CN114326402A - Pneumatic proportional position system control method of MFAC (Multi-frequency alternating Current) based on genetic algorithm optimization - Google Patents
Pneumatic proportional position system control method of MFAC (Multi-frequency alternating Current) based on genetic algorithm optimization Download PDFInfo
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Abstract
The application relates to the field of control, and particularly discloses a pneumatic proportional position system control method of MFAC (MFAC) based on genetic algorithm optimization, which comprises the following steps: the method comprises the steps of firstly obtaining a target transfer function based on a pneumatic proportional position system model, then relating to a model-free self-adaptive controller based on the target transfer function, and optimizing parameters of the controller through a genetic algorithm, so that parameter setting and optimization of the model-free self-adaptive algorithm are realized, the problem that in the prior art, in the actual control process, parameter setting and optimization are difficult is solved, and the control effect is improved.
Description
Technical Field
The application relates to the technical field of control, in particular to a pneumatic proportional position system control method of MFAC based on genetic algorithm optimization.
Background
The pneumatic energy is clean energy, and the pneumatic system has the advantages of quick response, high safety factor, strong stability and the like. Pneumatic systems are currently widely used in various aspects of the industrial automation field. The emergence of pneumatic proportional technology has made the application level of pneumatic system more extensive. However, in practical applications, the control of the pneumatic proportional position system is affected by many factors, such as compressibility of gas, nonlinearity of a valve, frictional force characteristics of a cylinder, and susceptibility of system parameters to environmental influences, and therefore, a higher requirement is put forward on the control difficulty of the pneumatic proportional position system. The problem to be solved at present is to realize the quick and accurate adjustment of the pneumatic proportional position system.
In the prior art, the traditional PID control algorithm still has a large improvement space in control accuracy and speed. In addition, the hou loyd professor proposed model-free adaptive control (MFAC) in 1994, which is a control algorithm based on data driving, and the method is to establish an equivalent dynamic linearized data model at each dynamic working point by introducing pseudo partial derivatives or pseudo gradient parameters, so that a controller can be directly designed by only using the input quantity and the output quantity of a controlled object without depending on a mathematical model excessively, and thus the MFAC has a unique advantage in dealing with the problem of nonlinearity. MFAC is now widely used in the control and design of unmanned vehicles, boilers, ships, drones, and other various fields.
Then, for different control systems, different control effects are caused by different parameters of the controller of the MFAC principle, and therefore, it is important to set appropriate controller parameters more quickly and accurately in the control process. The problem that parameter setting and optimization are difficult in the existing model-free adaptive control algorithm is solved.
Disclosure of Invention
The application provides a pneumatic proportional position system control method of MFAC based on genetic algorithm optimization, which aims to solve the problem that parameter setting and optimization are difficult in a model-free adaptive control algorithm in the prior art.
The above object of the present application is achieved by the following technical solutions:
the embodiment of the application provides a pneumatic proportional position system control method of MFAC based on genetic algorithm optimization, which comprises the following steps:
constructing a mathematical model of a pneumatic proportional position system, and determining a target transfer function;
designing a model-free self-adaptive controller according to the target transfer function;
setting parameters of the model-free adaptive controller based on a genetic algorithm principle to obtain optimized controller parameters;
obtaining an optimized model-free adaptive controller based on the optimized controller parameters;
and controlling a preset pneumatic proportional position system through the optimized model-free adaptive controller.
Further, the building a mathematical model of the pneumatic proportional position system and determining the transfer function includes:
calculating and determining a proportional flow continuity equation, a proportional valve port flow equation and a cylinder piston force balance equation;
rewriting the proportional flow continuity equation, the proportional valve port flow equation and the air cylinder piston force balance equation into an incremental equation, and performing Laplace transformation to obtain a valve control cylinder transfer function in a simultaneous manner;
and obtaining a mathematical model of the pneumatic proportional position system according to the valve control cylinder transfer function and a preset proportional flow valve mathematical model, and determining a target transfer function.
Further, the setting the parameters of the model-free adaptive controller based on the genetic algorithm principle to obtain the optimized controller parameters includes:
setting genetic algorithm operation parameters; wherein the parameters comprise population number, maximum iteration number, cross probability and variation probability;
randomly generating samples with the number of the populations based on the parameters of the model-free adaptive controller, and generating an initialized population; wherein each of the samples corresponds to a set of model-free adaptive controller parameters;
performing iterative operation on the population individuals according to the cross probability and the variation probability, calculating the fitness of each individual of the population after current iteration, and determining the optimal solution of the population after current iteration;
repeating the iteration operation until the iteration times are the maximum iteration times to obtain a final generation population;
and determining the optimized controller parameters based on the optimal solution in the last generation population.
Further, the performing an iterative operation on the population individuals according to the cross probability and the variation probability includes:
selecting the old population according to a preset probability to form a new population, and breeding to obtain next generation individuals; wherein the preset probability is determined based on an individual fitness value;
based on the cross probability, randomly selecting two individuals from the population to carry out cross operation to obtain a new individual;
and randomly selecting an individual from the population based on the mutation probability, and performing mutation operation to obtain a new individual.
Further, the calculating the fitness of each individual of the population after the current iteration and determining the optimal solution of the population after the current iteration include:
determining an absolute deviation integral performance index comprehensive function;
adjusting the absolute deviation integral performance index comprehensive function based on constraint conditions of overshoot, steady-state error and rise time to obtain a fitness function;
calculating the fitness of each individual of the population after current iteration according to the fitness function;
and determining a population optimal solution based on the fitness. .
The technical scheme provided by the embodiment of the application can have the following beneficial effects:
according to the technical scheme provided by the embodiment of the application, the target transfer function is obtained based on the pneumatic proportional position system model, the model-free self-adaptive controller is related to based on the target transfer function, and the parameters of the controller are optimized through the genetic algorithm, so that the parameter stabilization and optimization of the model-free self-adaptive algorithm are realized, the problem that in the prior art, in the actual control process, the parameter setting and optimization are difficult in the model-free self-adaptive control process is solved, and the control effect is improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application.
FIG. 1 is a schematic diagram of a pneumatic proportional position system suitable for use in the control method of the present application;
FIG. 2 is a schematic flow chart of a method for controlling a pneumatic proportional position system based on a genetic algorithm optimized MFAC according to an embodiment of the present application;
FIG. 3 is a block diagram of a control system for model-free adaptive control;
FIG. 4 is a flow chart of a genetic algorithm versus model-free adaptive algorithm parameter optimization in a method for controlling an MFAC based on genetic algorithm optimization according to an embodiment of the present application;
FIG. 5 is a variation curve of a performance index function J of a pneumatic proportional position system control method of MFAC based on genetic algorithm optimization provided by an embodiment of the present application;
FIG. 6 is a graph comparing the step-mapped tracking performance of the pneumatic proportional position system control method based on genetic algorithm optimization MFAC provided by the embodiment of the present application with other control methods;
fig. 7 is a comparison graph of step corresponding errors of the pneumatic proportional position system control method based on genetic algorithm optimization MFAC and other control methods provided in the embodiments of the present application.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.
FIG. 1 is a schematic diagram of a pneumatic proportional position system suitable for use in the control method of the present application, as shown in FIG. 1:
the pneumatic proportional control system is composed of cylinder and proportionThe system is characterized in that a schematic diagram of the system is shown in figure 1, a proportional flow valve is used as an actuator to control the displacement output of an air cylinder, a laser displacement sensor transmits a measured signal to a computer end through a data acquisition card, and a feedback signal bit u of the measured air cylinder displacement output is transmitted to a computer end through the action of a control algorithm in the computereWith a given system input signal ufThe difference value Δ u is gradually decreased to achieve better tracking performance, but the control effect is not good in the prior art.
In order to solve the problems, the application provides a pneumatic proportional position system control method of MFAC based on genetic algorithm optimization, and a genetic algorithm is applied to optimize model-free adaptive control algorithm parameters, so that the control effect is improved. Specific embodiments are illustrated in detail by the following examples.
Examples
Referring to fig. 2, fig. 2 is a schematic flow chart of a pneumatic proportional position system control method of a MFAC based on genetic algorithm optimization according to an embodiment of the present application, as shown in fig. 2, the method at least includes the following steps:
s101, a mathematical model of the pneumatic proportional position system is constructed, and a target transfer function is determined.
The method comprises the following steps: calculating and determining a proportional flow continuity equation, a proportional valve port flow equation and a cylinder piston force balance equation; rewriting the proportional flow continuity equation, the proportional valve port flow equation and the air cylinder piston force balance equation into an incremental equation, and performing Laplace transformation to obtain a valve control cylinder transfer function in a simultaneous manner; and obtaining a mathematical model of the pneumatic proportional position system according to the valve control cylinder transfer function and a preset proportional flow valve mathematical model, and determining a target transfer function.
The method specifically comprises the following steps: s1011, making basic assumption.
The compressed air generated by the air compressor in the system has the characteristics of low gas density, weak viscosity and longer response time of a pneumatic system; and the flow rate of the gas is fast when the gas passes through the valve port. The piping for gas delivery in this system is short.
In view of the above, the derivation of the model is based on the following ideal conditions:
(1) the gas media in the system are assumed to be ideal gases and all accord with an ideal gas state equation.
(2) The gas flow process during the opening and closing of the valve port is assumed to be an isentropic adiabatic process.
(3) Neglecting the influence of viscous resistance and temperature variation of the gas
(4) Neglecting pressure loss and flow changes during gas flow
And S1012, determining a proportional valve flow continuity equation.
Specifically, the mass flow Q of gas in and out of the cylinder can be known according to the mass conservation lawmThe mass m change rate of the air cylinder is equal to that of the accommodating cavity, and an equation of an a cavity column of the air cylinder is as follows:
when the assumed conditions are met, the following can be obtained according to the ideal gas state equation:
in the formula m1、V1、p1The mass, volume and pressure of the a cavity respectively; t is1Cavity a temperature; r-universal gas constant.
According to the formulae (1-1) and (1-2):
temperature T during cylinder operation under the above-mentioned assumption1And the initial temperature T10The heat insulation process is satisfied:
wherein k is an adiabatic coefficient, and k is 1.41. p is a radical of0Is the air supply pressure. T issIs the starting temperature.
The formula (1-3) is substituted by the derivation of the formula (1-4), and the mathematical expression of the gas dynamic process of the chamber a after the arrangement is as follows:
similarly, the mathematical expression of the dynamic process of the b-cavity gas is
In the formula T2、V2、p2The temperature, the volume and the pressure of the cavity b are respectively;
and S1013, determining a valve port flow equation of the proportional valve.
The gas flow process through the valve port is complex, and the ideal gas flow is approximated to one-dimensional isentropic flow through the contraction nozzle under the assumed conditions, and the calculation can be carried out by adopting the Sanville flow formula.
Qm1And Qm2Is about the displacement X of the valve core and the gas pressure p of the two cavities of the cylinder a and b1、p2The pressure-flow characteristic near the initial equilibrium position is obtained after linearization:
in the formula (1-7), the compound,
and S1014, determining a force balance equation of the cylinder piston.
From the second newton equation, the following equation can be obtained:
in the formula (1-8), M-is approximately the mass of a cylinder piston and an inertia load in kg; fL-external load force, in N; f-viscous damping coefficient, unit
FfFriction, in N.
And S1015, determining the transfer function of the valve control cylinder.
The equations (1-5) and (1-6), (1-7) and (1-8) are rewritten into incremental equations, and then are subjected to Laplace transform and then are combined to obtain the valve control cylinder transfer function as follows:
in the formula b1＝k1kRTs(AaVb0+AbVa0)；b0＝2Ts 2k2k1k2(Aa+Ab)；a3＝MVa0Vb0；a2＝Mkk2RTs(Va0+Vb0)+fVa0Vb0；a1＝MR2Ts 2k2k2 2+fk2KRTs(Va0+Vb0)+k(pa0Aa 2Vb0+pb0Ab 2Va0；a0＝fR2Ts 2k2k2 2+k2k2RTs(pa0Aa 2+pb0Ab 2)。
And S1016, determining a mathematical model of the proportional flow valve.
In a system where a single proportional valve controls a cylinder, as is typical, the mathematical model for the proportional valve is as follows:
in the formula: natural frequency of proportional valve
Damping ratio of proportional valve
KfkIs the gain of the proportional valve.
S1017, determining a mathematical model of the pneumatic proportional position system.
The mathematical model of the pneumatic proportional position system obtained after the arrangement according to the (1-9) and the (1-10) is as follows:
because the pneumatic proportional valve has high response speed in actual operation, the natural frequency of the pneumatic proportional valve is relatively lowest in a control loop, and the natural frequency has a crucial influence on the dynamic characteristics of a system and is substituted into
And further simplified to yield:
in the formula:
substituting the system parameters to obtain the third-order transfer function, namely the target transfer function:
and S102, designing a model-free self-adaptive controller according to the target transfer function.
Specifically, a model-free adaptive controller is used to control the pneumatic proportional position system, and fig. 3 is a block diagram of a control system of the model-free adaptive controller, in an embodiment provided in the present application, a process of determining the model-free adaptive controller includes:
and S1021, designing a pseudo gradient vector.
A typical single-input single-output discrete-time nonlinear system can be represented as:
y(k+1)＝f(y(k),y(k-1),…，y(k-ny),u(k),u(k-1),···,uk-nu2-1；
wherein y (k) e R, u (k) e R respectively represent the output and input of the system at time k; n isy、nuAre two unknown positive integers; f (…) is an unknown nonlinear function.
The following assumptions are made before deriving the control rate.
Assume that f (…) has a continuous partial derivative for each variable. And for the system represented by the above formula, it satisfies the generalized Lipschitz continuity condition, that is, for arbitrary k and Δ u (k) ≠ 0, there are:
Δy(k+1)≤bΔu(k) (2-2)；
wherein Δ y (k +1) ═ y (k +1) -y (k); Δ u (k) ═ u (k) — u (k-1); b is a constant. Thus, there must be a Φ (k) ∈ R such that the following holds:
Δy(k+1)＝Φ(k)Δu(k) (2-3)；
and S1022, deriving the control rate.
The criterion function that takes into account the control input is as follows:
J(u(k))＝[y*(k+1)-y(k+1)]2+λ[u(k)-u(k-1)]2 (2-4)；
in the formula y*(k +1) is the desired output, λ>0 is a weighting factor, and the derivative of u (k) is equal to 0, for the controlled system and the generic model thereof, the MFAC scheme based on the compact format dynamic linearized data model is as follows, and the basic form of the control rate of the model-free control method is as follows.
Where λ is an appropriate constant greater than 0, the effect of which limits the variation in the control input amount while also avoiding the above-described case where the denominator is 0.ρ ∈ (0, 2) is the step factor.
S1023 PPD estimation algorithm.
Since phi (k) is unknown, the above equation cannot be solved directly, so that the online estimation value of phi (k) is adopted to solve the above equation, and the estimation criterion function of PPD is
μ>0 is a weighting factor;
for the estimated value of PPD, φ (k) is extremized using the optimal conditions according to equations (2) and (5).
Where η ∈ (0, 1);. mu is a weighting factor, which is a suitable constant greater than zero.
If it is not
Or delta u (k-1) is less than or equal to epsilon or
The method comprises the following steps:
ε is a sufficiently small positive number;
is composed of
Is started.
S103, setting the parameters of the model-free adaptive controller based on the genetic algorithm principle to obtain optimized controller parameters. Fig. 4 is a flow chart of parameter optimization of a genetic algorithm to a model-free adaptive algorithm in a method for controlling a pneumatic proportional position system of an MFAC based on genetic algorithm optimization according to an embodiment of the present application, as shown in fig. 4:
the method specifically comprises the following steps:
and S1031, setting running parameters of the genetic algorithm.
Including the number N of the populationPMaximum number of iterations GmCross probability PcAnd the mutation probability adopts the self-adaptive mutation rate.
Pm＝0.1-[1:1:Np]×0.01/Np (3-1)；
S1032, parameter encoding, determining an optimization interval of four parameters μ, ρ, λ, η of a model-free adaptive controller (MFAC), determining an encoding method, and randomly generating N in an N-dimensional space (where N is 4, four parameters)PSamples within a feasible range, wherein each sample corresponds to a group of feasible MFAC controller parameters to generate an initialization population;
and S1033, calculating the fitness of each individual, and finding out the optimal solution of the population under the current iteration times.
In practical applications, the selection of the fitness function has an important influence on a parameter optimization result of the MFAC controller, and therefore, factors such as stability, rapidity, accuracy and the like of control need to be considered when the fitness function is selected. Therefore, an absolute deviation Integral (IAE) performance index comprehensive function is adopted, and in order to improve the problems existing in the adjusting process, constraint conditions aiming at overshoot, steady-state error and rise time are added, so that the following performance index function is formed as a fitness function.
γ1、γ2、γ3、γ4Selecting the numerical value of the weight according to a specific control object, and e (t) is a system error; Δ y (t) ═ y (t +1) -y (t), and y (t) are outputs of the controlled object; u (t) is the control algorithm output; t is tuIs the rise time.
The iteration includes genetic manipulation of the population, and manipulation of individual population by selection, crossover, mutation, and the like, so as to evolve to generate the next generation of population.
For example, the selection operation: selecting good individuals from the old population with a certain probability to form a new population so as to breed and obtain next generation individuals. The probability of the individual being selected is related to the fitness value, the higher the fitness value of the individual is, the higher the probability of the individual being selected is, the genetic algorithm selects and operates various methods such as a roulette method, a tournament method and the like, the roulette method is selected in the invention, and the probability of the individual i being selected is as follows:
wherein, FiFitness value for individual i; n is a radical ofpThe number of individuals in the population.
And (3) cross operation: two individuals are randomly selected from the population, and through the exchange combination of the two chromosomes, the excellent characteristics of the father string are inherited to the substring, so that a new excellent individual is generated. Since individuals are encoded using real numbers, the crossover operation uses a real number crossover method, the kth chromosome αkAnd the l-th chromosome alphalInterleaving of sub j bitsThe method comprises the following steps:
αkj＝αij(1-b)+αljb (3-4)；
αlj＝αlj(1-b)+αkjb (3-5)；
wherein b is [0, 1]]Random number of intervals. If P isc>And when in land, performing crossover operation. PcIn the present invention, 0.9 is taken
Mutation operation: the main objective is to maintain species diversity. The mutation operation randomly selects an individual from the population, and selects one point of the individual to perform mutation to generate more excellent individuals. J gene alpha of i individualijThe operation method for carrying out the mutation is
Wherein M isaxX is a vector consisting of the maximum values of four parameters for model-free adaptive control, MinX is a vector consisting of the minimum part of four parameters of model-free adaptive control, PmIs the mutation probability.
In practical application, in the iteration process of the genetic operation, the condition of loop iteration or termination is that when the evolution algebra is smaller than the maximum iteration number, the loop is performed and step S1033 is performed, and if the set maximum iteration number is reached, the optimal individual is the optimal individual in the last generation population. And substituting the optimized model-free controller parameters into the built control system model.
And S104, obtaining the optimized model-free self-adaptive controller based on the optimized controller parameters.
And S105, controlling a preset pneumatic proportional position system through the optimized model-free adaptive controller.
The embodiment of the application provides a pneumatic proportional position system control method of MFAC based on genetic algorithm optimization, which comprises the steps of firstly obtaining a target transfer function based on a pneumatic proportional position system model, then relating to a model-free adaptive controller based on the target transfer function, and optimizing parameters of the controller through the genetic algorithm, so that parameter stabilization and optimization of the model-free adaptive algorithm are realized, the problem that in the prior art, in the actual control process, parameter setting and optimization are difficult in the model-free adaptive control process is solved, and the control effect is improved.
The control effect of the method for controlling the MFAC based on genetic algorithm optimization according to the embodiment of the present application is verified experimentally, and the specific process is as follows:
experimental protocol design was performed first. The method comprises the following steps: and performing mathematical modeling on the pneumatic proportional position control system to obtain a specific model transfer function. Substituting the system model into a program of a model-free adaptive control algorithm, and setting parameters of the model-free adaptive control algorithm through a genetic algorithm to obtain controller parameters expected to be output by the model. And substituting the parameters of the controller into a model-free adaptive pneumatic proportional position system control program to obtain a control experiment result of the GA-MFAC on the pneumatic proportional position system. The control method experimental results of the present invention were compared with those of the MFAC and PID control of manually set parameters.
Next, setting experimental parameters, the control method of the present invention sets experimental parameters as follows:
the construction of a mathematical model of the pneumatic proportional position system substitutes the data information of the equipment to obtain the transfer function of the system as follows:
the parameters of the genetic algorithm optimization model-free adaptive control algorithm are set as shown in the attached table 1:
TABLE 1 genetic Algorithm optimized model-free adaptive control Algorithm parameter Table
The final parameter optimization results are shown in the following attached table 2:
TABLE 2 results of parameter optimization
| Best_J | Bestfi | λ | μ | η | ρ |
| 39.2358 | 0.0255 | 0.1391 | 0.3924 | 0.5660 | 1.9272 |
The resulting performance indicator function is shown in FIG. 5.
The parameters obtained by setting in the invention are put into operation, and the output of the pneumatic proportional position control system under the condition of set step change can be obtained.
In the invention, four parameters are set when the model-free adaptive control algorithm is manually set as shown in attached table 3:
TABLE 3 four parameters for manual tuning of model-free adaptive control algorithms
| λ | μ | η | ρ |
| 0.01 | 0.01 | 10 | 0.8 |
And the invention carries out step response comparison of the pneumatic proportional position control system with the classical PID control algorithm. The corresponding control parameter settings are shown in attached table 4:
TABLE 4 control parameters
| kp | ki | kd |
| 1 | 5 | 0 |
The three control methods are compared with the step response tracking performance of the pneumatic proportional position control system, and the experimental effect is shown in FIG. 6; the step response error ratio is shown in figure 7.
Simulation experiment results show that the pneumatic proportional position system control method based on the MFAC optimized by the genetic algorithm provided by the embodiment of the application has obvious improvement on step response tracking characteristics no matter relative to the control effect realized after manually adjusting the parameters of the MFAC controller or compared with the PID control effect. The method has the advantages that the adjusting time of the positive control system is obviously shortened, the control quality is better, and the method has higher precision obviously obtained from an error effect graph.
And on the real object platform, the control method is a non-data-driven control algorithm, so that a high-performance control effect can be realized without additional controlled object data information. Therefore, for the control of the pneumatic proportional position control system, the control method provided by the embodiment of the application is a high-quality control method, and the control effect is better.
It is understood that the same or similar parts in the above embodiments may be mutually referred to, and the same or similar parts in other embodiments may be referred to for the content which is not described in detail in some embodiments.
It should be noted that, in the description of the present application, the terms "first", "second", etc. are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. Further, in the description of the present application, the meaning of "a plurality" means at least two unless otherwise specified.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and the scope of the preferred embodiments of the present application includes other implementations in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present application.
It should be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present application may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc.
In the description herein, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
Although embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application, and that variations, modifications, substitutions and alterations may be made to the above embodiments by those of ordinary skill in the art within the scope of the present application.
Claims (5)
1. A method for controlling a pneumatic proportional position system of an MFAC based on genetic algorithm optimization, comprising:
constructing a mathematical model of a pneumatic proportional position system, and determining a target transfer function;
designing a model-free self-adaptive controller according to the target transfer function;
setting parameters of the model-free adaptive controller based on a genetic algorithm principle to obtain optimized controller parameters;
obtaining an optimized model-free adaptive controller based on the optimized controller parameters;
and controlling a preset pneumatic proportional position system through the optimized model-free adaptive controller.
2. The method of claim 1, wherein the constructing a mathematical model of the aero-proportional position system to determine the target transfer function comprises:
calculating and determining a proportional flow continuity equation, a proportional valve port flow equation and a cylinder piston force balance equation;
rewriting the proportional flow continuity equation, the proportional valve port flow equation and the air cylinder piston force balance equation into an incremental equation, and performing Laplace transformation to obtain a valve control cylinder transfer function in a simultaneous manner;
and obtaining a mathematical model of the pneumatic proportional position system according to the valve control cylinder transfer function and a preset proportional flow valve mathematical model, and determining a target transfer function.
3. The method of claim 1, wherein the tuning the parameters of the model-free adaptive controller based on genetic algorithm principles to obtain optimized controller parameters comprises:
setting genetic algorithm operation parameters; wherein the parameters comprise population number, maximum iteration number, cross probability and variation probability;
randomly generating samples with the number of the populations based on the parameters of the model-free adaptive controller, and generating an initialized population; wherein each of the samples corresponds to a set of model-free adaptive controller parameters;
performing iterative operation on the population individuals according to the cross probability and the variation probability, calculating the fitness of each individual of the population after current iteration, and determining the optimal solution of the population after current iteration;
repeating the iteration operation until the iteration times are the maximum iteration times to obtain a final generation population;
and determining the optimized controller parameters based on the optimal solution in the last generation population.
4. The method of claim 3, wherein the iterative operation of the population of individuals according to the cross probability and the variation probability comprises:
selecting the old population according to a preset probability to form a new population, and breeding to obtain next generation individuals; wherein the preset probability is determined based on an individual fitness value;
based on the cross probability, randomly selecting two individuals from the population to carry out cross operation to obtain a new individual;
and randomly selecting an individual from the population based on the mutation probability, and performing mutation operation to obtain a new individual.
5. The method of claim 3, wherein the calculating the fitness of each individual of the population after the current iteration and determining the optimal solution of the population after the current iteration comprises:
determining an absolute deviation integral performance index comprehensive function;
adjusting the absolute deviation integral performance index comprehensive function based on constraint conditions of overshoot, steady-state error and rise time to obtain a fitness function;
calculating the fitness of each individual of the population after current iteration according to the fitness function;
and determining a population optimal solution based on the fitness.
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| CN113361713A (en) * | 2021-05-24 | 2021-09-07 | 唐山钢铁集团有限责任公司 | Rolling force control method for rolling mill production line based on self-adaptation |
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