Off-line self-scheduled robust predictive tracking control of linear parameter-varying systems

This paper presents a novel robust model predictive control (RMPC) method for a class of plants with fast time-varying parameters, strong uncertainty, and large operating envelope, which means the states of the plants vary widely. First, a linear parameter-varying model is built to describe model uncertainty and the large envelope. The model is of polytopic form augmented with integrals of tracking errors; the uncertainty of the model vertices is considered. Second, a set of RMPC controllers are designed for each polytope vertex sharing the same asymptotic stable invariant set. During the design, an additional stability constraint should be satisfied. With the additional constraint, global robust stability can be proven with Lyapunov theory. Lastly, a sample application of Rockwell Space Shuttle's attitude controller is provided. Simulation results reveal that the tracking error of off-line self-scheduled RMPC controller is smaller than the RMPC controller, and it also shows a convergence trend, while the RMPC controller's does not.