Multi-model Adaptive Control for a Class of Nonlinear System Based on Neural Networks

For a class of discrete time dynamical systems, an adaptive control scheme is proposed based on neural networks and multi-model. By designing a reasonable switching law among the models, the merits of linear robust adaptive controller and a neural networks based nonlinear adaptive controller can be well integrated, such that the best controller can be selected for the system at anytime. The control of stability and performance improving can achieve respectively, which not only guarantees the stability, but also improves the adaptive control performance by using neural network controller. Finally, it is demonstrated that improved performance and stability can be simultaneously achieved by simulation examples.