Intelligent control using multiple models and neural networks

Most adaptive control algorithms for nonlinear discrete time systems become invalid when the controlled systems have non-minimum phase properties and large uncertainties. In this paper, an intelligent control method using multiple models and neural networks (NN) is developed to deal with those problems. The proposed control method includes a set of fixed controllers, a re-initialized neural network (NN) adaptive controller and a free-running NN adaptive controller. The bounded-input-bounded-output (BIBO) stability and performance convergence of the system are guaranteed by the free-running adaptive controller, while the multiple fixed controllers and the re-initialized adaptive controller are used to improve the transient response. Simulation results are presented to demonstrate the effectiveness of the proposed method. Copyright (C) 2007 John Wiley & Sons, Ltd.