Identification for Robust Control; SISO Case

Abstract

Because of our limited knowledge about reality, mathematical models can never give an exact description of the process behaviour under study. In the identification of industrial processes, undermodelling and disturbances are the main causes of model errors. In the previous decade, robust control theory has been proposed and developed; cf. Zames (1981), Doyle (1982), Vidyasagar (1985) and Morati and Zafiriou (1989). The advantage of robust control is its capability to cope with modelling errors in the analysis and design of control systems. In order to apply robust control theory, one needs not only a nominal process model, but also a suitable description of the modelling errors. These are typically in the form of some bounds on the model parameter variations of the parametric models; or the bounds on the frequency response variations (see Section 7.2).