document: Robust output feedback model predictive control using off-line linear matrix inequalities A fundamental question about model predictive control -LRB- MPC -RRB- 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 -LRB- LMIs -RRB- . 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
doc_bio_tags: B I I I I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I I I I O O O O O O B I O O O O O O O O O O O O B I I I I I O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O
extractive_keyphrases: robust output feedback model predictive control robust constrained output feedback mpc algorithm polytopic uncertainty robust constrained state feedback mpc law state estimator
abstractive_keyphrases: off-line linear matrix inequalities model uncertainty robustness norm-bound uncertainty controller design procedure explicit control law sequence closed-loop system asymptotically stable invariant ellipsoid