Hybrid predictive control of constrained linear systems

Abstract

Model Predictive Control (MPC), also known as receding horizon control(RHC), is a popular control method for handling constraints (both on manipulated inputs and state variables) within an optimal control setting [231]. In MPC, the control action is obtained by solving repeatedly, on–line, a finite–horizon constrained open–loop optimal control problem. The popularity of this approach stems largely from its ability to handle, among other issues, multi–variable interactions, constraints on controls and states, and optimization requirements, all in a consistent, systematic manner. Its success in many commercial applications is also well–documented in the literature (see, for example, [99, 223]). These considerations have motivated numerous research investigations into the stability properties of model predictive controllers and led to a plethora of MPC formulations that focus on closed-loop stability (see, for example, [68, 101, 145, 228] and the review paper [191]).