Based Method Of Uncertain Nonlinear Systems, Adaptive Neural Network Control

In the recent years, it has developed greatly in the control theory research and application of control design for nonlinear systems, and people have gained tremendous progress in this field. Owing to the complexity of the modern engineering system and some inevitable uncertainties in-cluding the modeling error, parameter time delay, uncertain parameters, unmodeled dynamics and disturbance, We fail to acquire the satisfied solution and many conclusions of modern con-trol theory can't work effectively in practical engineering application. So uncertain nonlinear system control research has been retaining a certain hot spot up to now.Adaptive theory is a nonlinear control methodology, which is particularly useful for control of highly uncertain nonlinear system and complex systems.In recent years, with the develop-ment of the nonlinear backstepping adaptive technique and neural networks, in the design and analysis of uncertain nonlinear control system, the adaptive neural network control based back-stepping approach has become the highlight in the control theory, and become a new re-search direction in the neural network control field. Some correlative issues in this area are stud-ied in this paper. An adaptive neural network control scheme which employs the back-stepping technique based on Lyapunov stability theory and neural network control theory for a class of SISO uncertain nonlinear system is designed in this paper.The thesis consists of the following five papers:In chapter 1,introduce the background of the topic, the research situations and the research purpose and significance of the uncertain nonlinear adaptive control.In chapter 2,introduce the basis theory involved in the paper,including the basis concepts, the main lemma and the important inequalities.Such as Lyapunov stability theory, the backstep-ping control theory and the inequalities used frequently in the paper.In chapter 3,an adaptive neural networks control approach was developed for a class of SISO uncertain nonlinear systems using the back-stepping technique.,and realize the semi-global boundedness for all signals and at the same time, the tracking error is as small as possible.In chapter 4, consider a class of nonlinear system with uncertain control directions, an adap-tive neural networks controller was proposed using the neural network approximation princi-ple,the Lyapunov stability theory and the back-stepping technique,and realize the semi-global boundedness for all signals and at the same time, the tracking error is as small as possible.In chapter 5,we summarize the dissertation and discuss open problems for future research.