Adaptive Fuzzy Tracking Control Of Nonlinear Systems

The phenomenon of nonlinearity and time delay often occur in the real world and modern industrial control systems. Nonlinearity, uncertainty and time delay usually degrade controlled system performance, and lead to quite inconvenience to the application of control theory methods. So, this thesis is concerned with adaptive fuzzy control for a class of nonlinear systems with uncertainties and time delays. Fuzzy logic systems are used to approximate the unknown nonlinear functions, the adaptive fuzzy tracking controller is constructed by the backstepping technique and classical adaptive technology. The proposed adaptive fuzzy controller guarantees that all the signals in the resulting closed-loop system are bounded and the system output eventually converges to a small neighborhood of the desired reference signal. An advantage of the proposed control scheme lies in that the design of fuzzy controller does not involve the fuzzy bases vector functions. Therefore, the proposed control scheme is easily implemented to control system, and the online computational burden will be reduced considerably.The main content of the thesis are listed as following three components:The first part is concerned with the basic knowledge of this paper.The second part is concerned with the problem of adaptive fuzzy tracking for a class of nonlinear systems with the uncertainties, fuzzy logic systems are used to approximate the unknown nonlinear functions.The third part is concerned with the problem of adaptive fuzzy tracking for a class of nonlinear systems with the uncertainties and time delay. The control deign scheme proposed in the second part is extended to delayed nonlinear systems.Although the above systems are different, all adaptive fuzzy controllers of these different systems are designed based on adaptive technologyand the backstepping method. The proposed adaptive fuzzy controller guarantees that all the signals in the closed-loop system are bounded and the system output eventually converges to a small neighborhood of the desired reference signal. Finally, some numerical examples are used to demonstrate the effectiveness of our results proposed in this paper.