APPROXIMATE IDENTIFICATION WITH CLOSED-LOOP PERFORMANCE CRITERION AND APPLICATION TO LQG FEEDBACK DESIGN

A model-based controller generally works better with the model than with the modelled plant due to the modelling error. This difference between the performances can be made small by selecting a model that is accurate at the closed-loop relevant frequencies. In this paper it is shown that an iterative approach of identification and control design can lead to a model that is much better suited for feedback design than a model resulting from an unweighted open-loop identification. In this iteration each identification is performed such that a certain closed-loop criterion function is minimized. This is accomplished by closed-loop identification with persistent set-point excitation and a proper signal filtering Each control design step employs the latest identified model to construct an LOG compensator. The performance requirements are gradually increased during the iteration.