Research Papers

Controlling Fluctuated Chaotic Power Systems With Compensation of Input Saturation: Application to Electric Direct Current Machines

[+] Author and Article Information
Mohammad Pourmahmood Aghababa

Electrical Engineering Department,
Urmia University of Technology,
Urmia 57166-17165, Iran
e-mails: m.p.aghababa@ee.uut.ac.ir; m.p.aghababa@gmail.com

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT,AND CONTROL. Manuscript received September 5, 2017; final manuscript received August 21, 2018; published online September 26, 2018. Assoc. Editor: Heikki Handroos.

J. Dyn. Sys., Meas., Control 141(1), 011012 (Sep 26, 2018) (10 pages) Paper No: DS-17-1427; doi: 10.1115/1.4041298 History: Received September 05, 2017; Revised August 21, 2018

It is shown that brushless direct current (DC) motors (BLDCMs), which have found many useful applications in motion control areas, display chaotic behaviors. To avoid undesirable inherent oscillations of such DC motors, a control strategy should be adopted in the applications. So, the control problem of applied chaotic power systems is taken into account in this paper. Some important aspects of the design and implementation are considered to reach a suitable controller for the applications. In this regard, it is assumed that the system is fluctuated by unknown uncertainties and environmental noises. Additionally, a part of the system dynamics is supposed to be unknown in advance and the effects of nonlinear input saturation are fully taken into account. Then, a one input nonsmooth adaptive sliding mode controller is realized to handle the aforementioned issues. The proposed controller does not require any knowledge about the bounds of the system uncertainties and external fluctuations as well as about the parameters of the input saturation. The finite time convergence and robustness of the driven control scheme are mathematically proved and numerically illustrated using matlab simulations for DC motors.

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Grahic Jump Location
Fig. 1

Strange attractors of the BLDCM machine

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Fig. 2

States of the controlled BLDCM machine (45)

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Fig. 3

Time response of the sliding manifold (48)

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Fig. 4

Time history of the saturated control signal (49)

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Fig. 5

Time history of the actual control signal for BLDCM machine

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Fig. 6

Phase trajectory diagram of the BLDCM after the proposed controller

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Fig. 7

Chaotic attractors of the PMSM machine

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Fig. 8

States of the controlled PMSM machine (50)

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Fig. 9

Time response of the sliding manifold (53)

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Fig. 10

Time history of the saturated control signal (54)

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Fig. 11

Time history of the actual control signal PMSM machine

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Fig. 12

Phase trajectory diagram of the PMSM after the proposed controller



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