation Approximation. The weight error satisfies , To estimate the weights, design a virtual controller ,in , is the positive number to be designed, satisfy , ,and q and l is a positive odd number. Substitute the virtual controller model into The next moment ( ), that is, You will get , The error model after the transformation of time is used to prove that the virtual control signal of the current design satisfies the finite time stability.

[0040] 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.

[0041] In this embodiment, after repeatedly executing S201-S204, according to the transformed system model and the error tracking model definition, it can be obtained , according to the definition of the transformed control signal, we get , design the virtual control signal as ,in , is the positive number to be designed, p satisfy , Will be in n The first-order controller is designed, so The error dynamics can be expressed as: ,in, is called the given event trigger threshold.

[0042] 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.

[0043] In this embodiment, after obtaining the virtual control signal Afterwards, determine whether the virtual control signal is greater than or equal to the trigger signal threshold , the actual control signal at the previous moment and the virtual control signal are calculated to obtain the actual control signal at the current moment. The event triggering mechanism (ETM) is used to obtain the actual control signal at the current moment. , that is, in the update operation process , the default trigger time is 0, Taken from the buffer, the actual control signal is finally Transmitted to the buffer, there is a communication network between the controller and the actuator. At the trigger moment, the controller's signal is updated to the zero-order holder (ZOH), and the control information of the previous trigger moment is maintained at other times.

[0044] Optionally, introduce virtual control signals The design goal is to design a virtual control signal, and then obtain the input signal after the event trigger mechanism . The output of the time controller is To facilitate the analysis of event triggering mechanism, the trigger error is defined as , , where is the actual control signal at the current moment after adding the virtual control signal, The actual control signal output by the actuator at the last moment. The trigger threshold is defined according to the trigger error. , is called the given event trigger threshold. Based on the transformed system model, the definition of the event trigger sequence is obtained , the event trigger condition description only works when the virtual control signal When it is greater than the trigger threshold, the actuator control signal will be updated. , the actuator still maintains the control signal of the last triggering moment, and the zero-order holder information remains unchanged.

[0045] In one embodiment, judging whether the virtual control signal is greater than or equal to a trigger signal threshold by using a first event trigger includes: judging whether the virtual control signal is greater than or equal to a trigger signal threshold according to formula (2); 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.

[0046] In this embodiment, for the convenience of analysis, the sub-formula can be further written as , Obviously, there is a time-varying parameter Make .

[0047] S400 , transmitting the actual control signal at the current moment to the zero-order holder through a second event trigger.

[0048] In this embodiment, if the difference exceeds the trigger condition, the control input can be updated. Transmit to ZOH. That is, define a monotonically increasing time sequence , For the trigger moment, =0, that is, the default time 0 is the initial trigger time. The control information is only transmitted to the actuator at the trigger time, and the actuator still uses the control information of the last trigger time at other times. The actual operation method is to use a zero-order holder in front of the controller to hold the information transmitted at the last trigger time. When this moment is the trigger moment, the controller information is transmitted through the network to update the information in the zero-order holder.

[0049] 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.

[0050] In this embodiment, see Figure 2 , for the system model of the robot arm, considering the situation in real life where most of the nonlinear functions of the system are unknown, design the controller , ultimately ensuring the closed-loop system output Able to converge to the desired reference trajectory in a finite time , the state of the controlled object Through the sensor, For a known bounded smooth reference trajectory, a virtual input signal is designed through a finite time controller , superimposed to obtain Input buffer, output buffer The closed-loop control system is based on the initial value. Start. If The current error model of order is less than the control deviation, that is, , the loop is terminated.

[0051] In one embodiment, the method further includes: if the control deviation is greater than the control deviation threshold, repeatedly executing S100-S500 in a loop until the control deviation is less than or equal to the control deviation threshold.

[0052] In this embodiment, the control deviation That is, if the output value of the tracking error model at the current moment is greater than the control deviation threshold, the state parameters continue to be obtained through the sensor and the buffer, and the state parameters are input into the finite time controller. The finite time controller updates the parameters through steps S201-S205 until the control deviation is less than or equal to the control deviation threshold.

[0053] In one embodiment, the actual control signal at the current moment is transmitted to the zero-order holder through the second event trigger, and the method further includes: transmitting the actual control signal at the current moment to the buffer.

[0054] In this embodiment, when the second event trigger transmits the actual control signal at the current moment to the zero-order holder, the control signal at the current moment is Transmitted to the buffer to provide the actual control signal for the next stage parameter update.

[0055] In one embodiment, the method further includes: setting the virtual controller parameters and exponential order parameters; setting the actual controller initial values; setting the filter parameters and initial values; and setting the initial weights of the neural network.

[0056] In this embodiment, the virtual controller parameters are set , virtual controller exponential order parameters p , filter parameters , initial value , the actual controller initial value , set the initial weights of the neural network ,set up , reference trajectory , event trigger threshold ,deviation .

[0057] The present embodiment provides an unknown discrete manipulator finite time control method based on an event trigger mechanism, S100, obtaining state parameters of a finite time controller through a sensor and a buffer; S200, using a neural network to obtain the state parameters from the first 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; S300, through the first event trigger, it is determined whether the virtual control signal is greater than or equal to the trigger signal threshold. If so, the actual control signal at the previous moment and the virtual control signal are operated to obtain the actual control signal at the current moment; S400, the actual control signal at the current moment is transmitted to the zero-order holder through the second event trigger; S500, the actual control signal at the current moment is transmitted to the actuator through the zero-order holder, and the robot arm is controlled through the actuator to obtain a control deviation, and the control deviation is less than or equal to the control deviation threshold. The present application improves the convergence to the expected reference trajectory in a finite time while meeting the network bandwidth resource requirements by setting a finite time controller and an event trigger.

[0058] Example 2 In addition, an embodiment of the present application provides an unknown discrete robotic arm finite time control device based on an event trigger mechanism, which is applied to electronic equipment.

[0059] like Figure 5As shown, the unknown discrete mechanical arm finite time control device 500 based on the event trigger mechanism includes: An acquisition module 501 is used to acquire state parameters of a finite time controller through a sensor and a buffer; The updating module 502 is used to use a neural network to update the state parameters from the first 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; The operation module 503 is used to determine whether the virtual control signal is greater than or equal to the trigger signal threshold through the first event trigger, and if so, to calculate 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 504, configured to transmit the actual control signal at the current moment to the zero-order holder through a second event trigger; The second transmission module 505 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.

[0060] Optionally, the second transmission module 505 is further used to determine if the control deviation is greater than the control deviation threshold, and then repeatedly execute S100-S500 in a loop until the control deviation is less than or equal to the control deviation threshold.

[0061] Optionally, the updating module 502 is also used in 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 The first-order tracking error model; S203, using a neural network to approximate the unknown dynamic signal to the The actual control signal of the first order is used to obtain an approximation weight; S204, according to the approximation weight and the first The first-order tracking error model is constructed S205, repeat S201-S204, and finally obtain the first virtual controller model; 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.

[0062] Optionally, the updating module 502 is further configured to construct the first 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.

[0063] The operation module 503 is further used to determine whether the virtual control signal is greater than or equal to the trigger signal threshold according to formula (2); 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.

[0064] The first transmission module 504 is further configured to transmit the actual control signal at the current moment to the buffer.

[0065] The unknown discrete robotic arm finite time control device 500 based on event trigger mechanism also includes a setting module for setting the virtual controller parameters and exponential order parameters; setting the actual controller initial values; setting the filter parameters and initial values; and setting the initial weights of the neural network.

[0066] The unknown discrete robotic arm finite time control device 500 based on event trigger mechanism provided in this embodiment can implement the unknown discrete robotic arm finite time control method based on event trigger mechanism provided in Example 1, and will not be described again here to avoid repetition.

[0067] The unknown discrete manipulator finite time control device based on event trigger mechanism provided in this embodiment obtains the state parameters of the finite time controller through sensors and buffers; according to the state parameters, a neural network is used to obtain the state parameters of the finite time controller from the first 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; the first event trigger is used to determine whether the virtual control signal is greater than or equal to the trigger signal threshold, and if so, the actual control signal at the previous moment and the virtual control signal are operated to obtain the actual control signal at the current moment; the actual control signal at the current moment is transmitted to the zero-order holder through the second event trigger; the actual control signal at the current moment is transmitted to the actuator through the zero-order holder, and the robotic arm is controlled through the actuator to obtain a control deviation, and the control deviation is less than or equal to the control deviation threshold. The present 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 an event trigger.

[0068] Example 3 In addition, an embodiment of the present application provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program runs on the processor, it executes the unknown discrete robotic arm finite time control method based on the event trigger mechanism provided in Example 1.

[0069] The electronic device provided in the embodiment of the present invention can execute the steps of the unknown discrete robotic arm finite time control method based on the event trigger mechanism provided in the above method embodiment 1, which will not be described again here to avoid repetition.

[0070] Example 4 The present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the method for finite-time control of an unknown discrete robotic arm based on an event triggering mechanism provided in Example 1 is implemented.

[0071] In this embodiment, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0072] The computer-readable storage medium provided in this embodiment can implement the unknown discrete robotic arm finite-time control method based on the event trigger mechanism provided in Example 1, and will not be described again here to avoid repetition.

[0073] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or terminal including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or terminal. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the presence of other identical elements in the process, method, article or terminal including the element.

[0074] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for a terminal (which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods described in each embodiment of the present application.

[0075] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present application, ordinary technicians in this field can also make many forms without departing from the purpose of the present application and the scope of protection of the claims, all of which are within the protection 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 actual control signal at the current moment is transmitted to the zero-order holder through the second event trigger, and the method further 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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