System Modelling And Control Based On Chaotic Optimization And Support Vector Machines

Recently, computational intelligence theory and method, such as fuzzy logic, artificial neural networks, genetic algorithms, chaotic optimization algorithms (COA) and support vector machines (SVM), has been a hot research area in electronic engineering, automation, computer science both outside and inside our country. At the mean time, there are many great progresses in the research of computational intelligence, especially in the control system design. This paper focuses on two kinds of computational intelligence, that is, COA and SVM, for modelling and controller design of nonlinear systems.Firstly, this paper introduces the relative knowledge about chaos, and describes COA in detail. For improving the search ability and convergence of COA, this paper proposes a improved search algorithm which is the combination of parallel COA and simplex method. Simplex method has good local search ability, thus it is used for improving the local search ability for parallel COA in this proposed algorithms. The capability of the proposed algorithms is analysed and it is applied in system identification with diverse types.Thereafter, this paper introduces basic knowledge about SVM, another computational intelligence method, in the views of classification and regression. Then the main learning algorithm of SVM is presented in detail.As SVM have good ability for nonlinear system approximation, in this paper, it is employed for system identification as well as its inverse model, this is the base for controller design.In succession, this paper focuses on the design of inverse model control system using SVM, and compares three kinds of inverse model control based on inverse model, that is, direct inverse control, PID compensated inverse model control. The control capability of these two controllers are simulated which validate the performance of controllers.As fuzzy inference system(FIS) has poor self-learning ability, for the improving the learning ability of FIS, this paper presents a SVM-FIS self-learning controller. Both grads descending algorithm and chaotic optimization for the training of SVM-FIS self-learning controller are presented. Simulations show that the proposed controller has good performance.