Lazar, Mircea (Eindhoven Univ. of Tech.), Muñoz de la Peña, David (Univ. of California, Los Angeles), Heemels, Maurice (Eindhoven Univ. of Tech.), Alamo, Teodoro (Univ. de Sevilla)

Min-max Nonlinear Model Predictive Control with Guaranteed Input-to-State Stability

Scheduled for presentation during the Regular Session "Model Predictive Control I" (MoA10), Monday, July 24, 2006, 10:50−11:15, Room 103

17th International Symposium on Mathematical Theory of Networks and Systems, July 24-28, 2006, Kyoto, Japan

This information is tentative and subject to change. Compiled on April 19, 2024

Abstract

In this paper we consider discrete-time nonlinear systems that are affected, possibly simultaneously, by parametric uncertainties and disturbance inputs. The min-max model predictive control (MPC) methodology is employed to obtain a controller that robustly steers the state of the system towards a desired equilibrium. The aim is to provide a priori sufficient conditions for robust stability of the resulting closed-loop system via the input-to-state stability framework. First, we show that only input-to-state practical stability can be ensured in general for perturbed nonlinear systems in closed-loop with min-max MPC schemes and we provide explicit bounds on the evolution of the closed-loop system state. Then, we derive new sufficient conditions that guarantee input-to-state stability of the min-max MPC closed-loop system, via a dual-mode approach.