Fast neural learning and control of discrete-time nonlinear systems

The problem of learning control for a general class of discrete-time nonlinear systems is addressed in this paper using multilayered neural networks (M"s) with feedforward connections. A suitable extension of the concept of input-output linearization of discrete-time nonlinear systems is used to develop the control schemes for both output tracking and model reference control purposes. The ability of MNNs to model arbitrary nonlinear functions is incorporated to approximate the unknown nonlinear input-output relationship and its inverse using a new weight learning algorithm. In order to overcome the difficulties associated with simultaneous on-line identification and control in neural networks based adaptive control systems, the new learning control architectures are developed for both adaptive tracking and adaptive model reference control systems with on-line identification and control ability. The potentials of the proposed methods are demonstrated by simulation examples.