Control of a static nonlinear plant using a neural network linearization

Abstract

One possibility to control a static plant is the design of a controller based on the inverse of an identified model. For nonlinear plants, determining or identifying the plant model may be a difficult task. When a state space model of the plant is not explicitly needed, it is possible to consider the plant as a black box and approximate the plant using neural networks. In this paper a control strategy is presented, based on the combination of classical linear control methods with a neural network that inverses the plants nonlinear characteristics. A proof is given that the plant can be positioned with an arbitrary small positioning error. The method is experimentally illustrated on the positioning control of a flexible robot arm. The results of the neural network based control are compared with a PI controller.