TY - JOUR
T1 - Direct decentralized neural control for nonlinear MIMO magnetic levitation system
AU - Chen, Syuan Yi
AU - Lin, Faa Jeng
AU - Shyu, Kuo Kai
N1 - Funding Information:
The author would like to acknowledge the financial support of the National Science Council of Taiwan, ROC through its Grant NSC 95-2221-E-008-177-MY3.
PY - 2009/8
Y1 - 2009/8
N2 - A direct modified Elman neural networks (MENNs)-based decentralized controller is proposed to control the magnets of a nonlinear and unstable multi-input multi-output (MIMO) levitation system for the tracking of reference trajectories. First, the operating principles of a magnetic levitation system with two moving magnets are introduced. Then, due to the exact dynamic model of the MIMO magnetic levitation system is not clear, two MENNs are combined to be a direct MENN-based decentralized controller to deal with the highly nonlinear and unstable MIMO magnetic levitation system. Moreover, the connective weights of the MENNs are trained online by back-propagation (BP) methodology and the convergence analysis of the tracking error using discrete-type Lyapunov function is provided. Based on the direct and decentralized concepts, the computational burden is reduced and the controller design is simplified. Furthermore, the experimental results show that the proposed control scheme can control the magnets to track various periodic reference trajectories simultaneously in different operating conditions effectively.
AB - A direct modified Elman neural networks (MENNs)-based decentralized controller is proposed to control the magnets of a nonlinear and unstable multi-input multi-output (MIMO) levitation system for the tracking of reference trajectories. First, the operating principles of a magnetic levitation system with two moving magnets are introduced. Then, due to the exact dynamic model of the MIMO magnetic levitation system is not clear, two MENNs are combined to be a direct MENN-based decentralized controller to deal with the highly nonlinear and unstable MIMO magnetic levitation system. Moreover, the connective weights of the MENNs are trained online by back-propagation (BP) methodology and the convergence analysis of the tracking error using discrete-type Lyapunov function is provided. Based on the direct and decentralized concepts, the computational burden is reduced and the controller design is simplified. Furthermore, the experimental results show that the proposed control scheme can control the magnets to track various periodic reference trajectories simultaneously in different operating conditions effectively.
KW - Decentralized control
KW - Elman neural network
KW - MIMO system
KW - Magnetic levitation system
UR - http://www.scopus.com/inward/record.url?scp=78649386967&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=78649386967&partnerID=8YFLogxK
U2 - 10.1016/j.neucom.2009.02.009
DO - 10.1016/j.neucom.2009.02.009
M3 - Article
AN - SCOPUS:78649386967
SN - 0925-2312
VL - 72
SP - 3220
EP - 3230
JO - Neurocomputing
JF - Neurocomputing
IS - 13-15
ER -