Iteration Tuning of Disturbance Observer-Based Control System Satisfying Robustness Index for FOPTD Processes

A conventional disturbance observer (DOB)-based control system is a model-based control system. In this paper, a data-driven design method is proposed for the DOB-based control system using iteration feedback tuning (IFT) based on the first-order-plus-time-delay models. It is well known that the conventional data-based design methods cannot provide an explicit tradeoff between robustness and performance. Here, our goal is to develop a method to design the data-driven DOB-based control system satisfying a given robustness index. To this end, first, the tuning rules of the controller and the Q-filter in terms of the nominal process model are analytically determined for a given robustness index. Second, an optimization problem, solved by IFT algorithm, is established to find the optimal parameters of the nominal model. The merits of the proposed method are that: 1) the number of parameters needing to be tuned is reduced, since only the parameters of the nominal model are optimized and 2) the system satisfies the explicit robustness index if the parameters are optimal. Moreover, the selection of the robustness index, the output performance of the system, and the performance of the iteration algorithm are addressed. Two simulation examples and an experiment are presented to demonstrate the effectiveness and merits of the proposed method.