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A linear programming approach to constrained robust predictive control

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2 Author(s)
Y. I. Lee ; Div. of Electr. & Electron. Eng., Gyeongsang Nat. Univ., Gyeong-Nam, South Korea ; B. Kouvaritakis

A receding horizon predictive control algorithm for systems with model uncertainty and input constraints is developed. The proposed algorithm adopts the receding horizon dual-mode (i.e., free control moves and invariant set) paradigm. The approach is novel in that it provides a convenient way of combining predictions of control moves, which are optimal in the sense of worst case performance, with large target invariant sets. Thus, the proposed algorithm has a large stabilizable set of states corresponding to a cautious state feedback law while enjoying the good performance of a tightly tuned but robust control law. Unlike earlier approaches which are based on QP or semidefinite programming, here computational complexity is reduced through the use of LP

Published in:

IEEE Transactions on Automatic Control  (Volume:45 ,  Issue: 9 )