Proposal of a Fuzzy Controller for Radial Position in a Bearingless Induction Motor

Abstract

In this paper, a radial position control approach of a bearingless induction motor is proposed. The rotor is supported by magnetic forces, which is complex to model. Generally, simplifications are adopted to determine a linearized model that hinders classical controllers' performance. On the other hand, fuzzy controllers have non-linear characteristics and do not require precise mathematical models. Instead, it uses the experience and knowledge of human operators to build a knowledge base to be used on the control. The prototype under study is a three-phase induction motor without a rated power of 1 HP, 4-pole, 220/380V and rated current 3/1.75A. It operates on a vertical axis, so there is no need to compensate for the rotor weight. The paper proposes the implementation of a Mamdani Fuzzy PD controller in order to control the rotor radial position of a three-phase induction motor by means of a Texas Instruments TMS320F28335 Digital Signal Processor (DSP). We present experimental results that validate the good performance of the fuzzy controller.

Research on the Hoisting Motor Drive System’s Active Disturbance Rejection Control and Energy Consumption for a Crane

“…Vector control theories and methods mainly include adaptive control [23][24][25], back stepping control [26], inverse dynamic control [27], passive control [28], sliding mode control [29,30], and fuzzy logic control [31,32]. Owing to the coupling terms of the state variables in the motor dynamics equations, the torque and excitation components of the motor stator current influence each other, and affect the dynamic performance of the control system.…”

“…Owing to the coupling terms of the state variables in the motor dynamics equations, the torque and excitation components of the motor stator current influence each other, and affect the dynamic performance of the control system. The control methods described previously require complex controller design [23][24][25][26][27][28][29][30][31][32], and they cannot influence the decoupled control of the output variables. In addition, when motor parameters (e.g., self-inductance) change due to factors such as temperature rise, the control system's performance may suffer.…”

Robust Integral State Feedback Using Coefficient Diagram in Magnetic Levitation System

“…There are some proposed methods to control the maglev system such as PID controller [9], the fuzzy logic controller [10], [11], LQR [12], Fault-tolerant control and state observer [13], nonlinear power shaping [14], sliding mode control [15], Global Sliding Mode Control [16], Modified Sliding Mode Control [17], feedback linearization [18] and backstepping [19]. Each of them has its own advantages and disadvantages.…”

Active disturbance rejection control for bearingless permanent-magnet slice motor based on nonlinear phase-locked loop observer

“…However, the ultra-local model is not suitable for BPMSM with coupling characteristics. Besides, many advanced control theories, such as sliding mode control, 12–14 predictive control, 9,15 fuzzy control, 16,17 neural network control, 18,19 etc., have been employed to achieve better dynamic and steady performance or good disturbance rejection property for rotation and suspension control in the past few years, but they are still not good choices to apply in BPMSM for bearingless pumps considering the algorithm complexity and hardware cost in actual engineering applications.…”