CN115562015A - Fuzzy self-adaptive iterative learning control method and device for continuous reaction kettle - Google Patents
Fuzzy self-adaptive iterative learning control method and device for continuous reaction kettle Download PDFInfo
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Abstract
The application belongs to the technical field of industrial process control and discloses a fuzzy self-adaptive iterative learning control method and device for a continuous reaction kettle, wherein the method comprises the following steps: establishing a CSTR system state space model; acquiring the state information of the current running batch of the CSTR system; constructing a control signal equation of the fuzzy self-adaptive iterative learning controller to obtain a control signal of the current running batch; correcting the control signal and inputting the control signal into a system actuator to obtain an output signal of the system actuator, and acting the output signal on the CSTR reaction system to obtain an actual output result of the CSTR reaction system; obtaining a tracking error according to an actual output result and a target output result, and updating a control signal of the next operation batch based on parameters and adaptive items of a fuzzy adaptive iterative learning controller control signal equation corrected by the tracking error; and after repeated iterative updating, stopping iterative learning until the preset control precision is reached. The anti-interference capability of the reaction kettle system is improved, and the control precision is improved.
Description
Technical Field
The application relates to the technical field of industrial process control, in particular to a fuzzy self-adaptive iterative learning control method and device for a continuous reaction kettle.
Background
Continuous Stirred Tank Reactors (CSTRs) play an important role in modern industrial production, and Continuous Stirred Tank reactors are used in most industries, such as chemical industry, pharmaceutical industry, and production and manufacturing of biofuels. However, due to the problems of excessive temperature and excessive pressure, machine abrasion, component aging and the like during reaction, actuator faults of the control system are caused, which is the most common phenomenon in the industry and has high harmfulness, so that the control performance of the system is reduced, and even production safety accidents occur, and great loss is caused. Because the continuous reaction kettle is a system with repeated motion and has repeatability, each batch of products can be processed and reacted in the same way after coming in, but because of different environments and different products, the initial conditions of the reaction are different, and further, the ideal reactant concentration curve is different. These adverse conditions prevent the reactants from reacting under ideal conditions, resulting in poor reaction productivity and accuracy, which affect product quality. The PID control used at present is in a complex nonlinear CSTR reaction system, when the CSTR reaction system is interfered by the outside, the prior art can not cope with the interference, so that the system is easy to deviate from the set working point, corresponding measures can not be taken by self, and the control precision is low.
Disclosure of Invention
Therefore, the embodiment of the application provides a fuzzy self-adaptive iterative learning control method and device for continuous reaction kettles, so that the anti-interference capability of a reaction kettle system is improved, and the control precision is improved.
In a first aspect, the application provides a fuzzy adaptive iterative learning control method for a continuous reaction kettle.
The application is realized by the following technical scheme:
a method of fuzzy adaptive iterative learning control of a continuous reactor, the method comprising:
s10: establishing a state space model of a continuous reaction kettle system, and acquiring state information of a current operation batch of the continuous reaction kettle system based on the state space model;
s20: adding a self-adaptive item on the basis of a fuzzy controller to construct a control signal equation of the fuzzy self-adaptive iterative learning controller;
s30: obtaining a control signal of the current running batch through a control signal equation of the fuzzy self-adaptive iterative learning controller based on the state information of the current running batch and the tracking error of the last running batch;
s40: inputting the control signal into a system actuator, correcting the control signal according to the fault of the system actuator to obtain an output signal of the system actuator, and acting the output signal on the continuous reaction kettle system to obtain an actual output result of the continuous reaction kettle system;
s50: obtaining a tracking error of the continuous reaction kettle system according to an actual output result and a target output result of the continuous reaction kettle system, correcting parameters and adaptive items of a control signal equation of the fuzzy adaptive iterative learning controller based on the tracking error, and updating a control signal of the next operation batch;
s60: and (6) repeatedly executing S30 to S50, carrying out repeated iteration updating until the preset control precision is reached, stopping iterative learning, and outputting a final control signal.
In a preferred example of the present application, the step of correcting the control signal according to the fault of the system actuator based on the fault of the system actuator to obtain the output signal of the system actuator may further include:
correcting the control signal based on multiplicative fault and additive fault of a system actuator: u (t, k) = U (τ (t, k), t) = M (t) τ (t, k) + N (t),
where M (t) represents a multiplicative fault, N (t) represents an additive fault, τ (t, k) represents a control signal for a system actuator, and u (t, k) represents an output signal for a system actuator.
In a preferred example of the present application, it may be further configured that the multiplicative fault specifically includes:
M(t)＝0.2sint+0.8。
in a preferred example of the present application, it may be further configured that the additive fault specifically is:
N(t)＝0.2sint。
in a preferred example of the present application, it may be further configured that, by adding an adaptive term on the basis of the fuzzy controller, the step of constructing the control signal equation of the fuzzy adaptive iterative learning controller is:
and obtaining an estimated value of a fuzzy parameter vector according to the state information and a target output result through a fuzzy rule, multiplying the estimated value of the fuzzy parameter vector by a fuzzy basis function vector, and adding a self-adaptive item related to the fault of a system actuator to obtain a control signal equation of the fuzzy self-adaptive iterative learning controller.
In a preferred example of the present application, it may be further provided that the adaptive term related to the system actuator failure
The expression of (c) is specifically:
wherein, theta represents a design parameter,
the adaptive term representing the kth batch at time t,
the adaptive term of the k-1 th batch at the time t is represented, v (t, k) represents a discrete sequence, G (v (t, k)) represents a discrete Nussbaum gain, k (t, k) represents a switching signal, B (t, k) represents a system enhancement auxiliary signal, and A (t, k) represents an enhancement error.
In a preferred example of the present application, it may be further configured that the fuzzy adaptive iterative learning controller comprises:
wherein τ (t, k) represents the control signal for the kth run batch at time t,
is an adaptive term related to system actuator failure,
an estimate value representing a vector of fuzzy parameters,
representing a fuzzy basis function vector.
In a preferred example of the present application, the step of correcting the parameters of the fuzzy adaptive iterative learning controller and the adaptive terms thereof based on the tracking error further includes:
obtaining an enhancement error based on the tracking error, comparing the absolute value of the enhancement error with a preset threshold, and correcting the fuzzy self-adaptive iterative learning controller parameter and the self-adaptive item thereof when the absolute value of the enhancement error is greater than or equal to the preset threshold;
and when the absolute value of the enhancement error is smaller than a preset threshold value, stopping correcting the fuzzy self-adaptive iterative learning controller parameters and the self-adaptive items thereof.
In a second aspect, the present application provides a fuzzy adaptive iterative learning control apparatus for a continuous reactor.
The application is realized by the following technical scheme:
a fuzzy adaptive iterative learning control apparatus of a continuous reaction kettle, the apparatus comprising:
the model building module is used for building a state space model of the continuous reaction kettle system and acquiring the state information of the current running batch of the continuous reaction kettle system based on the state space model;
the controller module is used for adding a self-adaptive item on the basis of the fuzzy controller, constructing a control signal equation of the fuzzy self-adaptive iterative learning controller, and obtaining a control signal of the current running batch through the control signal equation of the fuzzy self-adaptive iterative learning controller on the basis of the state information of the current running batch and the tracking error of the previous running batch;
the actuator module is used for inputting the control signal into a system actuator, correcting the control signal according to the fault of the system actuator to obtain an output signal of the system actuator, and acting the output signal on the continuous reaction kettle system to obtain an actual output result of the continuous reaction kettle system;
the updating module is used for obtaining the tracking error of the continuous reaction kettle system according to the actual output result and the target output result of the continuous reaction kettle system, correcting the parameters and the adaptive items of the control signal equation of the fuzzy adaptive iterative learning controller based on the tracking error and updating the control signal of the next operation batch; and after repeated iterative updating, stopping iterative learning until the preset control precision is reached.
In a third aspect, the present application provides a computer-readable storage medium.
The application is realized by the following technical scheme:
a computer-readable storage medium storing a computer program which, when executed by a processor, implements any one of the above-described methods of fuzzy adaptive iterative learning control for continuous reactors.
In summary, compared with the prior art, the beneficial effects brought by the technical scheme provided by the embodiment of the present application at least include:
the method comprises the following steps of (1) obtaining a target control signal by using a state space model of a continuous reaction kettle system as a research model according to a target change curve of the concentration of a reactant in the system, and gradually estimating the target control signal by using a fuzzy logic system; designing an additional adaptive term to compensate the actuator fault, and correcting the influence caused by the actuator fault through a control error; the tracking error of the system is utilized to optimize the actual output result of the system of the current operation batch, the anti-interference capability of the continuous reaction kettle system in the operation process is effectively improved, the tracking control of the concentration of the system reactant can be still realized when the system actuator fails, the control precision can be improved, the system loss and the corresponding time are reduced, and the stability of the system is improved.
Drawings
FIG. 1 is a schematic flow diagram of a continuous reactor control method provided in an exemplary embodiment of the present application;
FIG. 2 is a schematic diagram of controlling tracking error provided by an exemplary embodiment of the present application;
FIG. 3 is a schematic flow chart illustrating target output results and actual output results for different operation batches according to an exemplary embodiment of the present application;
FIG. 4 is a schematic diagram of actuator control signals and output signals at different iterations provided by an exemplary embodiment of the present application.
Detailed Description
The present embodiment is only for explaining the present application, and it is not limited to the present application, and those skilled in the art can make modifications of the present embodiment without inventive contribution as needed after reading the present specification, but all of them are protected by patent law within the scope of the claims of the present application.
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
In addition, the term "and/or" in the present application is only one kind of association relationship describing the associated object, and means that three kinds of relationships may exist, for example, a and/or B may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" in this application generally indicates that the preceding and following related objects are in an "or" relationship, unless otherwise specified.
In this application, the terms "first," "second," and the like are used for distinguishing identical or similar items with substantially identical functions and functionalities, and it should be understood that "first," "second," and "n" have no logical or temporal dependency, and no limitation on the number or execution order.
The embodiments of the present application will be described in further detail with reference to the drawings.
In one embodiment of the present application, a fuzzy adaptive iterative learning control method for continuous reaction kettle is provided, as shown in fig. 1, the main steps are described as follows:
s10: establishing a state space model of the continuous reaction kettle system, and acquiring the state information of the current running batch of the continuous reaction kettle system based on the state space model.
Specifically, a mechanism model of the continuous reaction kettle system is obtained by analyzing the dynamic characteristics of the continuous reaction kettle in industrial production based on a reactant material balance formula and an energy conservation law, the heat release process of chemical reaction is simulated, a mathematical model of the continuous reaction kettle system is obtained based on the mechanism model for convenience of analysis and treatment, and a state space model of the continuous reaction kettle system is established.
The state space model of the discrete-time continuous reaction kettle system specifically comprises the following steps:
wherein x is 1 (t, k) represents the reactant concentration of the kth run at time t, x 2 (t, k) represents the reactant temperature of the k-th operation batch at the t moment, y (t, k) represents the actual output result of the system and represents the actual concentration of the reactant of the k-th operation batch at the t moment, d (t, k) represents an external interference item of the system and is generated by objective environment and manual operation, u (t, k) is the output signal of the system actuator, and a table showsTemperature of coolant, beta 1 、β 2 、β 3 And C α Representing a system parameter.
Selecting the appropriate x of the system 1 (t, k) and x 2 (t, k) as initial state information, according to the actual acquisition of beta 1 、β 2 、β 3 And C α And expressing the system parameter value, adding an external interference item d (t, k), and setting the temperature u (t, k) of the coolant, so that the state information of the current running batch and the actual output result y (t, k) of the system after reaction can be obtained.
In this embodiment, the parameters of the state space model are set as follows: beta is a 1 ＝28.5，β 2 ＝21.5，β 3 ＝25.2，C α =0.036, and system external interference term d (t, k) =0.01cos (0.05 t) cos (x) 1 (t, k)). When i =1,2, the initial state of the system is set to x i (0,k+1)＝x i (100,k), (k =0,1,2 …) where x i (0,0) =0.1. According to the production requirements, different products have different ideal reactant concentration curves, which are expressed as reference tracks y varying with running batches d (t, k) = (0.05 +0.05m (k)) sin (0.05 π t) +0.5, where the random factor m (k) is e [0,1]。
S20: and adding a self-adaptive item on the basis of the fuzzy controller to construct a fuzzy self-adaptive iterative learning controller.
Selecting a proper membership function, and designing a fuzzy controller by using a fuzzy rule system, wherein the membership function is as follows:
the input vector of the Fuzzy Logic System (FLS) is:
wherein z is i (t, k), (i =1,2,3) are the corresponding membership functions:
and adding a self-adaptive item on the basis of the fuzzy controller to obtain a control signal equation of the fuzzy self-adaptive iterative learning controller, specifically, obtaining an estimated value of a fuzzy parameter vector according to the state information and a target output result through a fuzzy rule, multiplying the estimated value of the fuzzy parameter vector by a fuzzy basis function vector, and adding the self-adaptive item related to the system actuator fault to obtain the control signal equation of the fuzzy self-adaptive iterative learning controller.
The expression of the control signal equation of the fuzzy adaptive iterative learning controller is as follows:
wherein tau (t, k) represents a control signal of the kth running batch at the time t obtained by the fuzzy adaptive iterative learning controller,
is an unknown parameter associated with a system actuator failure,
is eta * The iterative estimated value of (1) is a self-adaptive item related to the fault of the system actuator, and the supplementary effect on the system actuator is realized by correcting the parameter value of the controller through the iterative error,
an estimate value representing a vector of fuzzy parameters,
representing a fuzzy basis function vector.
The fuzzy parameter vector is specifically:
and the adaptive term is specifically:
wherein the content of the first and second substances,
representing the estimate of the fuzzy parameter vector at time t of the kth run,
the fuzzy parameter vector is an estimated value at the time t of the kth-1 running batch; theta denotes a design parameter, theta>0; g (v (t, k)) represents a discrete Nussbaum gain for enabling iterative identification of unknown control directions of the system. In the present embodiment, θ =2.5. And designing a self-adaptive item, compensating the influence caused by the fault of the actuator by controlling the error, enabling the controlled system to track an upper expected track, improving the system performance and achieving the safe and reliable control effect.
S30: obtaining a control signal of the current running batch through the fuzzy self-adaptive iterative learning controller based on the state information of the current running batch and the tracking error of the last running batch;
according to the actual output result y (t, k) of the current operation batch of the continuous reaction kettle system obtained in the step S10 and the target output result y in the ideal reactant concentration curve d Subtracting (t, k) to obtain tracking errorThe difference is: e (t, k) = y (t, k) -y d (t, k). And inputting the control signal to the fuzzy self-adaptive iterative learning controller to obtain a control signal tau (t, k) of the current running batch.
S40: and inputting the control signal into a system actuator, correcting the control signal according to the fault of the system actuator to obtain an output signal of the system actuator, and acting the output signal on the continuous reaction kettle system to obtain an actual output result of the continuous reaction kettle system.
Preferably, the control signal is corrected based on multiplicative fault and additive fault of the system actuator due to external interference and incomplete failure of the time-varying actuator of the continuous reaction kettle system, and specifically comprises the following steps: u (t, k) = U (τ (t, k), t) = M (t) τ (t, k) + N (t), where M (t) represents a multiplicative fault, N (t) represents an additive fault, τ (t, k) represents a control signal of a system actuator, and U (t, k) represents an output signal of the system actuator.
If M (t) =1,N (t) =0, the system actuator works normally; m (t) =0 represents a complete failure fault of the system actuator, such as the condition that the system actuator falls off or is stuck; otherwise, the actuator has partial failure faults, such as structural looseness and other faults of the system actuator. In this embodiment, when k =0,1,2 …, the multiplicative fault of the system actuator fault in the continuous reactor system is M (t) =0.2sint +0.8, the additive fault is N (t) =0.2sint, the multiplicative fault range is 0 to 1, and the additive fault range is limited. Therefore, when the nonlinear system has actuator faults and external interference, the actual output result tracks the target output result after limited iterations, the tracking error is converged, and all system signals are guaranteed to be bounded. Meanwhile, tracking and revising the faults of the actuator can avoid that the actual state of the system deviates from the ideal control state more and more due to repeated accumulation of control errors caused by the faults along with the increase of reaction iteration times.
Inputting the control signal τ (t, k) of the current running batch obtained in the step S30 into a system actuator, calculating the output signal U (t, k) by the system actuator according to an expression U (t, k) = U (τ (t, k), t) = M (t) τ (t, k) + N (t), and applying the output signal U (t, k) of the system actuator to the continuous reaction kettle system to obtain an actual output result y (t, k) of the continuous reaction kettle system.
S50: and obtaining the tracking error of the continuous reaction kettle system according to the actual output result and the target output result of the continuous reaction kettle system, correcting the fuzzy self-adaptive iterative learning controller parameters and the self-adaptive items thereof based on the tracking error, and updating the control signal of the next operation batch to enable the actual output result of the system to approach the target output result.
Specifically, the tracking error correction fuzzy adaptive iterative learning controller parameters are as follows: the updating process of the discrete sequence v (t, k), the enhancement error a (t, k), the system enhancement auxiliary signal B (t, k), the system auxiliary signal C (t, k), and the switching signal k (t, k) is as follows:
C(t，k)＝1+|G(v(t，k))|，
preferably, the estimate of the blur parameter vector
Adaptive terms
And when the discrete sequence v (t, k + 1) is updated, the absolute value of the enhancement error A (t, k) in the operation of the kth batch is utilized to be matched with a preset threshold valueComparing, when the absolute value of the enhancement error a (t, k) is greater than or equal to a preset threshold value, the preset threshold value is σ, that is, | a (t, k) | is greater than or equal to σ, the switch signal κ (t, k) =1, and the estimation value of the fuzzy parameter vector
Adaptive terms
And the adaptive updating of the discrete sequence v (t, k + 1) is executed, and the parameters of the fuzzy adaptive iterative learning controller are corrected; when the absolute value of the enhancement error A (t, k) is smaller than a preset threshold, i.e. | A (t, k) & gt<When sigma is, a switch signal kappa (t, k) =0, the correction of the fuzzy adaptive iterative learning controller parameters is stopped, and the estimated values of fuzzy parameter vectors in different operation batches are obtained
Adaptive term
And the discrete sequence v (t, k + 1) remains unchanged. Sigma>0 is a design parameter as a preset threshold for enhancing the error, and 10 can be selected according to the precision requirement 3 Or 10- 5 . In the present embodiment, σ =0.001.
S60: and (6) repeatedly executing S30 to S50, carrying out repeated iteration updating until the preset control precision is reached, stopping iterative learning, and outputting a final control signal.
And the control system can approach the target control result at every moment until the preset control precision is obtained, the system signal is converged, and the iterative learning is stopped. The control precision can be improved, the system loss and the corresponding time are reduced, the stability of the system is improved, and the purpose of real-time control is achieved.
Referring to fig. 2, after the limited operation batch k is updated, the tracking error of the system is gradually reduced, and the fact that the actual output result of the system gradually approaches the target control result is proved.
Referring to FIG. 3, run 100 is compared withUnder the 400 th operation batch, the actual output result y (t, k) and the target output result y of the system d (t, k) in the case of repeated tasks, it can be seen that the tracking performance is better and better as the actual output result y (t, k) of the system increases with the number of iterations.
Referring to FIG. 4, the actuator control signal and the actuator output signal converge to a certain interval after a finite number of iterations.
The present application further provides a continuous reactor control device, the device comprising:
the model building module is used for building a state space model of the continuous reaction kettle system;
the state acquisition module is used for acquiring the state information of the current running batch of the continuous reaction kettle system based on the state space model;
the controller module is used for adding a self-adaptive item on the basis of the fuzzy controller to obtain a control signal equation of the fuzzy self-adaptive iterative learning controller, and obtaining a control signal of the current running batch through the control signal equation of the fuzzy self-adaptive iterative learning controller according to an ideal reactant concentration curve, the state information of the current running batch and the tracking error of the previous batch running;
the actuator module is used for inputting the control signal into the system actuator to obtain an output signal of the system actuator, and applying the output signal of the system actuator to the continuous reaction kettle system to obtain an actual output result of the continuous reaction kettle system;
and the updating module is used for obtaining the tracking error of the continuous reaction kettle system according to the actual output result and the target output result of the continuous reaction kettle system, correcting the parameters and the adaptive items of the control signal equation of the fuzzy adaptive iterative learning controller based on the tracking error, updating the control signal of the next operation batch, performing iterative updating for multiple times until the preset control precision is reached, stopping iterative learning and outputting the final control signal.
In one embodiment, a computer-readable storage medium is provided and includes a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement any one of the above fuzzy adaptive iterative learning control methods for a continuous reaction kettle.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. 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 a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchlink (Synchlink), DRAM (SLDRAM), rambus (Rambus) direct RAM (RDRAM), direct Rambus Dynamic RAM (DRDRAM), and Rambus Dynamic RAM (RDRAM), among others.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the system described in this application is divided into different functional units or modules to perform all or part of the above-mentioned functions.
Claims (10)
1. A fuzzy self-adaptive iterative learning control method for a continuous reaction kettle is characterized by comprising the following steps:
s10: establishing a state space model of a continuous reaction kettle system, and acquiring state information of a current operation batch of the continuous reaction kettle system based on the state space model;
s20: adding a self-adaptive item on the basis of a fuzzy controller to construct a control signal equation of the fuzzy self-adaptive iterative learning controller;
s30: obtaining a control signal of the current running batch through a control signal equation of the fuzzy self-adaptive iterative learning controller based on the state information of the current running batch and the tracking error of the last running batch;
s40: inputting the control signal into a system actuator, correcting the control signal according to the fault of the system actuator to obtain an output signal of the system actuator, and acting the output signal on the continuous reaction kettle system to obtain an actual output result of the continuous reaction kettle system;
s50: obtaining a tracking error of the continuous reaction kettle system according to an actual output result and a target output result of the continuous reaction kettle system, correcting parameters and adaptive items of a control signal equation of the fuzzy adaptive iterative learning controller based on the tracking error, and updating a control signal of the next operation batch;
s60: and (5) repeatedly executing S30 to S50, and stopping iterative learning after repeated iterative updating until the preset control precision is reached, and outputting a final control signal.
2. The fuzzy adaptive iterative learning control method for the continuous reaction kettle according to claim 1, wherein the step of correcting the control signal according to the fault of the system actuator to obtain the output signal of the system actuator specifically comprises:
correcting the control signal based on multiplicative fault and additive fault of a system actuator:
u(t,k)＝U(τ(t,k),t)＝M(t)τ(t,k)+N(t)，
where M (t) represents a multiplicative fault, N (t) represents an additive fault, τ (t, k) represents a control signal for a system actuator, and u (t, k) represents an output signal for a system actuator.
3. The fuzzy adaptive iterative learning control method for continuous reactors according to claim 2, wherein the multiplicative fault is specifically:
M(t)＝0.2sint+0.8。
4. the fuzzy adaptive iterative learning control method for continuous reactors according to claim 2, wherein the additive fault is specifically:
N(t)＝0.2sint。
5. the fuzzy adaptive iterative learning control method of the continuous reaction kettle according to claim 1, wherein the step of adding an adaptive term on the basis of the fuzzy controller to construct a control signal equation of the fuzzy adaptive iterative learning controller comprises:
and obtaining an estimated value of a fuzzy parameter vector according to the state information and a target output result through a fuzzy rule, multiplying the estimated value of the fuzzy parameter vector by a fuzzy basis function vector, and adding a self-adaptive item related to the fault of a system actuator to obtain a control signal equation of the fuzzy self-adaptive iterative learning controller.
6. The fuzzy adaptive iterative learning control method for continuous reaction kettles according to claim 5, wherein the expression of the adaptive term related to the system actuator fault is specifically:
wherein the content of the first and second substances,
the adaptive term representing the kth lot at time t,
the adaptive term representing the k-1 th batch at time t,
representing the design parameters, v (t, k) representing the discrete sequence, G (v (t, k)) representing the discrete Nussbaum gain, k (t, k) representing the switching signal, B (t, k) representing the system enhancement auxiliary signal, a (t, k) representing the enhancement error.
7. The method of claim 6, wherein the fuzzy adaptive iterative learning controller has a control signal equation of:
wherein τ (t, k) represents the control signal for the kth run batch at time t,
is an adaptive term related to system actuator failure,
an estimate value representing a vector of fuzzy parameters,
representing a fuzzy basis function vector.
8. The method according to claim 1, wherein the step of correcting the parameters of the fuzzy adaptive iterative learning controller and the adaptive terms thereof based on the tracking error further comprises:
obtaining an enhancement error based on the tracking error, comparing the absolute value of the enhancement error with a preset threshold, and correcting the fuzzy self-adaptive iterative learning controller parameter and the self-adaptive item thereof when the absolute value of the enhancement error is greater than or equal to the preset threshold;
and when the absolute value of the enhancement error is smaller than a preset threshold value, stopping correcting the fuzzy self-adaptive iterative learning controller parameters and the self-adaptive items thereof.
9. A fuzzy adaptive iterative learning control apparatus for a continuous reactor, the apparatus comprising:
the model building module is used for building a state space model of the continuous reaction kettle system and acquiring the state information of the current running batch of the continuous reaction kettle system based on the state space model;
the controller module is used for adding a self-adaptive item on the basis of the fuzzy controller, constructing a control signal equation of the fuzzy self-adaptive iterative learning controller, and obtaining a control signal of the current running batch through the control signal equation of the fuzzy self-adaptive iterative learning controller on the basis of the state information of the current running batch and the tracking error of the previous running batch;
the actuator module is used for inputting the control signal into a system actuator, correcting the control signal according to the fault of the system actuator to obtain an output signal of the system actuator, and acting the output signal on the continuous reaction kettle system to obtain an actual output result of the continuous reaction kettle system;
the updating module is used for obtaining the tracking error of the continuous reaction kettle system according to the actual output result and the target output result of the continuous reaction kettle system, correcting the parameters and the adaptive items of the control signal equation of the fuzzy adaptive iterative learning controller based on the tracking error and updating the control signal of the next operation batch; and after repeated iterative updating, stopping iterative learning until the preset control precision is reached.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 8.
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