Huawei Zhou

Remedial Field-Oriented Control of Five-Phase Fault-Tolerant Permanent-Magnet Motor by Using Reduced-Order Transformation Matrices

A five-phase fault-tolerant permanent-magnet (FTPM) motor can offer high torque density, low torque ripple, and good fault-tolerant capability. In order to improve operation performance under an open-circuit fault condition, this paper proposes a new remedial field-oriented control (RFOC) strategy for a five-phase FTPM motor. The novelty of the proposed RFOC strategy lies in the orthogonal reduced-order transformation matrix, which is derived from the fault-tolerant current references, and a new zero-sequence current related to torque ripple. The pulsation of the neutral voltage can be neglected in the RFOC strategy, having no effect on the control action. Also, the effect of the open-circuit fault on the motor drive model under the transformation matrix is discussed. A five-phase FTPM motor drive is prototyped and the proposed RFOC strategy is evaluated in terms of the steady-state and dynamic performances. The simulated and experimental results are given to verify the proposed strategy.

Estimation of induction motor speed based on artificial neural networks inversion system

As rotation speed is necessary for high-performance induction motor control, how to estimate the speed quickly and accurately is concerned by most scholars. On the analysis of theoretic invertibility of the induction motorpsilas mathematic model, a speed estimation based on neural networks inversion is proposed. The structure of multi-layer feed-forward neural network (MFNN) is trained by advanced backpropagation arithmetic. Also the achievement method and experiment results were given. The results show that the responses based on ANN inversion method can track the rotation speed quickly and accurately. The method proposed is effective in application.

Dynamic Performance Improvement of Five-Phase Permanent-Magnet Motor With Short-Circuit Fault

Multiphase permanent-magnet (PM) brushless motors are popularly adopted for their high efficiency and high power density. However, short-circuit phase fault results in serious problems, such as increased torque fluctuations and deteriorated dynamic performance. This paper proposes a new vectorial approach to minimize pulsating torque and improve dynamic performance in a five-phase PM motor with short-circuit fault. The novelty of the proposed strategy is voltage feedforward compensation based on the relation of the short-circuit current and its fault-phase back electromotive force. First, the compensatory voltages are used to eliminate the impact of the short-circuit current. Then, its combination with the orthogonal reduced-order transformation matrices derived from fault-tolerant current references can improve the dynamic performance of the faulty PM motor. The effect of the short-circuit phase fault on the PM motor model under rotating synchronous frame is also discussed. This control strategy allows minimal reconfiguration of the control structure from healthy operation to fault-tolerant one and exhibits the improved dynamic performance. The simulated and experimental results are presented as validation for the proposed strategy.

Realization of Neural Network Inverse System with PLC in Variable Frequency Speed-Regulating System

The variable frequency speed-regulating system which consists of an induction motor and a general inverter, and controlled by PLC is widely used in industrial field. However, for the multivariable, nonlinear and strongly coupled induction motor, the control performance is not good enough to meet the needs of speed-regulating. The mathematic model of the variable frequency speed-regulating system in vector control mode is presented and its reversibility has been proved. By constructing a neural network inverse system and combining it with the variable frequency speed-regulating system, a pseudo-linear system is completed, and then a linear close-loop adjustor is designed to get high performance. Using PLC, a neural network inverse system can be realized in actural system. The results of experiments have shown that the performances of variable frequency speed-regulating system can be improved greatly and the practicability of neural network inverse control was testified.

Tension Identification of Multi-motor Synchronous System Based on Artificial Neural Network

Sensorlesstension control of multi-motor synchronous system with closed tension loop is required in many fields. How to identify the knowledge of instantaneous magnitude of tension is key. In this paper the tension identification is managed on the base of stator currents and its previous values with neural network. According to the fundamental state equations of multi-motor system for tension control, the novel method of tension identification using neural network is presented .A multi-layer feed-forward neural network (MFNN) is trained by Back Propagation Levenberger-Marquardts method. Simulation and experiment results show that the system with tension identification via a neural network has better performance, and it can be used in many application fields.

Direct thrust control for five-phase tubular linear PM motor based on third-harmonic current suppression

Five-phase tubular linear PM (TLPM) motors have high thrust force density, zero net radial force and volumetrically efficient. Conventional direct thrust control (DTC) improves the dynamic performance of TLPM motor, but exhibits significant ripple in thrust force and flux. To solve the problem, a new DTC strategy based on third-harmonic current suppression is proposed. The effect of inverter voltage vectors in the fundamental and third subspaces on the flux and the thrust force is analyzed. Then the voltage vectors in third subspace are used to restrain the third-harmonic currents. Simulated results are presented to verify the effectiveness of the proposed control strategy.

Online Inductance Identifications of Interior Permanent Magnet Synchronous Machine Based on Adaline Neural Network

This paper presents an online adaline neural network (ANN) based identification method for estimating the dq-axis inductances of interior permanent magnet synchronous machine (IPMSM). This method only needs to sample the phase currents, bus voltage, rotor position angle and speed without any design parameters. In the proposed estimation, the rotor flux linkage and the stator winding resistance are firstly estimated offline, and then they are regarded as constants to estimate the dq-axis inductances online. The proposed method shows good stability when the load changes suddenly. The white noise is added to the load torque to validate the good robustness of the method. The results of online identification are acceptable even small error exists in the value of offline identification. Simulations using an IPMSM model illustrate the validity of the proposed online identification strategy.

High-Order Sliding Mode Speed Control of Five-Phase Tubular Fault-Tolerant Linear Permanent Magnet Motor

In active electromagnetic suspension system, the motor speed performance is vital to the suspension performance. However, PI speed controller is not robust against the parameter variations and load disturbances. To solve the problem, a high-order sliding mode (HOSM) speed control algorithm for tubular fault-tolerant linear permanent magnet (TFT- LPM) motor is proposed. The algorithm is characterized by a discontinuous function acting on the 2-order time derivative of the sliding mode manifold. The proposed algorithm makes motor speed almost no overshoot and quick response, shows good robustness against parameter uncertainty and load variations, and improves steady-state and dynamic performance. Simulated results are presented to verify the proposed method.

Sideslip angle soft-sensor based on neural network left inversion for multi-wheel independently driven electric vehicles

Effective estimation of vehicle states such as the yaw rate and the sideslip angle is important for vehicle stability control. Unfortunately the devices are very expensive to measure the sideslip angle directly and are not suitable for ordinary vehicle. Therefore, it must be estimated. A novel sideslip angle soft-sensor using neural network left inversion (NNLI) is presented for the in-wheel motor driven electric vehicle (EV). The innovation of the presented algorithm is not only little concerned with reference model parameters identification, but also uses the characteristic of the in-wheel motor driven EV. Longitudinal acceleration, lateral acceleration, yaw rate, longitudinal velocity, steering angle, the torque of in-wheel motor which can be acquired by ordinary sensors are used as inputs. Co-simulations are carried out to demonstrate the effectiveness of the proposed soft-sensor with Simulink and CarSim.

Active Safety Neural Network Inverse Decoupling Control for Multi-Wheel Independently Driven Electric Vehicles

Vehicle active safety control attracts ever increasing attention in the attempt to improve the stability and the maneuverability. The main contribution of this paper is that a neural network inverse controller for combined active front steering with direct yaw moment control of the electric vehicles is proposed. The proposed system is co-simulated based on the vehicle simulation package CarSim in connection with Matlab/Simulink. The simulated results verify the effectiveness of the proposed control strategy.