On Piecewise Quadratic Terminal Costs for MPC
For control engineers designing linear MPC, this provides a way to improve performance and stability guarantees without increasing computational complexity.
This paper introduces a method for synthesizing stabilizing terminal ingredients in linear MPC that increases the region of attraction and reduces suboptimality relative to the infinite-horizon optimal control problem, achieving exact LQR cost in a nontrivial neighborhood of the steady-state.
This paper presents a novel approach to synthesize stabilizing termi- nal ingredients for linear model predictive control (MPC) schemes, with the aim of increasing the region of attraction while reducing suboptimal- ity with respect to the solution of the infinite-horizon optimal control problem. It is based on the construction of a novel terminal region using methods from the field of configuration-constrained polytopic computing, along with a terminal cost that is exactly equal to the infinite-horizon linear-quadratic regulator cost in a nontrivial neighborhood of the steady- state. The practical performance of the controller is illustrated through various case studies, and comparisons with state-of-the-art approaches are presented.