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Model Predictive Control and Multiparametric Linear Programming

In Model Predictive Control (MPC) at each sampling time, starting at the current state, an open-loop optimal control problem is solved over a finite horizon. At the next time step the computation is repeated starting from the new state and over a shifted horizon, leading to a moving horizon policy. The solution relies on a linear dynamic model, respects all input and output constraints, and optimizes a linear or quadratic performance index. The big drawback of MPC is the on-line computational effort which limits its applicability to relatively slow and/or small problems. It will be shown how, by using multiparametric programming, MPC for both discrete time linear time invariant systems and discrete time hybrid system (MLD) leads to piecewise linear controllers. Thus, the on-line computation is reduced to a simple linear function evaluation. The emphasis will be on a new geometric approach for multiparametric linear programming. A case study on vehicle traction control will be presented.

Type of Seminar:
Ph.D. Seminar
Borrelli Francesco
Automatic Control Lab Dept. of Electrical Engineering Physikstrasse 3 CH-8092 Zürich
May 29, 2000   16:00

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Borrelli Francesco
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