A simple nonlinear controller with diagonal recurrent neural network

A simple control law analogous to the linear generalized minimum variance ( GMV) control is presented for the general unknown nonlinear dynamic process es. With this control law, the iterative search of the control input, which is often encountered in the nonlinear control, can be eliminated, resultin g in an efficient computation for real-time implementation. The implementat ion of this control law requires two key quantities to be calculated: the i nput-output sensitivity function and the quasi-one-step-ahead predictive ou tput. The selection of a diagonal recurrent neural network (DRNN) as the pr ocess identifier allows a direct estimation of these two quantities, result ing in the proposed control law to be implemented in a straightforward mann er. Both simulation and experiment are given to demonstrate the effectivene ss of the proposed control algorithm. (C) 1999 Elsevier Science Ltd. All ri ghts reserved.