Iterative controller optimization for nonlinear systems

Recently, a data-driven model-free control design method has been proposed in Hjalmarsson et al. (Proceedings of the Conference on Decision and Control, Orlando, FL, 1994, pp. 1735-1740; IEEE Control Systems Mag. 18 (1998) 26) for linear systems. It is based on the minimization of a control criterion with respect to the controller parameters using an iterative gradient technique. In this paper, we extend this method to the case where both the plant and the controller can be nonlinear. It is shown that an estimate of the gradient of the control criterion can be constructed using only signal-based information obtained from closed-loop experiments. The obtained estimate contains a bias which depends on the local nonlinearity of the noise description of the closed-loop system which can be expected to be small in many practical situations. As a side effect the linear model-free control design method is reobtained in a new way.