On the Approximation of Constrained Linear Quadratic Regulator Problems and their Application to Model Predictive Control

Michael Muehlebach & Raffaello D'Andrea
This article is concerned with the approximation of constrained continuous-time linear quadratic regulator problems, which are, for example, encountered in model predictive control. By representing input and state trajectories using basis functions, the underlying infinite-dimensional optimal control problems are reduced to convex finite-dimensional optimization problems that can be solved efficiently. The article quantifies the suboptimality and establishes convergence of the obtained approximations. The results are applied in the context of model predictive control. In particular,...
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