Adaptive Control Based On Neural Network

Abstract

Neural network has good nonlinear function approximation ability. It can be widely used to identify the model of controlled plant. In this chapter, the theories of modeling uncertain plant by using two kinds of neural networks: feed-forward neural network and recurrent neural network are introduced. And two adaptive control strategies for robotic tracking control are developed. One is recurrent fuzzy neural network based adaptive control (RFNNBAC), and another is neural network based adaptive robust control (NNBARC). In RFNNBAC, a kind of recurrent fuzzy neural network (RFNN) is constructed by using recurrent neural network to realize fuzzy inference, In which, temporal relations are embedded in the network by adding feedback connections on the first layer of the network. Two RFNNs are used to identify and control plant respectively. Base on the Lyapunov stability approach, the convergence of the proposed RFNN is analyzed. In NNBARC, A robust controller and a neural network are combined into an adaptive robust robotic tracking control scheme. Neural network is used to approximate the modeling uncertainties in robotic system. Then the disadvantageous effects on tracking performance, due to the approximating error of the neural network and non-measurable external disturbances in robotic system, are attenuated to a prescribed level by robust controller. The robust controller and the adaptation law of neural network are designed based on Hamilton-JacobiIssacs (HJI) inequality theorem. The weights of NN are easily tuned on-line by a simple adaptation law, with no need of a tedious and lengthy off-line training phase. This chapter is organized in the following manner. In the first section a robust robotic tracking controller based on neural network is designed and its effectiveness is proved by applying it to control the trajectories of a two-link robot. Secondly, a recurrent fuzzy neural network based adaptive control is proposed and simulation experiments are made by applying it on robotic tracing control problem to confirm its effectiveness. Finally, some conclusions are drawn.