Introduction to Model Predictive Control for Linear and Hybrid Systems

Om kursen

In constrained control problems, states and input variables are subject to physical and/or design constraints for which traditional control laws can be inadequate. In Model Predictive Control (MPC), instead, a fixed control law is replaced by an on-line optimization performed over a receding horizon. As consequence, MPC can deal with almost any time-varying process, constraints and specifications over a future horizon, limited only by the availability of real-time computational power. In the last few years, tremendous progresses have been made. For example, methods have emerged to handle hybrid systems, i.e., systems comprising both continuous and discrete components. Moreover, it is now possible to perform most of the MPC computation off-line thus reducing the control law to a simple look-up table.

The first part of the course is an overview of advanced concepts of system theory and optimization, including hybrid systems and multi-parametric programming. In the second part we will show how these concepts are utilized to derive MPC algorithms and to establish their properties.

The course will be given in Lp1, with four hours of lecture per week, on Tuesdays and Thursdays, 10 am-12 pm in Lunnerummet and weekly homeworks. Prerequisites are basics of optimization, matrix analysis, theory of linear systems and basic and optimal control theory.

Mer information

Paolo Falcone
Telephone: 031-772 1803


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