H∞ tracking of unknown nonlinear systems using neural network

A stable neural adaptive control scheme, is proposed to achieve H ^∞ performance for a class of unknown nonlinear SISO systems with external disturbances. In the control design, the controller comprises a certainty equivalence control term and an H^∞ compensating term. The neural network is used to approximate the unknown nonlinear functions for the design of the equivalence controller, and the Lyapunov method is used for the update of the parameters of the neural network, the stability of the proposed control algorithm can be guaranteed. The performance of the proposed method is demonstrated through the control of the inverted pendulum system