Department of Civil and Industrial Engineering. University of Pisa
Tuesday, November 24, 2015, 11:00 - 12:00
Room 01-012, Georges-Köhler-Allee 102, Freiburg 79110, Germany
The role of feedback in the design of effective control systems is central. In Model Predictive Control (MPC), an optimal control problem is solved at each decision time (mainly) because a new state measurement (or estimate) becomes available. Feedback is necessary to reduce the effect of disturbances and to cope with unavoidable modeling errors. Nonetheless, the way in which feedback is used to achieve offset-free tracking in the presence of persistent errors or disturbances appears to be otien a question of personal preference among possible different
methods. The general goal of this talk is to describe in a tutorial way this aspect of MPC theory and design, which is otien overlooked in academic papers but is fundamental for actual implementation. General formulations of offset-free MPC algorithms are based on disturbance models and observers. First, we present a comprehensive description of the available results on offset-free nonlinear MPC, and then we show new results on the asymptotic convergence of the estimator. Then, we extend the concepts of offset-free estimation for nonlinear MPC to design an economic MPC algorithm that is able to cope with persistent errors while still achieving the optimal ultimate economic performance. Next, the offset-free linear MPC design is discussed to show that several alternative offset-free MPC algorithms are special cases of the general disturbance model/observer method. Extensive application results are presented to show the benefits of offset-free MPC algorithms over standard ones, and to clarify misconceptions and design errors that can prevent constraint satisfaction, closed-loop stability, and offset-free performance.