Robust output feedback model predictive control using off-line linear matrix inequalities |
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| Authors: | Zhaoyang Wan Mayuresh V Kothare |
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| Affiliation: | 1. School of Electro-Mechanical Engineering, Xidian University, China;2. State Key Laboratory of Intelligent Manufacturing System Technology, Beijing Institute of Electronic System Engineering, Beijing, China;3. College of Automation, Chongqing University of Posts and Telecommunications, China;4. Inria Lille-Nord Europe, France;1. Division of Automatic Control, Department of Electrical Engineering, Linköping University, Linköping, Sweden;2. LAAS-CNRS, Université de Toulouse, CNRS, Toulouse, France;1. Institute for Automation Engineering, Otto-von-Guencke-Unwersity Magdeburg, Germany |
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| Abstract: | A fundamental question about model predictive control (MPC) is its robustness to model uncertainty. In this paper, we present a robust constrained output feedback MPC algorithm that can stabilize plants with both polytopic uncertainty and norm-bound uncertainty. The design procedure involves off-line design of a robust constrained state feedback MPC law and a state estimator using linear matrix inequalities (LMIs). Since we employ an off-line approach for the controller design which gives a sequence of explicit control laws, we are able to analyze the robust stabilizability of the combined control laws and estimator, and by adjusting the design parameters, guarantee robust stability of the closed-loop system in the presence of constraints. The algorithm is illustrated with two examples. |
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| Keywords: | Model predictive control Linear matrix inequalities Asymptotically stable invariant ellipsoid Output feedback Robust stability |
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