The Research Of Optimizing Method Of PID Controller Based On Intelligent Algorithm

PID is one of the earliest control measures, it is used widely in kinds of industry circumstance for its simple structure, easy implementation and strong robustness. The capability of controller directly influences the qualities of producing process and products. Therefore, parameter tuning of controllers is the most important step during system design. With the development of computer technology and artificial intelligence in automatic control field, all kinds of parameters tuning methods of PID controller have emerged in endlessly, which bring much energy for the study of PID controller. The development of auto-tune release control engineers from field configuration, and save a large amount of time. On the other hand, it makes tuning result more reliable, and some refined but complex methods can be used in practical industrial process control. But many advanced tuning methods behave not so perfect as to be expected as to be expected. So there are academic value and engineering application value which study PID parameter auto-tuning. The paper go deep into studying the question.In the chapter one, We discusses the meanings of PID parameter auto-tuning methods and researching achievements on this subject. In the chapter two, genetic algorithm was described in detail. GA has such good qualities as no differential coefficient requiring, no local linearization, low request for initial model, robust and so on. It is applied in PID para--meter auto-tuning, simulation results show that the adaptive genetic algorithm has perfect optimization effect. In the chapter three, the basic principles of DNA-GA is presented. DNA-GA is very agile, the length of the DNA chromosome is veried which makes insert and delete DNA sequence easy to realize. It is applied in PID parameter auto- tuning, simulation results show that DNA-GA can search more excellent parameters than GA in the same evolution and that the algorithm for optimizing parameter is applied and effective, and is much better than that of common Genetic Algorithm, and has good perfectible application future. In section two of the chapter four, a new CPSO algorithm is formed by combining the traditional chaos algorithm and Particle Swarm Optimization, and used in PID controller to optimize parameters. Simulation results show that the algorithm is efficient to realize self-tuning global optimal parameters of PID controller, which have the advantages of stability and small overshoot, and it is easy to realize, highly effective and speediness. The algorithm supports a effective method for searching for global optimal PID parameters. In section three of the chapter four, we integrate GA and chaos optimizing, by the use of the chaos serial's property of "ergodicity, randomicity, regularity", original population is generated; adding chaos operator to simple genetic algorithm greatly improves the local search ability, which avoids local optimization and premature convergence in effect. The results of the examples demonstrate that the chaos genetic algorithm has ideal and satisfied optimization result and much better than that of common Genetic Algorithm. The summary of the paper and personal perspective are given in the chapter five.