On the Approximation of Constrained Linear Quadratic Regulator Problems and their Application to Model Predictive Control - Supplementary Notes
Michael Muehlebach, Raffaello D'Andrea
TL;DR
This paper discusses how different basis function parametrizations can approximate constrained linear quadratic regulator problems, with implications for model predictive control applications.
Contribution
It provides technical results on trajectory parametrizations that enhance the approximation of constrained LQR problems for MPC.
Findings
Various basis functions improve approximation accuracy
Results are applicable to MPC implementations
Technical insights support future research in constrained control
Abstract
By parametrizing input and state trajectories with basis functions different approximations to the constrained linear quadratic regulator problem are obtained. These notes present and discuss technical results that are intended to supplement a corresponding journal article. The results can be applied in a model predictive control context.
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Taxonomy
TopicsAdvanced Control Systems Optimization · Stability and Control of Uncertain Systems · Control Systems and Identification