Stable neural controller design based on composite adaptation

An indirect learning and direct adaptive control scheme based on neural networks-composite adaptive neural control-is proposed for a class of nonlinear systems. With indirect learning, the neural network learns the nonlinear basis functions of the system inverse dynamics by a modified backpropagation learning rule. The basis set spans the locally partitioned vector space of inverse dynamics with direct adaptation when indirect learning is achieved within a prescribed error tolerance. For localization of the state space of inverse dynamics, the hash addressing technique from CMAC is used for selecting only a small subset of the network hidden nodes according to where the input vector lies. As such, the global control performance can be obtained by the cooperation of many local convergence properties. For uniform stability, sliding mode control is introduced when the neural network has not sufficiently learned the plant dynamics. With suitable assumptions on the controlled plant, global stability and tracking error convergence proof can be given. Finally, the proposed control scheme is verified with computer simulation