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Daily Overview |
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OC5: Optimisation and Control Location: A702 Session Chair: Behzad Azmi | |
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Reduced Order Model Predictive Control for Parametrized Partial Differential Equations RWTH Aachen, Germany Model Predictive Control (MPC) is a well established approach to solve infinite horizon optimal control problems. Since optimization over an infinite time horizon is, in general, infeasible, the method determines a suboptimal feedback control by repeatedly solving finite time optimal control problems. In this talk, we consider systems governed by parametrized parabolic partial differential equations and employ the reduced basis method (RB) as a low-dimensional surrogate model for the finite time optimal control problem. The reduced order optimal control serves as the feedback control for the MPC of the original large-scale system. We briefly recall the RB-MPC approach for linear systems which guarantees asymptotic stability of the closed-loop system. We then extend these results to nonlinear sytems based on the Schlögl model and problems with distributed controls. Numerical results are presented to validate our approach. | |
