TY - GEN
T1 - Nonlinear control for MIMO magnetic levitation system using direct decentralized neural networks
AU - Chen, Syuan Yi
AU - Lin, Faa Jeng
PY - 2009
Y1 - 2009
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 trajectory. 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. 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 periodic sinusoidal reference trajectory 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 trajectory. 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. 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 periodic sinusoidal reference trajectory simultaneously in different operating conditions effectively.
UR - http://www.scopus.com/inward/record.url?scp=70350436278&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=70350436278&partnerID=8YFLogxK
U2 - 10.1109/AIM.2009.5229811
DO - 10.1109/AIM.2009.5229811
M3 - Conference contribution
AN - SCOPUS:70350436278
SN - 9781424428533
T3 - IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM
SP - 1763
EP - 1768
BT - 2009 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2009
T2 - 2009 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2009
Y2 - 14 July 2009 through 17 July 2009
ER -