ABSTRACT
Model predictive control (MPC), also known as receding horizon control, is widely used in industrial processes, especially in processes with slow dynamics, for it is able to deal with physical constraints and multivariable systems efficiently in an online optimal way. Typically, based on an explicit plant model and information about input and output constraints, MPC solves a constrained optimization problem online at each time instant and implements the first element of the optimal control profile resulting from the optimization procedure. The process is repeated at the next time instant with the updated new state. One of the most appealing features of MPC is that the transient control performance of model predictive control systems can be adequately addressed in terms of certain optimal performance cost. In this chapter, two approaches to model predictive controller synthesis of T-S fuzzy systems are presented.
