Contents 1 Introduction
In recent years, model predictive control (receding horizon control) has attracted the attention of researchers [1]. Bemporad et al. have proposed the explicit controller for receding horizon control problems to reduce the computational complexity of on-line optimization [2]. This result is a breakthrough in the research of receding horizon control. The generalization of the problem is reported [3], [4] and the sub-optimal problem is developed [5]. Further it is well known that, in the practical applications, the control law is required to guarantee that the closed-loop system fulfills constraints. The robustness is important since when disturbances or model mismatch are present closed-loop performance can be poor with likely violations of the constraints and no convergence can be guaranteed. For the issue the terminal penalty and constraints play important role [6]. In [7] feedback min-max model predictive control for linear time invariant discrete-time systems is proposed and the control algorithm which guarantees a convergence to the invariant set with no constraint violation. On the other hand, hybrid systems arise in a large number of application areas, and are attracting increasing attention. The hybrid system framework allows to model a broad class of systems arising many applications and to address the cooperative control problems and reconfigure problems [8]. It is known that a class of hybrid models can be described by the piecewise linear systems.
