A modeling and control approach to magnetic levitation system based on state-dependent ARX model |
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| Affiliation: | 1. School of Information Science and Engineering, Central South University, Changsha, Hunan 410083, China;2. Hunan Engineering Laboratory for Advanced Control and Intelligent Automation, Changsha, Hunan 410083, China;3. Swan College, Central South University of Forestry and Technology, Changsha, Hunan 410201, China;1. Dept. of Electrical and Electronics Engineering, Birla Institute of Technology, Mesra, Ranchi 835215, India;2. Dept. of Electrical Engineering, Indian Institute of Technology, Kharagpur 721302, India;3. Dept. of Electrical Engineering, National Institute of Technology, Raipur 492010, India;1. Tecnológico de Monterrey, Av. Eugenio Garza Sada 2501 Sur, Monterrey, N.L., México;2. Universidad Autónoma de Nuevo León, Av. Universidad s/n. San Nicolás de los Garza, N.L., México;3. Universidad Autónoma Metropolitana, Av. San Pablo No. 180, Azcapotzalco, D.F, México;1. Universidad Autónoma Metropolitana, Unidad Azcapotzalco, Departamento de Energía, C.P. 02200 Mexico, D.F., Mexico;2. Universidad Panamericana Campus Guadalajara, Prol. Calzada Circunvalación Pte. No. 49, Col. Ciudad Granja, Zapopan, Jalisco C.P. 45010, Mexico;3. Department of Mechatronics and Automation, ITESM Campus Monterrey, C.P. 64849 Monterrey, N.L., Mexico;1. School of Electrical Sciences, Indian Institute of Technology Bhubaneswar, Odisha 751013, India;2. Department of Computer Science and Communication, KTH (Royal Institute of Technology), Stockholm, Sweden;1. College of Transportation Engineering, Tongji University, 201804, China;2. National Maglev Transportation Engineering R&D Center, Tongji University, 201804, China;3. College of Logistics Engineering, Shanghai Maritime University, 201306, China |
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| Abstract: | Magnetic levitation (Maglev) systems are usually strongly nonlinear, open-loop unstable and fast responding. In order to control the position of the steel ball in a Maglev system, a data-driven modeling approach and control strategy is presented in this paper. A state-dependent AutoRegressive with eXogenous input (SD-ARX) model is built to represent the dynamic behavior between the current of electromagnetic coil and the position of the ball. State-dependent functional coefficients of the SD-ARX model are approximated by Gaussian radial basis function (RBF) neural networks. The model parameters are identified offline by applying the structured nonlinear parameter optimization method (SNPOM). Based on the model, a predictive controller is designed to stabilize the magnetic levitation ball to a given position or to make it track a desired trajectory. The real-time control results of the proposed approach and the comparisons with other two approaches are given, which demonstrate that the modeling and control method presented in this paper are very effective and superior in controlling the fast-responding, strongly nonlinear and open-loop unstable system. This paper gives the real experimental evidence that the RBF-ARX model is capable of not only globally, but also locally capturing and quantifying a nonlinear and fast-response system's behavior, and the model-based predictive control strategy is able to work quite well in a wide working-range of the nonlinear system. |
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| Keywords: | Magnetic levitation system RBF-ARX model Parameter optimization Predictive control Real-time control |
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