NONLINEAR AUTO-LEARNING CONTROL USING MULTI-NEURAL NETWORKS

This paper proposes a multi-network auto-learning control structure of the unknown nonlinear dynamic system. The generalized Delta rule of the controller network has been developed based on the identifier of the inverse dynamics.The controller architecture is presented with simulations demonstrating itsadaptive and learning abilities.It is also shown in the simulation that the maximum tracking error is within 1% after 100 periods.