The Study On Intelligent Control Based On Genetic Algorithms

With the rapid development of the science and technique, modern industry control often has complexity, uncertainty, non-linearity, time-delay and bad-modeling, and the traditional control can not satisfy these demands. After long time's gestation and development, people recognized that a new and effective control strategy which can deal with the complicated task, perceive the intricate environment and control the complex object should be put forward and explored. Intelligent Control occurred under this background. Intelligent Control is a complicated system that has the ability of learning, adapting and error-tolerance. Intelligent Control system has several types but in which Neurocontrol and Fuzzy Control are used widely. This paper will discuss and solve the questions occurred in Intelligent Control by using these two types.Genetic Algorithm is a global optimal algorithm with robustness and applicability. It is a heuristic random algorithm using natural selection and nature genetic mechanism for reference. Genetic Algorithm is an excellent method that deals with the complicated and nonlinear question, which cannot be solved by the traditional search methods well. It not only can explain the adaptive process of the nature further, but also can apply the important mechanism of the natural biologic system to the designing of the artificial system to Research GA.The purpose of this paper is utilizing GAs to optimize and design the intelligent controller automatically and releasing people from the time-consuming trial and error exercises. Some modified methods of Genetic Algorithms are proposed and adopted in the simulation experiments of this paper in order to avoid the premature convergence and strengthen the search capability of GAs.The main work and research results of this dissertation are summarized in the following aspects:(1) The basic knowledge and current situation of Intelligent Control and Genetic Algorithms are introduced.(2) The foundations of Genetic Algorithm are analyzed and researched deeply, including SGA, Schema Theorem and Building Block Hypothesis.(3) The improved schemes of SGA are discussed, including the improvements of encoding, fitness function, selection operators, crossover operators, mutation operators and some advanced GAs.(4) Some new improved strategies are proposed in this paper, such as nonlinear adaptive probabilities of crossover and mutation, similar individuals' recognition method and so on.(5) The basic theory of Neurocontrol and Fuzzy control is discussed respectively. The methods to eliminate their disadvantages by using GA are discussed, and the studies on the combination of GA with Neurocontrol and fuzzy control are given more emphases.(6) The simulation examples to overcome the shortcomings of Neurocontrol and Fuzzy Control by using GAs proposed in this paper are presented at last. MATLAB and SIMULINK software are employed in the experiments. The integration of function programming with SIMULINK is realized:...