Control of Nonlinear System with a Disturbance Using Multilayer Neural Networks

The mathematical solutions of the stability convergence are important problems in system control. In this paper, such problems are analyzed and resolved for system control using multilayer neural networks. We describe an algorithm to control an unknown nonlinear system with a disturbance, using a multilayer neural network. We include a disturbance among the modeling error, and the weight update rules of multilayer neural network are derived to satisfy Lyapunov stability. The overall control system is based upon the feedback linearization method. The weights of the neural network used to approximate a nonlinear function are updated by rules derived in this paper . The proposed control algorithm is verified through computer simulation. That is, as the weights of neural network are updated at every sampling time, we show that the output error become finite within a relatively short time.