Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Modeling and predictive control of magnetic levitation ball system based on RBF-ARX model with linear functional weights
AU - Qin, Yemei
AU - Peng, Hui
AU - Ruan, Wenjie
PY - 2016/8/26
Y1 - 2016/8/26
N2 - In order to fully describe the dynamic behavior of the magnetic levitation ball system which has nonlinear, open-loop instable and rapid response characteristics, RBF-ARX model with linear functional weights (LFWRBF-ARX model) was established. Different from a general RBF-ARX model, the LFWRBF-ARX model introduces a local linear structure as the weights of output layer. This model varies with the working-point, it is a locally linear ARX model when the working-point is fixed and it becomes a globally nonlinear ARX model when the working-point changes. According to the model structure, a structured nonlinear parameter optimization method (SNPOM) was applied to identify the structure, linear and nonlinear parameters of model. Then the identified model-based predictive controller was designed. The results show that the LFWRBF-ARX model may capture the local and global dynamic characteristics of the magnetic levitation ball system well, and the ball may be controlled to levitate stably. The control results are better than those of the general ARX model and RBF-ARX model-based control.
AB - In order to fully describe the dynamic behavior of the magnetic levitation ball system which has nonlinear, open-loop instable and rapid response characteristics, RBF-ARX model with linear functional weights (LFWRBF-ARX model) was established. Different from a general RBF-ARX model, the LFWRBF-ARX model introduces a local linear structure as the weights of output layer. This model varies with the working-point, it is a locally linear ARX model when the working-point is fixed and it becomes a globally nonlinear ARX model when the working-point changes. According to the model structure, a structured nonlinear parameter optimization method (SNPOM) was applied to identify the structure, linear and nonlinear parameters of model. Then the identified model-based predictive controller was designed. The results show that the LFWRBF-ARX model may capture the local and global dynamic characteristics of the magnetic levitation ball system well, and the ball may be controlled to levitate stably. The control results are better than those of the general ARX model and RBF-ARX model-based control.
KW - Magnetic levitation (maglev) system
KW - Nonlinear ARX model
KW - Nonlinear model predictive control
KW - RBF-ARX model with linear functional weights (LFWRBF-ARX)
KW - SNPOM
U2 - 10.11817/j.issn.1672-7207.2016.08.019
DO - 10.11817/j.issn.1672-7207.2016.08.019
M3 - Journal article
AN - SCOPUS:84988737070
VL - 47
SP - 2676
EP - 2684
JO - Zhongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Central South University (Science and Technology)
JF - Zhongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Central South University (Science and Technology)
SN - 1672-7207
IS - 8
ER -