SUMMARYThis paper addresses the problem of controlling a continuous-time linear system with large modelling errors. We employ an adaptive control algorithm consisting of a family of linear candidate controllers supervised by a high-level switching logic. Methods for constructing such controller families have been discussed in the recent paper by the authors. The present paper concentrates on the switching task in a multiple model context. We describe and compare two di!erent switching logics, and in each case study the behaviour of the resulting closed-loop hybrid system.

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Recently, a data-driven model-free control design method has been proposed in (7; 6) 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 re-obtained in a new way.