Control horizon estimation of input constrained MPC based on control saturation

This paper gives two new algorithms for estimating the upper bound of the control horizon for input constrained model predictive control (MPC) problem based on the input saturation and the K-step null-controllable region. The input saturation must occur at one time, which is considered in estimating the control horizon. The state within the polytope can be expressed by a convex combination of the vertices of one facet of the polytope and these vertices are considered to compute the global upper bound. The proposed algorithms are compared with others by an example and the results show that the methods of this paper have the less conservative estimation of the control horizon.