Iterative Learning Control And Adaptive Neural Network Control For Uncertain Nonlinear Systems

There widely exist uncertainty and nonlinearity in practice engineering. In this thesis, iterative learning controller and adaptive neural network controller are designed for some kinds of uncertain nonlinear systems, respectively. Moreover, theory analysis and simulation study are carried out. The main research work and contribution of this thesis are summarized as follows:1. An adaptive learning controller is designed for one order delay system with time-varying parameter using a composite energy function. Stability and convergence analysis and simulation study are carried out. Using a moving average estimation function of time-varying parameter and extended Backstepping technology, one novel AILC algorithm is proposed for a class of nonlinear time-delay system with periodic time-varying parameter.2. Using an integral type Lyapunov function, RBF neural network and Backstepping technology, an adaptive neural design is presented for a class of parametric-strict-feedback nonlinear systems with unknown virtual control coefficients. It has been proved that all signals in the closed-loop system are semi-global unifonnly ultimately bounded, and the output of the system converges to an arbitrarily small neighborhood of the origin. Simulation effect of three control stratagems is compared.3. Robust adaptive iterative learning controller design procedure is proposed for two kinds of nonlinear systems with unknown virtual control coefficients, using...