TY - JOUR
T1 - Decentralized PID neural network control for five degree-of-freedom active magneticbearing
AU - Chen, Syuan Yi
AU - Lin, Faa Jeng
N1 - Funding Information:
The authors would like to acknowledge the financial support of the National Science Council of Taiwan, R.O.C . through its grant NSC 98-2221-E-008-115-MY3 .
PY - 2013/3
Y1 - 2013/3
N2 - A decentralized proportional-integral-derivative neural network (PIDNN) control scheme is proposed to regulate and stabilize a fully suspended five degree-of-freedom (DOF) active magnetic bearing (AMB) system which is composed of two radial AMBs (RAMBs) and one thrust AMB (TAMB). First, the structure and operating principles of the five-DOF AMB system are introduced. Then, the adopted differential driving mode (DDM) for the drive system of the AMB is analyzed. Moreover, due to the exact dynamic model of the five-DOF AMB system is vague, a decentralized PIDNN controller is proposed to control the five-axes of the rotor simultaneously in order to confront the uncertainties including inherent nonlinearities and external disturbances effectively. Furthermore, the connective weights of the PIDNN are trained on-line and the convergence analysis of the regulating error is provided using a discrete-type Lyapunov function. Based on the decentralized concepts, the computational burden is reduced and the controller design is simplified. Finally, the experimental results show that the proposed control scheme provides good control performances and robustness for controlling the fully suspended five-DOF AMB system in different operating conditions.
AB - A decentralized proportional-integral-derivative neural network (PIDNN) control scheme is proposed to regulate and stabilize a fully suspended five degree-of-freedom (DOF) active magnetic bearing (AMB) system which is composed of two radial AMBs (RAMBs) and one thrust AMB (TAMB). First, the structure and operating principles of the five-DOF AMB system are introduced. Then, the adopted differential driving mode (DDM) for the drive system of the AMB is analyzed. Moreover, due to the exact dynamic model of the five-DOF AMB system is vague, a decentralized PIDNN controller is proposed to control the five-axes of the rotor simultaneously in order to confront the uncertainties including inherent nonlinearities and external disturbances effectively. Furthermore, the connective weights of the PIDNN are trained on-line and the convergence analysis of the regulating error is provided using a discrete-type Lyapunov function. Based on the decentralized concepts, the computational burden is reduced and the controller design is simplified. Finally, the experimental results show that the proposed control scheme provides good control performances and robustness for controlling the fully suspended five-DOF AMB system in different operating conditions.
KW - Active magnetic bearing
KW - Decentralized control
KW - Gradient descent method
KW - PID neural network
UR - https://www.scopus.com/pages/publications/84873990448
UR - https://www.scopus.com/pages/publications/84873990448#tab=citedBy
U2 - 10.1016/j.engappai.2012.11.002
DO - 10.1016/j.engappai.2012.11.002
M3 - Article
AN - SCOPUS:84873990448
SN - 0952-1976
VL - 26
SP - 962
EP - 973
JO - Engineering Applications of Artificial Intelligence
JF - Engineering Applications of Artificial Intelligence
IS - 3
ER -