Self-adaptive control method, device and equipment for motor system and medium

Abstract

The embodiment of the invention provides a self-adaptive control method and device for a motor system, equipment and a medium. The method comprises the steps that the actual output quantity of the motor system is acquired; estimating a state variable of the motor system by using a predefined fractional order state observer based on the actual output quantity to obtain an estimated state variable; obtaining a self-adaptive parameter variable of the motor system, and determining a Nussbaum function value corresponding to the self-adaptive parameter variable according to a pre-defined fractional order multiple heterogeneous Nussbaum function; determining a virtual control rate according to an error between the estimated state variable and an expected reference state variable; according to the Nussbaum function value and the virtual control rate, determining an adaptive control input parameter; and controlling the motor system based on the self-adaptive control input parameter to realize self-adaptive control of the motor system.

Description

Technical Field

[0001] The present application relates to the field of motor control technology, and in particular to an adaptive control method, device, equipment and medium for a motor system. Background Art

[0002] With the rapid development of industrial automation and intelligent control technologies, motor drive systems are becoming increasingly widely used in the industrial sector, encompassing a wide range of scenarios, from high-speed rotating equipment to precision positioning systems. In these complex applications, motors must not only cope with traditional challenges such as nonlinear loads, structural coupling, and external disturbances, but also overcome the uncertainties introduced by actuator failures, placing extremely high demands on control accuracy, response speed, and system stability.

[0003] At present, fractional-order differential equations have shown unique advantages in describing the actual behavior of motor systems. They can more accurately characterize non-integer-order dynamic effects caused by high-speed friction losses, viscoelastic material couplings, etc. However, in actual engineering applications, the operating environment of motor systems is complex and changeable. When encountering external interference factors or internal faults in the system, the control directions of the input channels are very likely to be inconsistent, and even extreme conditions of mutual reversal may occur. This situation is defined as "heterogeneous control direction". However, the current control research field of fractional-order systems lacks a unified theory to deal with the problem of uncertain control direction signs and possible dynamic changes over time. This makes it difficult to ensure the stable, efficient and precise operation of fractional-order systems in such complex control scenarios. Summary of the Invention

[0004] The main purpose of the embodiments of the present application is to propose an adaptive control method, device, equipment and medium for a motor system, aiming to achieve adaptive control of the motor system to ensure that the system can still perform stable control and precise tracking in the presence of unknown control direction and external interference.

[0005] To achieve the above objectives, a first aspect of an embodiment of the present application provides an adaptive control method for a motor system, the method comprising: Get the actual output of the motor system; estimating a state variable of the motor system based on the actual output using a predefined fractional-order state observer to obtain an estimated state variable; Obtaining an adaptive parameter variable of the motor system, and determining a Nussbaum function value corresponding to the adaptive parameter variable according to a predefined fractional-order multi-heterogeneous Nussbaum function; determining a virtual control rate based on an error between the estimated state variable and a desired reference state variable; determining an adaptive control input parameter according to the Nussbaum function value and the virtual control rate; The motor system is controlled based on the adaptive control input parameter.

[0006] Through the method provided in the first aspect, it is possible to achieve that even when the gain direction of the input end is unknown or undergoes random changes, the system can still maintain its stability, significantly improving the control accuracy and system robustness in application scenarios such as high-speed operation and flexible coupling, and overcoming the stability problems or control accuracy limitations that are prone to occur in existing integer-order control methods when faced with unknown gain directions and complex coupling conditions.

[0007] In one possible implementation, the method further includes: Obtain nonlinear coupling parameters, control input gain parameters and observer gain parameters of the motor system; The fractional-order state observer is constructed according to the nonlinear coupling parameter, the control input gain parameter and the observer gain parameter.

[0008] In one possible implementation, the method further includes: The observer gain parameter is updated according to the error between the estimated state variable and the desired reference state variable to control the error to converge to zero.

[0009] In one possible implementation, the method further includes: The adaptive parameter variable is updated according to the virtual control rate.

[0010] In one possible implementation, determining the virtual control rate according to the error between the estimated state variable and the expected reference state variable includes: obtaining a fractional-order error derivative term based on an error between the estimated state variable and a desired reference state variable; Obtain the forward control gain parameters of the motor system; The virtual control rate is determined according to the fractional-order error derivative term and the forward control gain parameter.

[0011] In one possible implementation, controlling the motor system based on the adaptive control input parameter includes: The adaptive control input parameters are fuzzified using a predefined fuzzy logic system to obtain fuzzy input parameters, and the motor system is controlled based on the fuzzy input parameters.

[0012] In one possible implementation, the fuzzy processing of the adaptive control input parameters by using a predefined fuzzy logic system to obtain the fuzzy input parameters includes: The predefined fuzzy logic system is used to determine the membership degree of the adaptive control input parameter based on a Gaussian function to obtain the fuzzified input parameter.

[0013] To achieve the above-mentioned object, a second aspect of an embodiment of the present application provides an adaptive control device for a motor system, the device comprising: Actual output acquisition module: used to obtain the actual output of the motor system; A state variable estimation module is configured to estimate the state variables of the motor system based on the actual output using a predefined fractional-order state observer to obtain estimated state variables; Function value acquisition module: used for acquiring the adaptive parameter variables of the motor system, and determining the Nussbaum function values corresponding to the adaptive parameter variables according to a predefined fractional-order multi-heterogeneous Nussbaum function; A control rate acquisition module is configured to determine a virtual control rate based on an error between the estimated state variable and the expected reference state variable; Input parameter acquisition module: used to determine the adaptive control input parameters according to the Nussbaum function value and the virtual control rate; A control module is configured to control the motor system based on the adaptive control input parameters.

[0014] Through the device provided in the second aspect, it is possible to achieve that the system can maintain its stability even when the gain direction of the input end is unknown or undergoes random changes, significantly improving the control accuracy and system robustness in application scenarios such as high-speed operation and flexible coupling, and overcoming the stability problems or control accuracy limitations that are prone to occur in existing integer-order control methods when faced with unknown gain directions and complex coupling conditions.

[0015] In a third aspect, an electronic device is provided, comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, an adaptive control method for a motor system as described in any possible implementation method in the first aspect is implemented.

[0016] In a fourth aspect, a computer-readable storage medium is provided, wherein the storage medium stores a computer program, and when the computer program is executed by a processor, the adaptive control method for a motor system as described in any possible implementation of the first aspect is implemented.

[0017] It can be seen from the technical solutions provided by one or more embodiments of the present specification above that the adaptive control method for a motor system provided by the embodiments of the present application obtains the actual output of the motor system, and based on the actual output, estimates the state variables of the motor system using a predefined fractional-order state observer to obtain estimated state variables, and then obtains the adaptive parameter variables of the motor system, and determines the Nussbaum function value corresponding to the adaptive parameter variable according to the predefined fractional-order multiple heterogeneous Nussbaum function, determines the virtual control rate according to the error between the estimated state variable and the expected reference state variable, determines the adaptive control input parameter according to the Nussbaum function value and the virtual control rate, and finally controls the motor system based on the adaptive control input parameter, so as to achieve the system maintaining its stability even when the input gain direction is unknown or undergoes random changes. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate one or more embodiments of this specification or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the description of one or more embodiments or the prior art. Obviously, the drawings described below are only some embodiments recorded in this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0019] Figure 1 is a flow chart of an adaptive control method for a motor system provided in an embodiment of the present application; Figure 2 Schematic diagram of the output tracking curve of the motor system provided in an embodiment of the present application; Figure 3 Schematic diagram of a tracking error change curve of a motor system provided in an embodiment of the present application; Figure 4 Schematic diagram of the adaptive control input parameter change curve provided by the embodiment of the present application; Figure 5 This is a structural block diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0020] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in one or more embodiments of this specification will be clearly and completely described below in conjunction with the drawings in one or more embodiments of this specification. Obviously, the one or more embodiments described are only part of the embodiments of this specification, not all of the embodiments. Based on one or more embodiments in this specification, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of this document.

[0021] It should be noted that although the device schematics illustrate functional module divisions and the flowcharts illustrate logical sequences, in certain circumstances, the steps shown or described may be performed in a sequence that differs from the module divisions in the device or the sequence in the flowcharts. The terms "first," "second," and so on, in the specification, claims, and drawings, are used to distinguish similar items and are not necessarily used to describe a specific sequence or precedence.

[0022] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application pertains. The terms used herein are for the purpose of describing the embodiments of this application only and are not intended to limit this application.

[0023] In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other. The embodiments of the present invention will be further described below with reference to the accompanying drawings.

[0024] In practical engineering applications, motor systems often operate in complex and ever-changing environments. When affected by external interference or internal system faults, the control directions of the input channels can easily become inconsistent, or even in opposite directions. This situation is defined as "heterogeneous control directions." However, current research on fractional-order system control lacks a unified theory to address the uncertainty of control direction signs and their potential dynamic changes over time. This makes it difficult to ensure stable, efficient, and precise operation of fractional-order systems in such complex control scenarios.

[0025] Based on this, the embodiments of the present application provide an adaptive control method, device, equipment and medium for a motor system, which can realize adaptive control of the motor system to ensure that the system can still perform stable control and precise tracking in the presence of unknown control direction and external interference.

[0026] The embodiments of the present application are further described below with reference to the accompanying drawings.

[0027] Figure 1 is an optional flow chart of an adaptive control method for a motor system provided in an embodiment of the present application. Figure 1 The method may include but is not limited to steps S100 to S600.

[0028] First, as Figure 1 As shown, an adaptive control method for a motor system is provided, the method comprising: S100: Obtain the actual output of the motor system.

[0029] S200 , estimating state variables of the motor system based on actual output quantities using a predefined fractional-order state observer to obtain estimated state variables.

[0030] It should be noted that fractional-order interconnected systems usually have complex dynamic characteristics, and their internal state variables may be affected by multiple factors, including nonlinearity, time lag, fractional-order derivatives, etc. These complex characteristics make it possible for the estimated state variables to be impossible to measure directly. Therefore, the embodiment of the present application proposes to obtain the actual output of the motor system, and based on the actual output, use a predefined fractional-order state observer to estimate the state variables of the motor system, provide feedback information for obtaining adaptive control input parameters, thereby achieving precise control of the motor and improving the operating efficiency and performance of the motor system.

[0031] S300 , obtaining adaptive parameter variables of the motor system, and determining Nussbaum function values corresponding to the adaptive parameter variables according to a predefined fractional-order multi-heterogeneous Nussbaum function.

[0032] In some embodiments, for a fractional-order interconnected system with unknown heterogeneous control directions, the embodiments of the present application propose constructing a Nussbaum function to handle the problem of unknown control directions in adaptive control, and determining the Nussbaum function values corresponding to the adaptive parameter variables by obtaining the adaptive parameter variables of the motor system according to a predefined fractional-order multiple heterogeneous Nussbaum function.

[0033] It should be noted that constructing the Nussbaum function plays an important role in adaptive control. When dealing with complex dynamic systems with unknown control directions, the Nussbaum function provides a special control gain whose sign changes alternately and whose amplitude gradually increases, thereby being able to adapt to the uncertainty of the control direction. The characteristics of this function enable it to effectively deal with the problem of unknown control direction in adaptive control, ensuring the stability and convergence of the system.

[0034] Specifically, we first need to define the Nussbaum function, assuming that the function It is a continuous function. If the function satisfies that the average value of the integral at infinity tends to positive infinity and negative infinity, then the function is called This property enables the Nussbaum function to adapt to the unknown control direction by periodically changing the sign of the control force. The specific formula is as follows: , .

[0035] Then, by obtaining the adaptive parameter variables of the motor system, the Nussbaum function value corresponding to the adaptive parameter variables is determined according to the pre-defined fractional-order multi-heterogeneous Nussbaum function. The specific formula is as follows: , in, represents the fractional-order interconnection system The heterogeneous Nussbaum functions of the input channels of each subsystem are used to achieve automatic adaptation to unknown control directions; represents the adaptive parameter variable, represents the number of the subsystem in the fractional-order interconnected system, Represents the total number of subsystems in the fractional-order interconnected system.

[0036] S400 : Determine a virtual control rate according to an error between the estimated state variable and the expected reference state variable.

[0037] In some embodiments, in order to enhance the stability of the control of the motor system by the adaptive control input parameters, a virtual control rate needs to be introduced. In the embodiment of the present application, the virtual control rate is determined based on the error between the estimated state variable and the expected reference state variable.

[0038] S500 : Determine adaptive control input parameters according to the Nussbaum function value and the virtual control rate.

[0039] S600: Control the motor system based on the adaptive control input parameters.

[0040] It should be noted that by combining the Nussbaum function value and the virtual control rate, the motor system's adaptive control input parameters can be obtained and used to control the motor system. These adaptive control input parameters can dynamically adjust the control strategy based on the motor's actual operating state, optimizing system performance and achieving more precise control. They also enable the motor control system to maintain stable operation even when the control gain direction is unknown, and maintain stable and efficient tracking performance in complex environments such as faults or sign switching, thereby improving the system's control accuracy and robustness.

[0041] Through the method provided in the first aspect, it is possible to achieve that even when the gain direction of the input end is unknown or undergoes random changes, the system can still maintain its stability, significantly improving the control accuracy and system robustness in application scenarios such as high-speed operation and flexible coupling, and overcoming the stability problems or control accuracy limitations that are prone to occur in existing integer-order control methods when faced with unknown gain directions and complex coupling conditions.

[0042] In one possible implementation, the method further includes: obtaining nonlinear coupling parameters, control input gain parameters, and observer gain parameters of the motor system; and constructing the fractional-order state observer based on the nonlinear coupling parameters, the control input gain parameters, and the observer gain parameters.

[0043] In some embodiments, since the fractional-order interconnected system has complex dynamic characteristics, its internal state variables may be affected by multiple factors, including nonlinearity, time lag, fractional-order derivatives, etc., and to address the problem that the estimated state variables may not be directly measured due to these complex characteristics, the embodiments of the present application propose to obtain the actual output of the motor system and, based on the actual output, use a predefined fractional-order state observer to estimate the state variables of the motor system, provide feedback information for obtaining adaptive control input parameters, thereby achieving precise control of the motor and improving the operating efficiency and performance of the motor system.

[0044] Specifically, constructing a fractional-order state observer requires obtaining the nonlinear coupling parameters, control input gain parameters, and observer gain parameters of the motor system. Then, the fractional-order state observer is constructed based on the obtained nonlinear coupling parameters, control input gain parameters, and observer gain parameters. The specific formula is as follows: , in, represents the estimated state variable; represents the Caputo fractional differential operator; represents the nonlinear coupling parameter; represents the unknown control gain parameter; represents the adaptive control input parameter; represents the observer gain parameter.

[0045] In one possible implementation, the method further includes: updating the observer gain parameter according to an error between the estimated state variable and a desired reference state variable, so as to control the error to converge to zero.

[0046] In some embodiments, it should be noted that by updating the observer gain parameters of the fractional-order state observer based on the error between the estimated state variable and the expected reference state variable, the error between the estimated state variable and the expected reference state variable can be converged to 0, even if the estimated state variable fits the expected reference state variable. This dynamic adjustment enables the observer to more accurately track the true state of the system and maintain a high estimation accuracy even in the presence of external interference and system uncertainty.

[0047] In one possible implementation, the method further includes: updating the adaptive parameter variable according to the virtual control rate.

[0048] It should be noted that by updating the adaptive parameter variables according to the virtual control rate and dynamically adjusting the adaptive parameter variables, rapid tracking and adaptation to the dynamic characteristics of the system can be achieved. This allows the motor system to maintain good control stability and control accuracy when facing complex environments and working conditions such as uncertainty or external interference, thereby enhancing the robustness and adaptability of the system. The specific formula is as follows: , in, represents the Caputo fractional differential operator; represents the adaptive parameter variable; represents the virtual control rate; Ensures adaptive parameter variables Dynamically adjust with the system to match unknown control direction.

[0049] In one possible implementation, determining the virtual control rate based on the error between the estimated state variable and the desired reference state variable includes: obtaining a fractional-order error derivative term based on the error between the estimated state variable and the desired reference state variable; obtaining a forward control gain parameter of the motor system; and determining the virtual control rate based on the fractional-order error derivative term and the forward control gain parameter.

[0050] In some embodiments, in order to enhance the stability of the control of the motor system by the adaptive control input parameters, a virtual control rate needs to be introduced. To obtain the virtual control rate, it is first necessary to obtain the fractional-order error derivative term based on the error between the estimated state variable and the expected reference state variable, and then obtain the forward control gain parameter of the motor system. The virtual control rate is determined based on the obtained fractional-order error derivative term and the forward control gain parameter. The specific formula is as follows: , in, represents the virtual control rate; represents the forward control gain parameter, which is used to adjust the speed of convergence of the error between the estimated state variable and the desired reference state variable; represents the error between the estimated state variable and the expected reference state variable; Represents the fractional-order error derivative term, which is used to ensure the dynamic response of the system, that is, in the process of controlling the system, ensure that the system can respond to input signals or external disturbances quickly and accurately, and maintain stability and performance indicators during the response process.

[0051] In one possible implementation, controlling the motor system based on the adaptive control input parameters includes: using a predefined fuzzy logic system to fuzzify the adaptive control input parameters to obtain fuzzy input parameters, and controlling the motor system based on the fuzzy input parameters.

[0052] It should be noted that the fuzzy logic system is able to handle the uncertainty and ambiguity in the input parameters, so that the motor control system can still maintain good control performance when facing complex nonlinear characteristics, time-varying parameters and external interference. In addition, the fuzzy logic system can quickly respond to changes in the motor's operating state and dynamically adjust the control strategy through fuzzy rules, thereby improving the system's dynamic performance and response speed. Based on this, the embodiment of the present application utilizes a predefined fuzzy logic system to fuzzify the adaptive control input parameters to obtain fuzzy input parameters, and controls the motor system based on the fuzzy input parameters. Using a predefined fuzzy logic system to fuzzify the adaptive control input parameters allows the adaptive control input parameters to be converted into fuzzy sets, and reasoning is performed through fuzzy rules to obtain more accurate control outputs, which helps to improve the control accuracy of the system and maintain the stability of the motor system operation.

[0053] In one possible implementation, the fuzzy processing of the adaptive control input parameters using a predefined fuzzy logic system to obtain the fuzzy input parameters includes: using the predefined fuzzy logic system to determine the membership of the adaptive control input parameters based on a Gaussian function to obtain the fuzzy input parameters.

[0054] In some embodiments, it should be noted that the membership function provided in the embodiments of the present application selects a Gaussian function, which has the shape of a bell curve and has the characteristics of continuity and smoothness, so that the Gaussian function is suitable for describing a continuous and smooth fuzzy set, and can achieve a smooth transition of the control input parameters. This smooth transition is particularly important for the dynamic performance of the control system, and can avoid system instability or performance degradation caused by signal mutations. In the adaptive control process, the membership of the adaptive control input parameter is determined by the Gaussian function to obtain the fuzzy input parameter, which can reduce the signal mutations generated in the adaptive control process, thereby improving the control accuracy, enhancing the robustness and adaptability of the system, and enabling it to maintain efficient and stable operation when facing various complex working conditions and uncertainties.

[0055] The specific implementation process also includes verifying the adaptive control method for the motor system provided by the embodiment of the present application. For example, a permanent magnet synchronous motor is selected as the controlled object, and a fractional order mathematical model is used to describe the dynamic characteristics of the motor. Simulation experiments are performed to comprehensively evaluate the performance of the adaptive control method in dealing with nonlinear, time-varying characteristics and external disturbances in the motor system. The simulation results are as follows: Figure 2 、 Figure 3 and Figure 4 shown, specifically, Figure 2 Schematic diagram of the motor system output tracking curve provided by the embodiment of the present application, such as Figure 2 As shown, the outputs of the two subsystems in the motor system effectively track the reference signal; Figure 3 : is a schematic diagram of a tracking error curve of a motor system provided in an embodiment of the present application, such as Figure 3 As shown, the tracking errors of the two subsystems in the motor system converge to a small enough neighborhood of 0; Figure 4 Schematic diagram of the adaptive control input parameter change curve provided by the embodiment of the present application, such as Figure 4 As shown, after a brief initial fluctuation, the control changes stabilize.

[0056] To achieve the above-mentioned object, a second aspect of an embodiment of the present application provides an adaptive control device for a motor system, the device comprising: Actual output acquisition module: used to obtain the actual output of the motor system.

[0057] A state variable estimation module is used to estimate the state variables of the motor system based on the actual output quantity using a predefined fractional-order state observer to obtain estimated state variables.

[0058] It should be noted that fractional-order interconnected systems usually have complex dynamic characteristics, and their internal state variables may be affected by multiple factors, including nonlinearity, time lag, fractional-order derivatives, etc. These complex characteristics make it possible for the estimated state variables to be impossible to measure directly. Therefore, the embodiment of the present application proposes to obtain the actual output of the motor system, and based on the actual output, use a predefined fractional-order state observer to estimate the state variables of the motor system, provide feedback information for obtaining adaptive control input parameters, thereby achieving precise control of the motor and improving the operating efficiency and performance of the motor system.

[0059] Function value acquisition module: used to obtain the adaptive parameter variables of the motor system, and determine the Nussbaum function values corresponding to the adaptive parameter variables according to a predefined fractional-order multi-heterogeneous Nussbaum function.

[0060] In some embodiments, for a fractional-order interconnected system with unknown heterogeneous control directions, the embodiments of the present application propose constructing a Nussbaum function to handle the problem of unknown control directions in adaptive control, and determining the Nussbaum function values corresponding to the adaptive parameter variables by obtaining the adaptive parameter variables of the motor system according to a predefined fractional-order multiple heterogeneous Nussbaum function.

[0061] It should be noted that constructing the Nussbaum function plays an important role in adaptive control. When dealing with complex dynamic systems with unknown control directions, the Nussbaum function provides a special control gain whose sign changes alternately and whose amplitude gradually increases, thereby being able to adapt to the uncertainty of the control direction. The characteristics of this function enable it to effectively deal with the problem of unknown control direction in adaptive control, ensuring the stability and convergence of the system.

[0062] Specifically, we first need to define the Nussbaum function, assuming that the function It is a continuous function. If the function satisfies that the average value of the integral at infinity tends to positive infinity and negative infinity, then the function is called This property enables the Nussbaum function to adapt to the unknown control direction by periodically changing the sign of the control force. The specific formula is as follows: , .

[0063] Then, by obtaining the adaptive parameter variables of the motor system, the Nussbaum function value corresponding to the adaptive parameter variables is determined according to the pre-defined fractional-order multi-heterogeneous Nussbaum function. The specific formula is as follows: , in, represents the fractional-order interconnection system The heterogeneous Nussbaum functions of the input channels of each subsystem are used to achieve automatic adaptation to unknown control directions; represents the adaptive parameter variable, represents the number of the subsystem in the fractional-order interconnected system, Represents the total number of subsystems in the fractional-order interconnected system.

[0064] A control rate acquisition module is used to determine a virtual control rate according to an error between the estimated state variable and the expected reference state variable.

[0065] In some embodiments, in order to enhance the stability of the control of the motor system by the adaptive control input parameters, a virtual control rate needs to be introduced. In the embodiment of the present application, the virtual control rate is determined based on the error between the estimated state variable and the expected reference state variable.

[0066] Input parameter acquisition module: used to determine the adaptive control input parameters according to the Nussbaum function value and the virtual control rate.

[0067] A control module is configured to control the motor system based on the adaptive control input parameters.

[0068] It should be noted that by combining the Nussbaum function value and the virtual control rate, the motor system's adaptive control input parameters can be obtained and used to control the motor system. These adaptive control input parameters can dynamically adjust the control strategy based on the motor's actual operating state, optimizing system performance and achieving more precise control. They also enable the motor control system to maintain stable operation even when the control gain direction is unknown, and maintain stable and efficient tracking performance in complex environments such as faults or sign switching, thereby improving the system's control accuracy and robustness.

[0069] Through the device provided in the second aspect, it is possible to achieve that even when the gain direction of the input end is unknown or undergoes random changes, the system can still maintain its stability, significantly improving the control accuracy and system robustness in application scenarios such as high-speed operation and flexible coupling, and overcoming the stability problems or control accuracy limitations that are prone to occur in existing integer-order control methods when faced with unknown gain directions and complex coupling conditions.

[0070] The present application also provides an electronic device, such as Figure 5 As shown, the electronic device 1400 includes: one or more processors 1410; The memory 1420 stores one or more programs. When the one or more programs are executed by the one or more processors 1410, the one or more processors 1410 implement the adaptive control method for the motor system provided by any embodiment of the present application.

[0071] The memory 1420 is a non-transient network system that can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory 1420 may include a high-speed random access memory and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory 1420 may optionally include a memory 1420 remotely located relative to the processor 1410, and these remote memories 1420 may be connected to the processor 1410 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0072] The memory 1420 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 1420 can store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 1420 and is called by the processor 1410 to execute the methods of the embodiments of this application.

[0073] The processor 1410 can be implemented using a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application.

[0074] In some embodiments, the electronic device further comprises: Input / output interface, used to realize information input and output; Communication interface, used to realize communication interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, Wi-Fi, Bluetooth, etc.); A bus that transmits information between various components of the device (e.g., the processor 1410 , memory 1420 , input / output interfaces, and communication interfaces); The processor 1410 , the memory 1420 , the input / output interface, and the communication interface can be communicatively connected to each other within the device via a bus.

[0075] An embodiment of the present application further provides a computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions are used to execute the adaptive control method for a motor system provided by any embodiment of the present application.

[0076] An embodiment of the present application also provides a computer program product, including a computer program or computer instructions, which are stored in a computer-readable storage medium. A processor of a computer device reads the computer program or computer instructions from the computer-readable storage medium, and the processor executes the computer program or computer instructions, so that the computer device executes the adaptive control method for a motor system provided by any embodiment of the present application.

[0077] The system architecture and application scenarios described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Those skilled in the art will appreciate that with the evolution of the system architecture and the emergence of new application scenarios, the technical solutions provided in the embodiments of the present application are equally applicable to similar technical problems.

[0078] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0079] Those skilled in the art will appreciate that all or some of the steps and systems disclosed above can be implemented as software, firmware, hardware, or any suitable combination thereof. Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software may be distributed on computer-readable media, which may include computer storage media (or non-transitory media) and communication media (or transient media). As is well known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information, such as computer-readable instructions, data structures, program modules, or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disks (DVDs) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by a computer. Furthermore, as is well known to those skilled in the art, communication media typically embodies computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0080] The above description of some embodiments of the present application with reference to the accompanying drawings does not limit the scope of the present invention. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and essence of the present invention shall be within the scope of the present application.

[0081] Those skilled in the art will appreciate that all or some of the steps in the methods, systems, and functional modules / units in the devices disclosed above may be implemented as software, firmware, hardware, or appropriate combinations thereof.

[0082] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0083] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0084] The various embodiments in this specification are described in a progressive manner. Similar parts between the various embodiments can be referred to in conjunction with each other. Each embodiment focuses on the differences between the other embodiments. In particular, the system embodiments are generally similar to the method embodiments, so the description is relatively simple. For relevant parts, refer to the description of the method embodiments.

[0085] The foregoing description of this specification describes specific embodiments. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in an order different from that described in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order shown or the sequential order to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0086] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but are not intended to limit the scope of the present invention. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and essence of the present invention should be within the scope of the present invention.

Claims

1. An adaptive control method for a motor system, characterized in that: The method comprises: Get the actual output of the motor system; estimating a state variable of the motor system based on the actual output using a predefined fractional-order state observer to obtain an estimated state variable; Obtaining an adaptive parameter variable of the motor system, and determining a Nussbaum function value corresponding to the adaptive parameter variable according to a predefined fractional-order multi-heterogeneous Nussbaum function; determining a virtual control rate based on an error between the estimated state variable and a desired reference state variable; determining an adaptive control input parameter according to the Nussbaum function value and the virtual control rate; The motor system is controlled based on the adaptive control input parameter.

2. The method according to claim 1, characterized in that The method further comprises: Obtain nonlinear coupling parameters, control input gain parameters and observer gain parameters of the motor system; The fractional-order state observer is constructed according to the nonlinear coupling parameter, the control input gain parameter and the observer gain parameter.

3. The method according to claim 2, characterized in that The method further comprises: The observer gain parameter is updated according to the error between the estimated state variable and the desired reference state variable to control the error to converge to zero.

4. The method according to claim 1, wherein The method further comprises: The adaptive parameter variable is updated according to the virtual control rate.

5. The method according to claim 1, wherein Determining the virtual control rate according to the error between the estimated state variable and the expected reference state variable includes: obtaining a fractional-order error derivative term based on an error between the estimated state variable and a desired reference state variable; Obtain the forward control gain parameters of the motor system; The virtual control rate is determined according to the fractional-order error derivative term and the forward control gain parameter.

6. The method according to claim 1, characterized in that The controlling the motor system based on the adaptive control input parameter comprises: The adaptive control input parameters are fuzzified using a predefined fuzzy logic system to obtain fuzzy input parameters, and the motor system is controlled based on the fuzzy input parameters.

7. The method according to claim 6, characterized in that The fuzzy processing of the adaptive control input parameters by using a predefined fuzzy logic system to obtain the fuzzy input parameters includes: The predefined fuzzy logic system is used to determine the membership degree of the adaptive control input parameter based on a Gaussian function to obtain the fuzzified input parameter.

8. An adaptive control device for a motor system, characterized in that: The device comprises: Actual output acquisition module: used to obtain the actual output of the motor system; A state variable estimation module is configured to estimate the state variables of the motor system based on the actual output using a predefined fractional-order state observer to obtain estimated state variables; Function value acquisition module: used for acquiring the adaptive parameter variables of the motor system, and determining the Nussbaum function values corresponding to the adaptive parameter variables according to a predefined fractional-order multi-heterogeneous Nussbaum function; A control rate acquisition module is configured to determine a virtual control rate based on an error between the estimated state variable and the expected reference state variable; Input parameter acquisition module: used to determine the adaptive control input parameters according to the Nussbaum function value and the virtual control rate; A control module is configured to control the motor system based on the adaptive control input parameters.

9. An electronic device, characterized in that: The electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the adaptive control method for a motor system according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the adaptive control method for a motor system according to any one of claims 1 to 7 is implemented.

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