LAUSR

Abstract Many control problems contain time-varying or parameter-varying dynamics. With model predictive control (MPC), it is possible to include known plant variations into the controller for improving control performance. Unfortunately, perfect knowledge of the plant is rarely available and the accurateness of models may change over time and operating points. Robust control approaches consider worst-case realizations with a static model which ensure constraint satisfaction and stability but may yield conservative performance. The control algorithm presented in this paper uses anticipative information about future uncertainties and varying models to improve control performance while ensuring stability and feasibility. Possible system trajectories are bounded by polytopic tubes and recursive feasibility is achieved by the use of a terminal set. The controller properties are evaluated in a numerical example and compared to a similar control algorithm.