LMI-based model predictive control for switched nonlinear systems. (November 2017) - Nodozi, Iman; Rahmani, Mehdi
Highlights: An LMI approach to model predictive control for switched nonlinear systems is proposed. An SOS optimization is presented to find a bound for the nonlinear part of the system. The stability condition of the system is obtained by a discrete Lyapunov function. The control law is achieved so that guarantees the stability and minimizes a performance index. The maximum area region of convergence inscribed in the region of stability is achieved. Abstract: This paper proposes an LMI approach to model predictive control of nonlinear systems with switching between multiple modes. In this approach, at each mode, the nonlinear system is divided to a linearized model in addition to a nonlinear term. A sum of squares (SOS) optimization problem is presented to find a quadratic bound for the nonlinear part. The stability condition of the switching system is obtained by using a discrete Lyapunov function and then the sufficient state feedback control law is achieved so that guarantees the stability of the system and also minimizes an infinite prediction horizon performance index. Moreover, two other LMI optimization problems are solved at each mode in order to find the maximum area region of convergence of the nonlinear system inscribed in the region of stability. The performance and effectiveness of the proposed MPC approach are illustrated by two case studies.