LMI-based model predictive control for switched nonlinear systems |
| |
| Affiliation: | 1. Department of Engineering, Design and Mathematics, University of the West of England, Bristol, United Kingdom;2. Department of Electrical and Computer Engineering, University of California, Santa Barbara, 93106-9560 CA, USA;3. Computational and Biological Learning Group, Department of Engineering, University of Cambridge, Cambridge, United Kingdom;4. Department of Mechanical Engineering, Politecnico di Milano, Milan 20156, Italy;1. School of Mathematics Science, Liaocheng University, Liaocheng 252000, PR China;2. College of information science and engineering, Yanshan University, Qinhuangdao 066004, China;3. School of Mathematics Science, Liaocheng University, Shandong 252000, China;4. School of Automation, Nanjing University of Science and Technology, Nanjing 210094, PR China;1. Federal Rural University of the Semi-Arid Region, Rua Francisco Mota, 572, Pres. Costa e Silva, Mossoró, 59625-900, RN, Brazil;2. São Paulo State University, Avenida Brasil, 56, Centro, Ilha Solteira, 15385-000, SP, Brazil;3. Goiás Federal Institute of Education, Science, and Technology, Av. Presidente Juscelino Kubitschek, 775, Residencial Flamboyant, Jataí, 75804-714, GO, Brazil |
| |
| 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. |
| |
| Keywords: | Nonlinear switched systems Model predictive control Linear matrix inequalities Sum of square optimization |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
