Decentralized PID neural network control for five degree-of-freedom active magneticbearing
A decentralized proportionalintegralderivative 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.
pid neural network active magnetic bearing decentralized control gradient descent method