Model control method and device based on fuzzy weighted fusion, equipment and medium

Abstract

The invention relates to the technical field of non-minimum phase systems, and relates to a model control method and device based on fuzzy weighted fusion, equipment and a medium, the method comprises the following steps: obtaining an output signal, and obtaining a first error value and a dynamic error change rate; obtaining a weight coefficient output by the fuzzy system in the current iteration round; obtaining a target reference model of the current iteration round according to the weight coefficient; obtaining a feedforward controller of the current iteration round according to the weight coefficient; and dynamically adjusting the control parameters of the PID controller according to the design parameters of the target reference model and the feed-forward output signal of the feed-forward controller. According to the double-degree-of-freedom model adopted by the invention, feedforward control and feedback control can be independently designed and optimized, the adjustability and flexibility of the double-degree-of-freedom model are improved, and different control requirements can be met; and meanwhile, through cooperation of the feedforward controller and the target reference model, an output signal of the non-minimum phase system can accurately track a reference target signal.

Description

Technical Field

[0001] This application relates to the technical field of non - minimum - phase systems. In particular, it relates to a model - based control method, device, equipment, and medium based on fuzzy weighted fusion. Background Art

[0002] A non - minimum - phase system is a type of system with special dynamic characteristics in control engineering. Its core feature is the existence of right - half - plane zeros in the transfer function. These right - half - plane zeros cause the system to exhibit some non - intuitive behaviors in the time - domain response, such as the negative - overshoot phenomenon in the initial response and a large phase lag. These characteristics make the control design of non - minimum - phase systems extremely complex, especially in scenarios that require high - precision tracking and fast response. Different from minimum - phase systems, the inverse - dynamics model of non - minimum - phase systems is usually unstable, which means that directly using a feed - forward control method based on the inverse model may cause the system to become unstable or oscillate, thus failing to achieve the desired tracking performance. In addition, the internal dynamic characteristics of non - minimum - phase systems make it difficult for traditional single - degree - of - freedom (1 - DOF) control strategies to simultaneously meet the system's stability and dynamic - performance requirements. A 1 - DOF controller usually can only adjust the system through a single feedback loop. At the same time, feedback control cannot completely eliminate the negative impact of right - half - plane zeros on the system response, making it often seem inadequate when dealing with non - minimum - phase systems. For example, when tracking a rapidly changing reference signal, a 1 - DOF controller may not be able to achieve precise tracking due to phase lag and initial reverse phenomena, and may even cause the system to become unstable. Therefore, designing a controller for non - minimum - phase systems is a challenging task. Summary of the Invention

[0003] This application provides a model - based control method, device, equipment, and medium based on fuzzy weighted fusion to solve one or more technical problems existing in the prior art, and at least provides a beneficial option or creation condition.

[0004] Other features and advantages of this application will become apparent through the following detailed description, or be learned in part through the practice of this application.

[0005] According to one aspect of the embodiments of this application, a model - based control method based on fuzzy weighted fusion is provided, which is applied to a two - degree - of - freedom model. The two - degree - of - freedom model includes at least two sub - feed - forward controllers, at least two sub - reference models, a fuzzy system, a PID controller, and a non - minimum - phase system. The method includes:

[0006] Obtain the output signal output by the non - minimum - phase system in the current iteration round;

[0007] Obtain the first error value of the preset reference target signal and the output signal in the current iteration round and the second error value in the previous iteration round, and obtain the dynamic error change rate according to the first error value and the second error value;

[0008] Input the first error value and the dynamic error change rate into the fuzzy system to obtain the weight coefficient output by the fuzzy system in the current iteration round;

[0009] Perform fuzzy weighted fusion on each of the sub-reference models according to the weight coefficient to obtain the target reference model in the current iteration round;

[0010] Perform fuzzy weighted fusion on each of the sub-feedforward controllers according to the weight coefficient to obtain the feedforward controller in the current iteration round;

[0011] Dynamically adjust the control parameters of the PID controller according to the design parameters of the target reference model and the feedforward output signal of the feedforward controller, so as to adjust the weight coefficient output by the fuzzy system in the next iteration round, so that the output signal of the non-minimum phase system tracks the reference target signal;

[0012] Wherein, the feedforward output signal is obtained by inputting the reference target signal into the feedforward controller.

[0013] In an embodiment of the present application, based on the foregoing solution, the weight coefficient includes an output weight corresponding to each of the sub-reference models, and the performing fuzzy weighted fusion on each of the sub-reference models according to the weight coefficient to obtain the target reference model in the current iteration round includes:

[0014] For each sub-reference model, multiply the design parameters of the sub-reference model by the output weight to obtain the model parameters of the sub-reference model;

[0015] Add the model parameters of each sub-reference model to obtain the initial reference model.

[0016] In an embodiment of the present application, based on the foregoing solution, after obtaining the target reference model in the current iteration round, the method further includes:

[0017] Determine the parameter update rate of the PID controller in the current iteration round according to the design parameters of the target reference model.

[0018] In an embodiment of the present application, based on the foregoing solution, the determining the parameter update rate of the PID controller in the current iteration round according to the design parameters of the target reference model includes:

[0019] Obtain the minimum-phase parameters and non-minimum-phase parameters of the non-minimum-phase system;

[0020] Determine the first output parameter of the non-minimum-phase system according to the minimum-phase parameters and the non-minimum-phase parameters;

[0021] Determine the second output parameter of the target reference model according to the design parameters of the target reference model;

[0022] Determine the first tracking error according to the first output parameter and the second output parameter;

[0023] Determine the parameter update rate according to the second tracking error corresponding to the minimum-phase parameters, the third tracking error corresponding to the non-minimum-phase parameters, and the first tracking error.

[0024] In an embodiment of the present application, based on the foregoing solution, the dynamic adjustment of the control parameters of the PID controller according to the design parameters of the target reference model and the feedforward output signal of the feedforward controller includes:

[0025] Determine the parameter update rate according to the design parameters of the target reference model;

[0026] Dynamically adjust the control parameters of the PID controller in the previous iteration round according to the parameter update rate and the feedforward output signal to obtain the control parameters of the PID controller in the current iteration round.

[0027] In an embodiment of the present application, based on the foregoing solution, the weight coefficient includes output weights corresponding to each of the sub-feedforward controllers, and each output weight corresponds to a sub-feedforward controller or a sub-reference model. The fuzzy weighted fusion of each sub-feedforward controller according to the weight coefficient to obtain the feedforward controller in the current iteration round includes:

[0028] For each sub-feedforward controller, multiply the design parameters of the sub-feedforward controller by the output weight to obtain the model parameters of the sub-feedforward controller;

[0029] Add the model parameters of each sub-feedforward controller to obtain the feedforward controller.

[0030] According to one aspect of the embodiments of the present application, there is provided a model control device based on fuzzy weighted fusion, which is applied to a two-degree-of-freedom model. The two-degree-of-freedom model includes at least two sub-feedforward controllers, at least two sub-reference models, a fuzzy system, a PID controller, and a non-minimum-phase system. The device includes:

[0031] A first acquisition unit, configured to acquire an output signal output by the non-minimum phase system in the current iteration round;

[0032] A second acquisition unit, configured to acquire a first error value between a preset reference target signal and the output signal in the current iteration round and a second error value in the previous iteration round, and obtain a dynamic error change rate according to the first error value and the second error value;

[0033] A weight output unit, configured to input the first error value and the dynamic error change rate into the fuzzy system to obtain a weight coefficient output by the fuzzy system in the current iteration round;

[0034] A reference unit, configured to perform fuzzy weighted fusion on each of the sub-reference models according to the weight coefficient to obtain a target reference model in the current iteration round;

[0035] A feedforward unit, configured to perform fuzzy weighted fusion on each of the sub-feedforward controllers according to the weight coefficient to obtain a feedforward controller in the current iteration round;

[0036] A dynamic adjustment unit, configured to dynamically adjust control parameters of the PID controller according to design parameters of the target reference model and a feedforward output signal of the feedforward controller, so as to adjust a weight coefficient output by the fuzzy system in the next iteration round, so that the output signal of the non-minimum phase system tracks the reference target signal;

[0037] Wherein, the feedforward output signal is obtained by inputting the reference target signal into the feedforward controller.

[0038] According to one aspect of the embodiments of the present application, there is provided a computer-readable storage medium, on which a computer program is stored, and the computer program includes executable instructions, and when the executable instructions are executed by a processor, the method described in the above embodiments is implemented.

[0039] According to one aspect of the embodiments of the present application, there is provided an electronic device, including: one or more processors; a memory, configured to store executable instructions of the processor, and when the executable instructions are executed by the one or more processors, the one or more processors implement the method described in the above embodiments.

[0040] Advantages of the present application: Through the framework of the two-degree-of-freedom model proposed in the present application, a feedforward controller, a target reference model, and a PID controller are designed to regulate the output signal of the non-minimum phase system. First, the continuously updated weight parameters of the fuzzy system can perform weighted fusion on each sub-feedforward controller and sub-reference model to convert and output the input reference target signal. The converted and output signal is sent to the PID controller on the one hand and used for error analysis with the non-minimum phase system on the other hand.

[0041] By performing error analysis with the non-minimum phase system, the control parameters of the PID controller can be adjusted so that the output signal of the non-minimum phase system can track the reference target signal under the adjustment of the control parameters after PID control.

[0042] In the current iteration round, the output signal of the non-minimum phase system is compared with the preset reference target signal to obtain the first error value and the dynamic error change rate. Inputting the first error value and the dynamic error change rate into the fuzzy system can obtain the updated weight coefficients, that is, the weight coefficients for the next iteration round.

[0043] Through multiple iteration rounds, the control parameters of the PID controller are iteratively updated so that the feedforward output signal output by the feedforward controller for the reference target signal can be sent to the PID controller and combined with the dynamically adjusted control parameters of the PID controller to enable the output signal of the non-minimum phase system to track the reference target signal.

[0044] There is an unknown part in the non-minimum phase system. Through the adaptive control mechanism of the present application, the control strategy can be adjusted in real time so that the two-degree-of-freedom model can continuously maintain good control effects in the face of unknown disturbances or changing environmental conditions.

[0045] Finally, the two-degree-of-freedom model adopted in the present application enables the feedforward control corresponding to the feedforward controller, the target reference model, and the feedback control corresponding to the PID controller to be independently designed and optimized, improving the adjustability and flexibility of the two-degree-of-freedom model and meeting the requirements of different controls.

[0046] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present application. Description of the Drawings

[0047] The accompanying drawings here are incorporated into the specification and form a part of this specification, showing embodiments consistent with this application, and are used together with the specification to explain the principles of this application. Obviously, the accompanying drawings in the following description are only some embodiments of this application, and for those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts. In the drawings:

[0048] Figure 1 is a flowchart of a model control method based on fuzzy weighted fusion shown according to an embodiment of this application;

[0049] Figure 2 is a logical schematic diagram of a fuzzy system shown according to an embodiment of this application;

[0050] Figure 3 is a relationship diagram of the input and output of a fuzzy system shown according to an embodiment of this application;

[0051] Figure 4 is a block diagram of a model control system based on fuzzy weighted fusion shown according to an embodiment of this application;

[0052] Figure 5 is a structural diagram of an electronic device shown according to this application. Detailed Embodiments

[0053] Example embodiments will now be described more fully with reference to the accompanying drawings. However, the example embodiments can be implemented in various forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this application will be more complete and comprehensive, and will fully convey the concept of the example embodiments to those skilled in the art.

[0054] In addition, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of the embodiments of this application. However, those skilled in the art will realize that the technical solutions of this application can be practiced without one or more of the specific details, or other methods, components, devices, steps, etc. can be adopted. In other cases, well-known methods, devices, implementations, or operations are not shown or described in detail to avoid obscuring aspects of this application.

[0055] The block diagrams shown in the accompanying drawings are only functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontrol node devices.

[0056] The flowcharts shown in the accompanying drawings are only illustrative and not necessarily inclusive of all content and operations / steps, nor are they necessarily to be executed in the order described. For example, some operations / steps can be decomposed, while some operations / steps can be combined or partially combined, so the actual execution order may change according to the actual situation.

[0057] It should be noted that: "a plurality of" as mentioned in this text refers to two or more. "And / or" describes the association relationship of associated objects and indicates that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. The character " / " generally represents an "or" relationship between the associated objects before and after.

[0058] The following provides a detailed introduction to the background technology of this application:

[0059] Non-minimum phase systems are a class of systems with special dynamic characteristics in control engineering. Their core feature is the existence of right-half plane zeros in the transfer function. These right-half plane zeros cause the system to exhibit some non-intuitive behaviors in the time-domain response, such as the negative overshoot phenomenon in the initial response and a large phase lag. These characteristics make the control design of non-minimum phase systems extremely complex, especially in scenarios that require high-precision tracking and fast response. Different from minimum phase systems, the inverse dynamics model of non-minimum phase systems is usually unstable, which means that directly using a feedforward control method based on the inverse model may cause the system to become unstable or oscillate, thus unable to achieve the desired tracking performance. In addition, the internal dynamic characteristics of non-minimum phase systems make it difficult for traditional single-degree-of-freedom (1-DOF) control strategies to simultaneously meet the system's stability and dynamic performance requirements. A 1-DOF controller usually can only adjust the system through a single feedback loop. At the same time, feedback control cannot completely eliminate the negative impact of right-half plane zeros on the system response, making it often seem inadequate when dealing with non-minimum phase systems. For example, when tracking a rapidly changing reference signal, a 1-DOF controller may be unable to achieve precise tracking due to phase lag and the initial reverse phenomenon, and may even cause the system to become unstable. Therefore, designing a controller for non-minimum phase systems is a challenging task.

[0060] To solve the above problems, a two-degree-of-freedom (2-DOF) control strategy has been proposed and has gradually become an effective tool for dealing with non-minimum phase systems. The core idea of the 2-DOF controller is to decouple the feedforward control and feedback control, enabling them to be designed and optimized independently to improve the tracking performance and anti-interference ability of the system. The feedforward controller improves the tracking performance by directly compensating for the dynamic characteristics of the system, while the feedback controller focuses on suppressing external disturbances and model uncertainties to ensure the robust stability of the system. This decoupled design enables the 2-DOF controller to effectively address the inherent challenges of non-minimum phase systems. By reasonably designing the feedforward and feedback controllers, the 2-DOF control strategy can not only achieve high-precision reference tracking but also maintain the stability of the system in the presence of external disturbances and model uncertainties.

[0061] Model reference adaptive control (MRAC) is an adaptive control method whose core idea is to adjust the controller parameters so that the output of the controlled object can track the output of a predefined reference model. Among them, the selection of the reference model is particularly crucial. It represents the desired dynamic characteristics of the system, and the adaptive mechanism is used to adjust the controller parameters in real time when the system parameters are unknown or changing to ensure that the system performance always meets the requirements of the reference model. By adaptively adjusting the PID control parameters through model reference to adapt to the dynamic changes and external disturbances of the system, the robustness and stability of the system can be improved. Different from traditional fixed-parameter PID control, MRAC can automatically optimize the control parameters according to the reference model without an accurate system model, ensuring the best performance of the system in complex or uncertain environments. In addition, MRAC can quickly respond to system load changes and environmental factors, avoiding over-regulation or under-regulation phenomena, thus enhancing the flexibility and adaptability of the control system. However, in the design of a 2-DOF controller based on MRAC for adjusting PID, there are still certain challenges in the selection of the feedforward controller and the reference model. Therefore, this application proposes a 2-DOF model reference control strategy based on fuzzy weighted fusion.

[0062] The implementation details of the technical solutions of the embodiments of this application are elaborated in detail below:

[0063] According to one aspect of this application, a model control method based on fuzzy weighted fusion is provided. Figure 1 FIG. is a flowchart of the model control method based on fuzzy weighted fusion shown in the embodiments of this application. The model control method based on fuzzy weighted fusion at least includes steps S1 to S6, which are introduced in detail as follows:

[0064] In step S1, the output signal output by the non-minimum phase system in the previous iteration round is obtained.

[0065] First, in the first round, the respective sub-reference models are subjected to fuzzy weighted fusion according to preset weight coefficients to obtain an initial reference model. At this time, a preset value can be given to the weight coefficients output by the fuzzy system, and in subsequent multiple rounds of iteration and update, the weight coefficients of the fuzzy system can be made more accurate, and then after acting on the feedforward controller and the reference model, the regulation of the output signal of the non-minimum phase system is also more accurate.

[0066] First, it should be noted that the number of sub-reference models and sub-feedforward models in the embodiments of the present application is set to two, but the present application does not limit the number of sub-reference models and sub-feedforward models, and two is only an exemplary number.

[0067] In an embodiment of the present application, the weight coefficients include output weights corresponding one by one to the respective sub-reference models, and the weight coefficients perform fuzzy weighted fusion on the respective sub-reference models to obtain a target reference model for the current iteration round, including:

[0068] For each sub-reference model, multiply the design parameters of the sub-reference model by the output weight to obtain the model parameters of the sub-reference model;

[0069] Add the model parameters of the respective sub-reference models to obtain the initial reference model.

[0070] Specifically, the reference model based on fuzzy weighted fusion can be designed as:

[0071] Y m = w1Y m1 + w2Y m2 +…+ w n Y mn

[0072] wherein, w1, w2,..., w n are output weights based on the output of the fuzzy system, and the set of w1, w2,..., w n is the weight coefficient based on the output of the fuzzy system, w1 + w2 + … + w n = 1; Y m1 , Y m2 ,…, Y mn are sub-reference models respectively; Y m is the initial reference model obtained after fuzzy weighted fusion. It should be noted that in the next round, Y m is updated to the target reference model obtained after fuzzy weighted fusion. In different rounds of feedback regulation, Y m has different representative meanings.

[0073] For a non-minimum phase system with a single right zero and time-delay characteristics, the sub-reference model can be designed as follows:

[0074]

[0075] where Y m1 and Y m2 represent two sub-reference models respectively. c m1 , d m1 , p m1 , c m2 , d m2 , p m2 are all positive constants and are parameters in the sub-reference model. Different parameters in the reference model will result in different adjustments to the PID controller parameters. s is the Laplace variable. Then c m1 , d m1 , p m1 , c m2 , d m2 , p m2 and s constitute the design parameters of the sub-reference model.

[0076] In step S2, obtain the first error value of the preset reference target signal and the output signal in the current iteration round and the second error value in the previous iteration round, and obtain the dynamic error change rate according to the first error value and the second error value.

[0077] For the significant differences in the system dynamic characteristics presented by the feedforward controller and the reference model under different parameter configurations, this application innovatively introduces the fuzzy control theory. By designing an adaptive weighting mechanism, the dynamic fusion of the feedforward controller and the reference model under different working conditions is achieved. Specifically, according to the real-time system state and performance indicators (related to the reference target signal), the method dynamically adjusts the weight coefficients of each sub-feedforward controller and sub-reference model (i.e., w1, w2,..., w n ) through fuzzy inference rules, thereby optimizing the overall control performance of the fuzzy system and ensuring stable and accurate control effects under different operating conditions. It should be noted that the output signal (obtained in the previous iteration round) from the non-minimum phase system and the target value (reference target signal) will be fed into the fuzzy system for operation, that is, in the step of obtaining the first error value and the dynamic error change rate, the weight coefficients of the current round are output by the fuzzy system.

[0078] Specifically, the inputs of the fuzzy system are the error e (i.e., the first error value) and the error derivative change rate e c (i.e., the dynamic error change rate), and the universes of discourse are both [-1, 1]; the output is the weight coefficient w (i.e., w1, w2,..., w n ), which can be asFigure 3 As shown Figure 3 is a relationship diagram between the input and output of the fuzzy system, and the universe of discourse is [0,1]. Their fuzzy subsets are defined as follows:

[0079] e = {NB, NM, Z, PM, PB}

[0080] e c = {NB, NM, Z, PM, PB}

[0081] w = {NB, Z, PB}

[0082] where NB, NM, Z, PM, and PB represent the levels of extremely large, large, medium, small, and extremely small respectively.

[0083] In terms of the membership function, the Gaussian membership function is adopted for the input variable, and the triangular membership function is adopted for the output variable.

[0084] In step S3, the first error value and the dynamic error change rate are input into the fuzzy system to obtain the weight coefficient output by the fuzzy system in the current iteration round.

[0085] For the reference model of the model reference controller, the selection of parameters c mi , d mi , p mi (i = 1, 2) is particularly crucial, which determines the response speed and smoothness of the PID controller. The design of the control strategy for the non-minimum phase system needs to be based on stability. Therefore, the starting point of the fuzzy control design idea is as follows: 1) When the reference value undergoes a step change, that is, when the error between the reference value and the true value is large, a reference model with a larger damping is adopted to ensure the stability of the system. 2) When the error between the reference value and the true state value is small, a reference model with a smaller damping is adopted to improve the response speed of the system on the premise of ensuring stability.

[0086] In step S4, according to the weight coefficient, fuzzy weighted fusion is performed on each of the sub-feedforward controllers to obtain the feedforward controller in the current iteration round.

[0087] Specifically, the weight coefficient includes output weights corresponding to each of the sub-feedforward controllers, and each output weight corresponds to a sub-feedforward controller or a sub-reference model. The fuzzy weighted fusion of each of the sub-feedforward controllers according to the weight coefficient to obtain the feedforward controller in the current iteration round includes:

[0088] For each of the sub-feedforward controllers, multiply the design parameters of the sub-feedforward controller by the output weight to obtain the model parameters of the sub-feedforward controller;

[0089] Add the model parameters of each of the sub - feedforward controllers to obtain the feedforward controller.

[0090] For the feedforward controller, the selection of parameters a fi , b fi (i = 1, 2) is particularly crucial, which determines the response speed and smoothness of the feedforward controller. Due to the existence of right - hand zeros in the non - minimum - phase system, when the reference value undergoes a sudden change, it will cause a negative - regulation phenomenon in the system, and this phenomenon usually occurs when the tracking error is large. Therefore, the design idea of fuzzy control is as follows: 1) When the reference value undergoes a step change, that is, when the error between the reference value and the true state value is large, a relatively smooth feedforward controller is adopted to minimize the negative - regulation effect to ensure the stability of the system. 2) When the error between the reference value and the true state value is small, a feedforward controller with a faster response speed is adopted to improve the response speed of the system.

[0091] Therefore, the feedforward controller based on fuzzy - weighted fusion is designed as:

[0092] G fc = w1G fc1 + w2G fc2 +…+ w n G fcn

[0093] where w1, w2,..., w n are the output weights based on the output of the fuzzy system, and the set of w1, w2,..., w n represents the weight coefficients, and w1 + w2 +…+ w n = 1; G fc1 , G fc2 ,…, G fcn are sub - feedforward controllers respectively; G fc is the feedforward controller obtained after weighted fusion.

[0094] The feedforward controller can improve the tracking performance by directly compensating the dynamic characteristics of the system. For a non - minimum - phase system with a single right - hand zero and time - delay characteristics, the sub - feedforward controllers can be designed as follows:

[0095]

[0096] where G fc1 and G fc2 represent two sub - feedforward controllers respectively. a f1 , a f2 , b f1 , b f2are all positive constants and are parameters in the sub-feedforward controller. Different parameters in the sub-feedforward controller will result in different dynamic responses of the system. s is the Laplace variable.

[0097] In an embodiment of the present application, after obtaining the target reference model of the current iteration round, the method further includes:

[0098] Determine the parameter update rate of the PID controller in the current iteration round according to the design parameters of the target reference model.

[0099] Specifically, determining the parameter update rate of the PID controller in the current iteration round according to the design parameters of the target reference model includes:

[0100] Obtain the minimum-phase parameter and non-minimum-phase parameter of the non-minimum-phase system;

[0101] Determine the first output parameter of the non-minimum-phase system according to the minimum-phase parameter and the non-minimum-phase parameter;

[0102] Determine the second output parameter of the target reference model according to the design parameters of the target reference model;

[0103] Determine the first tracking error according to the first output parameter and the second output parameter;

[0104] Determine the parameter update rate according to the second tracking error corresponding to the minimum-phase parameter, the third tracking error corresponding to the non-minimum-phase parameter, and the first tracking error.

[0105] Dynamically adjust the control parameters of the PID controller according to the design parameters of the target reference model and the feedforward output signal of the feedforward controller, including:

[0106] Determine the parameter update rate according to the design parameters of the target reference model;

[0107] Dynamically adjust the control parameters of the PID controller in the previous iteration round according to the parameter update rate and the feedforward output signal to obtain the control parameters of the PID controller in the current iteration round.

[0108] Specifically, the transfer function expression of a non-minimum-phase system is as follows:

[0109]

[0110] Among them, a1, a2, and K are all constants greater than 0. b1 is a constant less than 0. τ is the time-delay coefficient.

[0111] Decompose the non-minimum-phase system G(s) into a minimum-phase system Gk (s) and the non - minimum - phase part G u (s), are expressed as follows:

[0112] G(s) = G k (s) + G u (s)

[0113] In the formula, the minimum - phase system can be expressed as G k (s) = b / (s 2 + a1s + a2), where b > 0 is a constant.

[0114] Then, the first output parameter y of the non - minimum - phase system p can be equivalent to:

[0115] y p = G(s)U(s)

[0116] = [G k (s) + G u (s)]U(s)

[0117] = y k + y u

[0118] where U(s) represents the input of the non - minimum - phase system. y k and y u are the outputs corresponding to the minimum - phase parameter G k (s) and the non - minimum - phase parameter G u (s) respectively.

[0119] Set the reference - signal transfer function as U c (s). Taking the PID controller as the controller, with y k as the output and the controlled system as G k (s), then the closed - loop control system can be expressed as

[0120]

[0121] where Y k (s) is the Laplace transform of the output y k . K p , K i , K d are the proportional, integral, and derivative parameters of the PID controller respectively.

[0122] Define the reference model Y m (s) as:

[0123]

[0124] where a m1 , am2 , a m3 > 0 are all parameters in the reference model or design parameters.

[0125] Define the first output parameter y p and the first tracking error ε of the second output parameter y m of the reference model is

[0126] ε = y p - y m

[0127] where y m is the inverse Laplace transform of the reference model output Y(s).

[0128] According to y p = y k + y u , it can be further obtained that:

[0129] ε = y k - y m + y u

[0130] = ε k + ε u

[0131] where ε k = y k - y m and ε u = y u represent two error terms, ε k = y k - y m is the second tracking error, and ε u = y u is the third tracking error.

[0132] Define the loss function:

[0133]

[0134] Apply the gradient descent method to obtain the target PID control parameters K p , K i , K d , and the update rate is designed as:

[0135]

[0136] where is the Jacobian matrix, which characterizes the sensitivity of the output of the fuzzy system to the control input. D = d / dt represents the differential operator.

[0137] According to the closed-loop transfer function Y k(S) It can be obtained that:

[0138]

[0139]

[0140] Finally, the update rate can be obtained:

[0141]

[0142] where γ p , γ i , γ d are update rate parameters.

[0143] Furthermore, the dynamically changing target PID control parameters can be expressed as:

[0144]

[0145] where K p (0), K i (0), K d (0) are the initial PID control parameters, i.e., the initial proportional, integral, and derivative parameters.

[0146] Specifically, the preset reference target signal is the target value shown in Figure 2 , that is, a reference target signal (the target value in Figure 2 ) is given, such as a step signal. By adjusting the target PID control parameters of the PID controller, the output of the non-minimum phase system can stably track the reference target signal. Among them, the process of adjusting the target PID control parameters mainly uses a feedforward controller and a PID controller based on model reference adjustment. Here, a reference model composed of multiple sub-reference models is used to adjust the PID controller. When designing the parameter update rate of the PID controller based on model reference adjustment, the gradient descent method is adopted. The design of the update rate is obtained based on the above steps and involves Figure 2 the three errors ε, ε k , ε u (i.e., the first tracking error, the second tracking error, and the third tracking error described in this application). The above three errors are mainly reflected in this process. After obtaining the update rate, the control parameters of the PID controller updated in the current iteration round are further obtained; finally, according to the dynamic characteristics of the fuzzy system for the target value (reference target signal) in different application environments, the fuzzy weighted fusion mechanism is used to achieve the weighted fusion of multiple sub-feedforward controllers to obtain the final feedforward controller; at the same time, the fuzzy weighted fusion mechanism is used to perform weighted fusion on multiple sub-reference models to obtain the final target reference model, and then the control parameters of the PID controller are adjusted.

[0147] Finally, according to the finally obtained Gfc and Y m and cyclically and dynamically adjusts the control parameters of the PID controller, so as to implement the tracking of the output signal of the non-minimum phase system to the reference target signal.

[0148] In summary, the present application proposes a two-degree-of-freedom (2-DOF) model control strategy based on fuzzy weighted fusion. First, based on the design framework of the 2-DOF two-degree-of-freedom model, a feed-forward controller and a PID controller based on model reference adjustment are respectively constructed. Subsequently, according to the dynamic characteristics of the system under different error conditions, fuzzy weighted fusion is used to realize the weighted fusion of multiple sub-feed-forward controllers, thereby significantly improving the dynamic response performance of the system. Further, the same fuzzy weighted fusion principle is used to perform weighted fusion on multiple sub-reference models to enhance the parameter adjustment ability of the PID controller. Finally, through the collaborative weighted fusion of the feed-forward controller and the target reference model, stable and efficient control of the non-minimum phase system is achieved. This method improves the control performance of the fuzzy system while ensuring the robustness and adaptability of the fuzzy system.

[0149] According to one aspect of the embodiments of the present application, a model control device 300 based on fuzzy weighted fusion is proposed. Figure 4 is a schematic diagram of the model control device 300 based on fuzzy weighted fusion proposed in the embodiments of the present application. The system 300 includes: a first acquisition unit 301, a second acquisition unit 302, a weight output unit 303, a reference unit 304, a feed-forward unit 305, and a dynamic adjustment unit 306.

[0150] The first acquisition unit 301 is configured to acquire the output signal output by the non-minimum phase system in the current iteration round.

[0151] The second acquisition unit 302 is configured to acquire a first error value between a preset reference target signal and the output signal in the current iteration round and a second error value in the previous iteration round, and obtain a dynamic error change rate according to the first error value and the second error value.

[0152] The weight output unit 303 is configured to input the first error value and the dynamic error change rate into the fuzzy system to obtain the weight coefficient output by the fuzzy system in the current iteration round.

[0153] The reference unit 304 is configured to perform fuzzy weighted fusion on each of the sub-reference models according to the weight coefficient to obtain a target reference model in the current iteration round.

[0154] The feed-forward unit 305 is configured to perform fuzzy weighted fusion on each of the sub-feed-forward controllers according to the weight coefficient to obtain a feed-forward controller in the current iteration round.

[0155] A dynamic adjustment unit 306 is configured to dynamically adjust the control parameters of the PID controller according to the design parameters of the target reference model and the feedforward output signal of the feedforward controller, so as to adjust the weight coefficients output by the fuzzy system in the next iteration round, so that the output signal of the non-minimum phase system tracks the reference target signal;

[0156] Wherein, the feedforward output signal is obtained by inputting the reference target signal into the feedforward controller.

[0157] As another aspect, the present application also provides a computer-readable storage medium, on which a program product capable of implementing the method provided above in this specification is stored. In some possible implementation manners, various aspects of the present application can also be implemented in the form of a program product, which includes program code. When the program product runs on a terminal device, the program code is used to cause the terminal device to execute the steps according to various exemplary implementation manners of the present application described in the "Embodiment Method" section above in this specification.

[0158] The program product for implementing the above method according to the embodiments of the present application may adopt a portable compact disc read-only memory (CD-ROM) and include program code, and may run on a terminal device, such as a personal computer. However, the program product of the present application is not limited thereto. In this document, the readable storage medium may be any tangible medium that contains or stores a program, and the program may be used by or in combination with an instruction execution system, apparatus, or device.

[0159] The program product may adopt any combination of one or more readable media. The readable media may be a readable signal medium or a readable storage medium. The readable storage medium may, for example, but not be limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0160] A computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries readable program code. Such a propagated data signal may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing. The readable signal medium may also be any readable medium other than a readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device.

[0161] The program code contained on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0162] The program code for performing the operations of this application may be written in any combination of one or more programming languages, including object-oriented programming languages such as Java, C++, etc., and also including conventional procedural programming languages such as the "C" language or similar programming languages. The program code may be executed entirely on the user's computing device, partially on the user's device, executed as a stand-alone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user's computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., by connecting through the Internet using an Internet service provider).

[0163] The following refers to Figure 5 to describe the electronic device 400 according to such an embodiment of the present application. Figure 5 The illustrated electronic device 400 is merely an example and should not impose any limitation on the functions and scope of use of the embodiments of the present application.

[0164] As Figure 5 shown, the electronic device 400 is presented in the form of a general-purpose computing device. The components of the electronic device 400 may include but are not limited to: at least one of the above-mentioned processing units 410, at least one of the above-mentioned storage units 420, and a bus 430 connecting different system components (including the storage unit 420 and the processing unit 410).

[0165] Among them, the storage unit stores program code, which can be executed by the processing unit 410, so that the processing unit 410 executes the steps according to various exemplary embodiments of the present application described in the "Embodiment Method" section of the present specification above.

[0166] The storage unit 420 may include a readable medium in the form of a volatile storage unit, such as a random access storage unit (RAM) 421 and / or a cache storage unit 422, and may further include a read-only storage unit (ROM) 423.

[0167] The storage unit 420 may also include a program / utilities 424 having a set (at least one) of program modules 425. Such program modules 425 include, but are not limited to: an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment.

[0168] The bus 430 may represent one or more of several types of bus structures, including a storage unit bus or storage unit control node, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the various bus structures.

[0169] The electronic device 400 may also communicate with one or more external devices 1200 (such as a keyboard, a pointing device, a Bluetooth device, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 400, and / or may communicate with any device that enables the electronic device 400 to communicate with one or more other computing devices (such as a router, a modem, etc.). Such communication may be through an input / output (I / O) interface 450. And, the electronic device 400 may also communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through a network adapter 460. As shown in the figure, the network adapter 460 communicates with other modules of the electronic device 400 through the bus 430. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 400, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0170] Through the description of the above embodiments, those skilled in the art can easily understand that the example embodiments described herein can be implemented by software, or can be implemented by a combination of software and necessary hardware. Therefore, the technical solutions according to the embodiments of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiments of the present application.

[0171] In addition, the above drawings are only schematic illustrations of the processes included in the method according to the exemplary embodiments of the present application, rather than for limiting purposes. It is easily understood that the processes shown in the above drawings do not indicate or limit the chronological order of these processes. Additionally, it is also easily understood that these processes may be executed, for example, synchronously or asynchronously in multiple modules.

[0172] It should be understood that the present application is not limited to the exact structures that have been described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present application is only limited by the appended claims.

Claims

1. A model control method based on fuzzy weighted fusion, characterized in that Applied to a two-degree-of-freedom model, the two-degree-of-freedom model includes at least two sub-feedforward controllers, at least two sub-reference models, a fuzzy system, a PID controller, and a non-minimum phase system. The method includes: Obtain the output signal output by the non-minimum phase system in the previous iteration round; Obtain a first error value between a preset reference target signal and the output signal in the current iteration round and a second error value in the previous iteration round, and obtain a dynamic error change rate according to the first error value and the second error value; Input the first error value and the dynamic error change rate into the fuzzy system to obtain the weight coefficient output by the fuzzy system in the current iteration round; Perform fuzzy weighted fusion on each of the sub-reference models according to the weight coefficient to obtain the target reference model in the current iteration round; Perform fuzzy weighted fusion on each of the sub-feedforward controllers according to the weight coefficient to obtain the feedforward controller in the current iteration round; Dynamically adjust the control parameters of the PID controller according to the design parameters of the target reference model and the feedforward output signal of the feedforward controller, so as to adjust the weight coefficient output by the fuzzy system in the next iteration round, so that the output signal of the non-minimum phase system tracks the reference target signal; Wherein, the feedforward output signal is obtained by inputting the reference target signal into the feedforward controller.

2. The model control method based on fuzzy weighted fusion according to claim 1, wherein The weight coefficient includes output weights corresponding to each of the sub-reference models one by one. The performing fuzzy weighted fusion on each of the sub-reference models according to the weight coefficient to obtain the target reference model in the current iteration round includes: For each of the sub-reference models, multiply the design parameters of the sub-reference model by the output weight to obtain the model parameters of the sub-reference model; Add the model parameters of each of the sub-reference models to obtain the initial reference model.

3. The model control method based on fuzzy weighted fusion according to claim 2, wherein After obtaining the target reference model in the current iteration round, the method further includes: Determine the parameter update rate of the PID controller in the current iteration round according to the design parameters of the target reference model.

4. The model control method based on fuzzy weighted fusion according to claim 3, wherein The determining the parameter update rate of the PID controller in the current iteration round according to the design parameters of the target reference model includes: Obtain the minimum phase parameter and the non-minimum phase parameter of the non-minimum phase system; Determine a first output parameter of the non-minimum phase system according to the minimum phase parameter and the non-minimum phase parameter; Determine a second output parameter of the target reference model according to the design parameters of the target reference model; Determine a first tracking error according to the first output parameter and the second output parameter; Determine the parameter update rate according to the second tracking error corresponding to the minimum phase parameter, the third tracking error corresponding to the non-minimum phase parameter, and the first tracking error.

5. The model control method based on fuzzy weighted fusion according to claim 4, wherein The dynamically adjusting the control parameters of the PID controller according to the design parameters of the target reference model and the feedforward output signal of the feedforward controller includes: Determine the parameter update rate according to the design parameters of the target reference model; Dynamically adjust the control parameters of the PID controller in the previous iteration round according to the parameter update rate and the feedforward output signal, so as to obtain the control parameters of the PID controller in the current iteration round.

6. The model control method based on fuzzy weighted fusion according to claim 5, wherein The weight coefficients include output weights corresponding to each of the sub-feedforward controllers, and each output weight corresponds to one of the sub-feedforward controllers or one of the sub-reference models. The fuzzy weighted fusion of each of the sub-feedforward controllers according to the weight coefficients to obtain the feedforward controller in the current iteration round includes: For each of the sub-feedforward controllers, multiply the design parameters of the sub-feedforward controller by the output weight to obtain the model parameters of the sub-feedforward controller; Add the model parameters of each of the sub-feedforward controllers to obtain the feedforward controller.

7. A model control device based on fuzzy weighted fusion, characterized in that, Applied to a two-degree-of-freedom model, the two-degree-of-freedom model includes at least two sub-feedforward controllers, at least two sub-reference models, a fuzzy system, a PID controller, and a non-minimum phase system. The device includes: A first acquisition unit for acquiring the output signal output by the non-minimum phase system in the current iteration round; A second acquisition unit for acquiring the first error value between the preset reference target signal and the output signal in the current iteration round and the second error value in the previous iteration round, and obtaining a dynamic error change rate according to the first error value and the second error value; A weight output unit for inputting the first error value and the dynamic error change rate into the fuzzy system to obtain the weight coefficients output by the fuzzy system in the current iteration round; A reference unit for performing fuzzy weighted fusion on each of the sub-reference models according to the weight coefficients to obtain the target reference model in the current iteration round; A feedforward unit for performing fuzzy weighted fusion on each of the sub-feedforward controllers according to the weight coefficients to obtain the feedforward controller in the current iteration round; A dynamic adjustment unit for dynamically adjusting the control parameters of the PID controller according to the design parameters of the target reference model and the feedforward output signal of the feedforward controller, so as to adjust the weight coefficients output by the fuzzy system in the next iteration round, so that the output signal of the non-minimum phase system tracks the reference target signal; Wherein, the feedforward output signal is obtained by inputting the reference target signal into the feedforward controller.

8. An electronic device, characterized in that, The electronic device includes a memory and a processor, the memory stores a computer program, and when the processor executes the computer program, it implements the model control method based on fuzzy weighted fusion according to any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the model control method based on fuzzy weighted fusion according to any one of claims 1 to 7.

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