Fuzzy control system optimization method based on differential evolution-local unimodal sampling algorithm

A technology of fuzzy control system and sampling algorithm, applied in general control system, control/regulation system, adaptive control and other directions, can solve the problems of complex differential algorithm and long calculation time

Active Publication Date: 2016-09-28
SOUTHEAST UNIV
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The differential evolution (DE) algorithm is an improved genetic algorithm, but the di

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  • Fuzzy control system optimization method based on differential evolution-local unimodal sampling algorithm
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  • Fuzzy control system optimization method based on differential evolution-local unimodal sampling algorithm

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Embodiment Construction

[0033] A fuzzy control system optimization method based on differential evolution-local unimodal sampling algorithm, comprising the following steps;

[0034] (1) Determine the fitness function; analyze the characteristics of the fuzzy PID controller, select the parameters to be optimized of the system, and express it in a reasonable composition form, which can be a function expression, a pattern vector, a combination selection, etc.;

[0035] (2) Using the differential evolution algorithm, setting an appropriate number of iterations, repeatedly performing mutation, crossover, and selection operations to adjust and optimize these parameters, and initially obtain a better parameter combination;

[0036] (a) Population initialization

[0037] Determine the n-dimensional feasible solution space of the problem and randomly generate M individuals as the initial population. The specific expression is as follows:

[0038] x i j ...

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Abstract

The invention discloses a fuzzy control system optimization method based on a differential evolution-local unimodal sampling algorithm. The fuzzy control system optimization method comprises the following steps that (1) a fitness function is determined; (2) a differential evolution algorithm is used, the appropriate number of times of iterations is set to repeatedly perform variation, intersection and selection operation until meeting the convergence precision or reaching the maximum number of times of iterations, and corresponding parameter combinations are obtained; and (3) a local unimodal sampling algorithm is used, the parameter combinations finally obtained through the differential evolution algorithm act as initial values, search range reducing and target vector transferring operation is repeatedly performed according to the fitness value until the end of iteration, and the parameter combination of the best fitness value acts as the optimal result. The beneficial effects of the fuzzy control system optimization method are that the two algorithms are combined, the number of times of iteration of the differential evolution algorithm is reduced, and switching to the local unimodal sampling algorithm is performed after obtaining the preliminary optimal solution so that the advantages of the two algorithms can be fully exerted, the defects of the two algorithms can be compensated and the optimization computation efficiency and the global convergence can be greatly enhanced.

Description

technical field [0001] The invention relates to the field of fuzzy control, in particular to a method for optimizing a fuzzy control system based on a differential evolution-local unimodal sampling algorithm. Background technique [0002] With the development of computer level, more intelligent algorithms are used to solve system optimization problems, such as genetic (GA) algorithm, particle swarm optimization (PSO) algorithm, ant colony algorithm, etc. The competition mechanism is the operating criterion. The differential evolution (DE) algorithm is an improved genetic algorithm, but the differential algorithm is still relatively complicated and takes a long time to calculate. Contents of the invention [0003] The technical problem to be solved by the present invention is to provide a fuzzy control system optimization method based on differential evolution-local unimodal sampling algorithm, which can effectively play the advantages of the two algorithms, as a general m...

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Application Information

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IPC IPC(8): G05B13/04
CPCG05B13/042
Inventor 向文国刘一君陈时熠
Owner SOUTHEAST UNIV
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