An Off-line RMPC Algorithm for Uncertain Systems Using Polyhedral Invariant Sets

摘要

Based on the polyhedral invariant sets, an off-line robust model predictive control algorithm is developed for an input-constrained and output-constrained uncertain linear discrete system. First, a sequence of discrete states is chosen to compute the corresponding state feedback control laws, and also construct each polyhedral invariant set. At each sampling time, the smallest polyhedral invariant set that the current measured state can be embedded is determined. Implementing the continuous state feedback control laws based on the position of the current measured state between the adjacent polyhedral invariant sets. Simulation results show that, compared to the ellipsoidal off-line RMPC algorithm, the proposed algorithm yields a substantial expansion of the region can be stabilized, achieve a less conservative result.