In this paper, an off-line synthesis approach to robust constrained model predictive control is presented. Most of the computational burdens are reduced by computing off-line a sequence of state feedback control laws that corresponds to a sequence of polyhedral invariant sets. At each sampling time, the smallest polyhedral invariant set containing the currently measured state is determined and the corresponding state feedback control law is implemented to the process. The proposed algorithm is compared with an ellipsoidal off-line robust model predictive control algorithm. The results show that the proposed algorithm can achieve better control performance. Moreover, a significantly larger feasible region is obtained. The controller design is illustrated with an example of continuous stirred-tank reactor.
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