Abstract

This paper presents the design of a Model Predictive Control (MPC) strategy for Takagi & Sugeno (TS) systems that is based on a control law with optimised prediction dynamics, first proposed in a context of Robust MPC for systems with multiplicative uncertainty. Based on the similarities between this kind of systems and state-space TS systems, this predicted control law is adapted to fuzzy models to exploit the known information of the normalised degrees of activation. It is described how to design the parameters of the controller and how to apply it closed-loop fashion. It is shown that the proposed controller is guaranteed to be recursively feasible and asymptotically stabilises the controlled systems. A simulation example shows the attributes and benefits of the proposed controller.