An approach to inverse nonlinear control using neural networks

In this paper, we present a strategy for controlling a class of nonlinear dynamical systems using techniques based on neural networks. The proposed strategy essentially exploits the property of neural networks in being able to approximate arbitrary nonlinear maps when suitable learning strategies are applied. For the closed-loop control, such a network is used in conjunction with a technique of inverse nonlinear control to form what we call an inverse nonlinear controller using neural networks, abbreviated as the INC/NN controller. Properties of the controller are discussed, and it is shown that the proposed INC/NN controller allows the closed-loop error dynamics to be specified directly through a set of controller gains. Extensions of the basic INC/NN controller to incorporate integral control action, to higher order systems, and to a class of nonlinear multi-input multi-output dynamical systems are also indicated. Finally, results of some real-time experiments in applying the INC/NN controller to a position control system which has inherent nonlinearities are presented.