Towards data-based adaptive control

This paper presents a possible new direction for the design of adaptive controllers. It begins by outlining a measurement-based approach to the analysis, synthesis, and design of unknown systems. The systems under consideration can typically be electrical, mechanical, hydraulic, continuous, discrete, real, artificial, or biological, or of societal origin. Our approach is novel in the sense that we propose to use the measurement, or measured data, to directly design the control or decision variables whereas the current approach in the mainstream of system theory advocates that measured data be used to first identify the plant, and control design, or synthesis, then be based on the identified model. This latter approach has necessitated the creation of fields such as robust control and (indirect) adaptive control that try to account for errors in the identified model. Our approach, being data based, would potentially be automatically robust and adaptive. We show that such a measurement-based approach is certainly feasible by considering DC circuits, AC circuits, block diagrams, and control systems. These results are then used to formulate the research problems for data-based adaptive control.