Adaptive Neuro-Fuzzy Control For Uncertain Chaotic System

Since the early 1990s, the problems of chaos control attract the attention of the researchers particularly after OGY method found. There are many methods of chaos control, such as continuous feedback control, adaptive control, intelligent control, parameter perturbation control, and so on.In this paper, uncertain Duffing chaotic systems as the research object and in order to effectively achieve the tracking control of uncertain chaotic systems. The advantages and disadvantages of fuzzy control, neural network control and adaptive control were analyzed. And ANFIS-based fuzzy neural network adaptive control was proposed. And make the MATLAB/Simulink as the simulation platform. The adaptive fuzzy neural network controller is applied to chaos control system to verify the feasibility of control strategies.The content of this research thesis mainly included the following aspects:(1)Explained the background of chaos control and introduced several chaos control method and domestic and international research profile, and study the meaning and application prospects. Brief introduced the concept and characteristics of chaos, and criterion for chaos, and several chaotic systems. The composition of fuzzy controller and the basic theory of neural networks were introduced. The combination of fuzzy theory and neural network was introduced also. And highlighted analyzed the model reference adaptive control system.(2)Based on the theory of adaptive control, an adaptive controller for uncertain Duffing chaotic system is presented. Simulation results showed that control systems have stronger ability to track given signal and restrain random noise, and proved the feasibility of the control method.(3)The ANFIS controller applied to the parameters uncertain Duffing chaotic system. The chaotic system is stabilized to any given destination with unnecessary to slove the unfixed points in system. The controller system could track the sinusoidal reference signal accurately under the presence of parameter perturbations and stochastic noise cirumstances, with less tracking error and stronger robustness in system, which has a broad application prospect.