|dc.description.abstract||Progress in the advancement of control techniques has been mainly due to stringent design requirements, and the need to meet these requirements with less precise apriori knowledge of the plant under study. That is, the necessity to control a plant under increased uncertainty has been responsible for the evolution of control schemes. No general analytical solution for a system operating under uncertain conditions can be determined. This, therefore, necessitates the design of an adaptive controller with learning and adaptation features.
In the existing learning methodologies, controlling a given plant follows the learning phase, that is, learning and controlling are two distinct phases. In order to unify the above two phases into one phase, 'learning-while-functioning', a different approach called Inverse-Dynamics Adaptive Controller (IDAC) has been proposed in this thesis. In this approach, the controller is made to be an inverse-dynamics model of the plant under study. The concept of the inverse-dynamics approach, and the necessary algorithm for this technique, are developed in this thesis. The results of extensive computer simulation studies are given which detail the performance of the IDAC. From these results, it is observed that a controller designed using the inverse-dynamics approach can learn and control a given plant. Also, learning and control are achieved simultaneously. At each learning trial the plant is directed towards a desired performance. The use of the IDAC for control purposes is rather a direct approach in contrast to the conventional techniques using optimization theory. Furthermore, the inverse-dynamics adaptive control scheme is independent of the type of plant to be controlled, however, in this thesis, only linear plants considered.||en_US