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Robust dynamic programming for min-max model predictive control of constrained uncertain systems

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1 Author(s)
Diehl, M. ; Interdisciplinary Center for Sci. Comput., Univ. of Heidelberg, Germany

We address min-max model predictive control (MPC) for uncertain discrete-time systems by a robust dynamic programming approach, and develop an algorithm that is suitable for linearly constrained polytopic systems with piecewise affine cost functions. The method uses polyhedral representations of the cost-to-go functions and feasible sets, and performs multiparametric programming by a duality based approach in each recursion step. We show how to apply the method to robust MPC, and give conditions guaranteeing closed loop stability. Finally, we apply the method to a tutorial example, a parking car with uncertain mass.

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Automatic Control, IEEE Transactions on  (Volume:49 ,  Issue: 12 )