Optimal self-tuning control systems: theory and application

This paper covers the estimation aspects of self-tuning theory and examines the success of the methodology on a dynamic ship-positioning problem. The parameter-estimation algorithms for self-tuning are given their conceptual roots in the theory of statistics, particularly the properties of sufficiency, unbiasedness and efficiency. The particular system description is manipulated into a form suitable for the application of the discrete Kalman filtering methodology. Finally, recursive estimation formulae are given, and their numerical implementation is discussed. Some remarks on suitable rules for forgetting factors are also given. Bringing together the control and estimation theory results in the self-tuning controller per se. The two types: self-tuning with explicit, or implicit identification are considered in some detail. Finally, the application of a self-tuning controller to a dynamic ship-positioning problem is presented. The characteristic adaptation by the controller to different wave conditions is illustrated.