Constrained feedback RMPC for a category of LPV systems

For a category of linear parameter varying (LPV) systems, i.e. LPV systems with both bounded rates of parameter variations and parameter measurement errors, the approach to design the feedback robust model predictive control (RMPC) is studied. The proposed controller utilizes the information on system parameters so as to improve the control performance, where the LPV system model is transferred into a sequence of future models with parameter-incremental uncertainty to include both the parameter variations and the parameter measurement. Then, a sequence of feedback control laws is designed to correspond to the sequence of future models. Since the information on system parameters is utilized and the control actions will vary corresponding to the future variations of system parameters, the better control performance can be achieved. The recursive feasibility and closed-loop stability of the proposed RMPC are also proven.