ADAPTIVE CONTROLLER DESIGN FOR UNKNOWN SYSTEMS USING MEASURED DATA
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2015 Chinese Automatic Control Society and John Wiley & Sons Australia, Ltd This paper presents a measurement-based adaptive control design approach for unknown systems working over a wide range of operating conditions. Traditional control design approaches usually require the availability of a mathematical model. However, it has been shown in many practical situations that, due to complex dynamics of physical systems, some simplifying assumptions are made for the derivation of mathematical models. Hence, controller design based on simplified models may result in degradation of the desired closed-loop performance. Data-based control design approaches can be viewed as an alternative approach to model-based methods. Most data-based control methods available in the literature aim to design controllers for unknown systems that operate only at a given operating point. However, the dynamical behavior of plants may change for different operating conditions, which makes the task of designing a controller that works over the entire range of operating conditions more challenging. In this paper, we address such a problem and propose to design adaptive controllers based on measured data. Such a proposed method is based on designing a set of measurement-based controllers validated at a finite set of pre-specified operating points. Then, the parameters of the adaptive controller are obtained by interpolating between the set of pre-designed controller parameters to derive a gain-scheduling controller. Moreover, low-order adaptive controllers can be designed by simply selecting the desired controller structure. The efficacy of the proposed approach is experimentally validated through a practical application to control a heating system operated over a large range of flow rate.