A measurement-based approach for tuning of reduced-order controllers

This paper presents a new measurement-based technique to design fixed-structure controllers for unknown SISO systems. Most control design approaches existing in the literature are based on parametric models. Such models can be obtained using physical laws or via system identification using a set of measured data. Model-based control approaches have proven successful only when mathematical models used in the controller design provide a perfect description of the physical system behavior. In many practical situations, it has been shown that the derivation of models using either physical laws or system identification is usually based on simplifying assumptions due to complex dynamics characterizing real systems. Hence, the use of such simplified models in the design process may result in closed-loop performance deterioration. In this paper, an alternative approach is proposed to design controllers based on measured data without the need for model identification. Our proposed technique is novel in the sense that it can be applied even when the controller placement within a complex control system configuration is unknown. The principle of the proposed control methodology is to find the controller parameters so that the closed-loop frequency response is close to a desired frequency response. This problem is formulated as an error minimization problem. The main advantages of our proposed approach are: 1) its applicability to any complex and unknown control system configuration and without the use of any mathematical model, and 2) the design process is based on a pre-selected controller structure, which allows for the selection of low-order controllers. For simulation purposes, a PID measurement-based controller is designed to illustrate the feasibility and the efficacy of the proposed technique.