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New ideas towards real-time suboptimal MPC with stability and performance guarantees


M.N. Zeilinger

IfA Internal Seminar Series, Zurich

The model predictive control (MPC) problem is in general either solved by computing the optimal controller online or by deriving an explicit control law. In many real problems however, limits on the storage space or the computation time restrict the applicability of model predictive controllers. In order to overcome the limitations, several authors recently proposed new approaches to speed up online optimization or to reduce the complexity of explicit solutions. In this talk I will present new ideas in approximate MPC that aim at enlarging the possibilities to tradeoff solution properties. I will first introduce a new approach combining the two paradigms of explicit and online MPC to overcome their individual limitations. The algorithm computes a piecewise affine approximation of the optimal solution that is used to warm-start an active set linear programming procedure. A preprocessing method is introduced that provides hard real-time, stability and performance guarantees for the proposed controller. By choosing a combination of the quality of the approximation and the number of online active set iterations the presented procedure offers a tradeoff between the warm-start and online computational effort. The problem of identifying the optimal tradeoff for a given set of requirements on online computation time, storage and performance is addressed. I will then discuss the limitations and possible future extensions of the proposed method. At the end of the talk, ideas for future work will be presented that are inspired by the advantageous properties of interior-point methods. A brief analysis of available methods and their possible usage for computing approximate MPC controllers conclude the talk.


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