Design And Optimization Of Fuzzy Controller Based On Improved PSO

During the past recent years, the research on fuzzy control has been developed a lot, and a varity of fuzzy controller design methods have been used in industry. Meanwhile, people are not only hoping to obtain satified fuzzy controller using easier design methods, but also expecting them to have better control performance. Thus, design and optimization of fuzzy controller has now been developed into a research topic in the fuzzy control research field. Among all the methods to design and optimize the fuzzy controller, intelligent optimization algorithm is a quite efficient one.Intelligent optimization algorithms achieve the optimization goal through imitating the mechanism of the nature. Among them, Particle Swarm Optimization (PSO) has received much attention for its prominent global search ability, local sesarch ability and robustness, and it has been widely used in the industrial production process. Also, numerous researches aim to further improve PSO's optimization ability.First, this paper has done research on PSO and its improved algorithm, i.e. Quantum Behaved Particle Swarm Optimization (QPSO). On this basis, this paper draws immune operator into QPSO and proposes Quantum Behaved Particle Swarm Optimization–Immune (QPSO-IM), thus further improves QPSO's optimization ability. Through tests using multiple hump functions, it has been improved that QPSO-IM has strong superiority in convergency speed, optimization results and high-dimension search ability, to either PSO or QPSO.Furthermore, for the design of fuzzy controller, this paper proposes QPSO-IM based Clustering Algorithm (QPSO-IMCA) which is on the basis of QPSO-IM, and also proposes a special coding method that makes the iterative clustering algorithm more independent to its initial state. Through tests using Iris and Glass data set, this paper proves QPSO-IMCA's superiority in clustering analysis ability to either QPSO or PSO-based methods. Then, on the basis of the proposed clustering algorithm, this paper designs and develops a user-friendly fuzzy controller CAD platform, and then introduces its design principle and the design procedure.To further improve the fuzzy controller's control peformance, this paper proposes to optimize the fuzzy control table of the fuzzy controller using QPSO-IM. Considering the dependent of the fuzzy control table optimization to the model of the plant, this paper proposes QPSO-IM based Fuzzy Identification (QPSO-IMFI) algorithm. Then, based on the proposed optimization strategy and the analysi of QPSO-IMFI, this paper integrites the fuzzy identification and optimization function into the fuzzy controller CAD platform, and then introduces its design principlen and design procedure.Finally, through the experiments of controlling the single container water tank level and the oven temperature, this paper proves the effectiveness of all the proposed methods and the practicability of the developed fuzzy controller CAD platform.