A computationally intelligent maximum torque per ampere control strategy for switched reluctance machines

Due to their inherent advantages such as low cost, robustness and wide speed range, switched reluctance machines (SRMs) have attracted great attention in electrical vehicles. However, the vibration and noise problems of SRMs limit their application in the automotive industry because of the negative impact on driver and passengers’ comfort. In this paper, a new control method is proposed to improve the vibratory and acoustic behavior of SRMs. Two additional control blocks —direct force control (DFC) and reference current adapter (RCA)—are introduced to the conventional control method (average torque control (ATC)) of SRM. DFC is adopted to control the radial force in the teeth of the stator, since the dynamic of the radial force has a large impact on the vibratory performance. RCA is proposed to handle the trade-off between the DFC and ATC. It produces an auto-tuning current reference to update the reference current automatically depending on the control requirement. The effectiveness of the proposed control strategy is verified by experimental results under both steady and transient condition. The results show that the proposed method improves the acoustic performance of the SRM and maintains the dynamic response of it, which proves the potential of the proposed control strategy.

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A maximum torque per ampere control strategy for induction motor drives

IEEE Transactions on Energy Conversion ◽

10.1109/60.678980 ◽

1998 ◽

Vol 13 (2) ◽

pp. 163-169 ◽

Cited By ~ 88

Author(s):

O. Wasynczuk ◽

S.D. Sudhoff ◽

K.A. Corzine ◽

J.L. Tichenor ◽

P.C. Krause ◽

...

Keyword(s):

Induction Motor ◽

Control Strategy ◽

Motor Drives ◽

Induction Motor Drives ◽

Maximum Torque ◽

Maximum Torque Per Ampere

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A particle swarm optimization based maximum torque per ampere control for a switched reluctance motor

IECON 2014 - 40th Annual Conference of the IEEE Industrial Electronics Society ◽

10.1109/iecon.2014.7048522 ◽

2014 ◽

Cited By ~ 3

Author(s):

L. Griffin ◽

F. Fleming ◽

C. S. Edrington

Keyword(s):

Particle Swarm Optimization ◽

Particle Swarm ◽

Switched Reluctance Motor ◽

Maximum Torque ◽

Swarm Optimization ◽

Switched Reluctance ◽

Reluctance Motor ◽

Maximum Torque Per Ampere

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Hierarchical Distributed Motion Control for Multiple Linear Switched Reluctance Machines

10.20944/preprints201706.0066.v1 ◽

2017 ◽

Author(s):

Bo Zhang ◽

Jianping Yuan ◽

Jianjun Luo ◽

Xiaoyu Wu ◽

Li Qiu ◽

...

Keyword(s):

Motion Control ◽

Control Strategy ◽

Tracking Control ◽

Position Control ◽

Tracking Error ◽

Control Network ◽

Switched Reluctance ◽

Switched Reluctance Machines ◽

Reference Signals ◽

Coordinated Tracking

This paper investigates a distributed, coordinated motion control network based on multiple direct-drive, linear switched reluctance machines (LSRMs). A hierarchical, two-level synchronization control strategy is proposed for the four LSRMs based motion control network. The high-level, reference signals agreement algorithm is first employed to correct the asynchronous behaviors of the position commands. Then, the low-level tracking synchronization method is applied for the collaborative position control of the four LSRMs. The proposed two-level, fault-tolerant control strategy eliminates the asynchrony of the reference signals and it also guarantees the coordinated tracking control performance of the four LSRMs. Experimental results demonstrate that effective coordinated tracking control can be ensured, based on successful agreement of reference signals and an absolute tracking error falling within 2 mm can be achieved.

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