Nonlinear Control of an Active Magnetic Bearing System Achieved Using a Fuzzy Control with Radial Basis Function Neural Network

Abstract

Studies on active magnetic bearing (AMB) systems are increasing in popularity and practical applications. Magnetic bearings cause less noise, friction, and vibration than the conventional mechanical bearings; however, the control of AMB systems requires further investigation. The magnetic force has a highly nonlinear relation to the control current and the air gap. This paper proposes an intelligent control method for positioning an AMB system that uses a neural fuzzy controller (NFC). The mathematical model of an AMB system comprises identification followed by collection of information from this system. A fuzzy logic controller (FLC), the parameters of which are adjusted using a radial basis function neural network (RBFNN), is applied to the unbalanced vibration in an AMB system. The AMB system exhibited a satisfactory control performance, with low overshoot, and produced improved transient and steady-state responses under various operating conditions. The NFC has been verified on a prototype AMB system. The proposed controller can be feasibly applied to AMB systems exposed to various external disturbances; demonstrating the effectiveness of the NFC with self-learning and self-improving capacities is proven.

Robust Adaptive Cerebellar Model Articulation Controller for 1-DOF Nonlaminated Active Magnetic Bearings

“…Along with the development of the AMB system, in the past decades, there have been many classic control methods such as PID control, modern control such as optimal control, adaptive control, sustainable control and nonlinear controls such as Backstepping control, and sliding mode control. Especially in recent years, intelligence such as fuzzy control, adaptive PID control, and neural network control [4][5][6][7][8][9][10] has been developed and applied to control the desired position of the rotor in a conventional magnetic bearing (AMB) system where the actuator has a layered structure. For the case of nonlaminated magnetic drive [11][12][13][14] almost no research on intelligent control in general and neural network on control in particular has been published.…”

Determination of all stability region of unstable fractional order system in case of bearing system

“…Therefore, it is challenging to obtain a complete PID controller for the MB system by solving five complicated nonlinear equations. As for the rule-based method, it can easily calculate the controller parameters based on empirical tuning rules, which can be observed in [9][10][11][12][13][14][15][16][17][18][19][20][21]. In this paper, we propose and investigate the optimal tuning PID controller of unstable fractional order system by desired transient characteristics using the real interpolation method (RIM).…”

ARNISMC for MLS with global positioning tracking control

“…Numerous control methods have been proposed for MLSs such as feedback linearisation technique, non-linear, robust, and fuzzy neural network (NN)-based techniques, providing robustness against unmodelled non-linearities present in the system [5][6][7][8]. These control schemes have improved the performance of the MLSs from different aspects.…”