Finite-Time Chaos Suppression of Permanent Magnet Synchronous Motor Systems

Hou, Yi‐You

doi:10.3390/e16042234

Cited by 13 publications

(14 citation statements)

References 47 publications

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“…It is possible to find at least 35 works [ 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 ] in the Entropy journal that relate chaos theory in different areas; however, there are only five articles, from all areas, where reproducibility was mentioned [ 92 , 93 , 94 , 95 , 96 ]. From those aforementioned papers, only Funabashi [ 95 ] presented some slight relationship with the reproducibility of nonlinear dynamics fields.…”

Section: Related Workmentioning

confidence: 99%

A Note on the Reproducibility of Chaos Simulation

Nazare

Nepomuceno

Martins

et al. 2020

Entropy

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An evergreen scientific feature is the ability for scientific works to be reproduced. Since chaotic systems are so hard to understand analytically, numerical simulations assume a key role in their investigation. Such simulations have been considered as reproducible in many works. However, few studies have focused on the effects of the finite precision of computers on the simulation reproducibility of chaotic systems; moreover, code sharing and details on how to reproduce simulation results are not present in many investigations. In this work, a case study of reproducibility is presented in the simulation of a chaotic jerk circuit, using the software LTspice. We also employ the OSF platform to share the project associated with this paper. Tests performed with LTspice XVII on four different computers show the difficulties of simulation reproducibility by this software. We compare these results with experimental data using a normalised root mean square error in order to identify the computer with the highest prediction horizon. We also calculate the entropy of the signals to check differences among computer simulations and the practical experiment. The methodology developed is efficient in identifying the computer with better performance, which allows applying it to other cases in the literature. This investigation is fully described and available on the OSF platform.

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Section: Related Workmentioning

confidence: 99%

A Note on the Reproducibility of Chaos Simulation

Nazare

Nepomuceno

Martins

et al. 2020

Entropy

View full text Add to dashboard Cite

show abstract

“…The objective of this paper is to design the controller such that the synchronization performance of the states and , = 1, 2, 3 between chaotic systems (6) and 7are achieved.…”

Section: System Descriptionmentioning

confidence: 99%

“…Lots of control and synchronization schemes have been represented since the discovery of chaotic impact on the motor control performance, such as finite-time control [5,6], unidirectional correlation control [7], sliding mode control [8][9][10][11], linear control [12], dynamic surface control [13,14], optimal control [15], and neural network control [16]. In [5], an adaptive finite-time control method is proposed for PMSM system to suppress the chaos behavior with parameter uncertainties.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Chaos Synchronization Control of Nonlinear PMSM System Using Extended State Observer

Cheng¹,

Xue²,

Wang³

et al. 2016

Mathematical Problems in Engineering

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This paper proposes an adaptive chaos synchronization control scheme for nonlinear permanent magnet synchronous motor (PMSM) systems by using extended state observer (ESO). Frist of all, a chaotic PMSM system is built through an affine transformation and a time scale transformation of the mathematical PMSM model. Then, an adaptive sliding mode controller is developed based on the extended state observer to achieve the synchronization performance of two chaotic PMSM systems. Moreover, an adaptive parameter law of the control gain is designed to reduce the chattering problem existing in the traditional sliding mode control. Finally, the effectiveness of the proposed method is verified by simulation results.

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“…Previous studies have shown that the chaotic movement of PMSM will produce irregular oscillations of torque and speed, exacerbate current noise, and worsen operation performance and may even damage the entire drive system. Therefore, research on PMSM chaos phenomenon has attracted extensive attention worldwide [2][3][4][5], and further studying on the control method of PMSM chaos is of extreme significance [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Robust Backstepping Control of Permanent Magnet Synchronous Motor Chaotic System with Fully Unknown Parameters and External Disturbances

Guo

2016

Mathematical Problems in Engineering

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The chaotic behavior of permanent magnet synchronous motor is directly related to the parameters of chaotic system. The parameters of permanent magnet synchronous motor chaotic system are frequently unknown. Hence, chaotic control of permanent magnet synchronous motor with unknown parameters is of great significance. In order to make the subject more general and feasible, an adaptive robust backstepping control algorithm is proposed to address the issues of fully unknown parameters estimation and external disturbances inhibition on the basis of associating backstepping control with adaptive control. Firstly, the mathematical model of permanent magnet synchronous motor chaotic system with fully unknown parameters is constructed, and the external disturbances are introduced into the model. Secondly, an adaptive robust backstepping control technology is employed to design controller. In contrast with traditional backstepping control, the proposed controller is more concise in structure and avoids many restricted problems. The stability of the control approach is proved by Lyapunov stability theory. Finally, the effectiveness and correctness of the presented algorithm are verified through multiple simulation experiments, and the results show that the proposed scheme enables making permanent magnet synchronous motor operate away from chaotic state rapidly and ensures the tracking errors to converge to a small neighborhood within the origin rapidly under the full parameters uncertainties and external disturbances.

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Finite-Time Chaos Suppression of Permanent Magnet Synchronous Motor Systems

Cited by 13 publications

References 47 publications

A Note on the Reproducibility of Chaos Simulation

A Note on the Reproducibility of Chaos Simulation

Adaptive Chaos Synchronization Control of Nonlinear PMSM System Using Extended State Observer

Adaptive Robust Backstepping Control of Permanent Magnet Synchronous Motor Chaotic System with Fully Unknown Parameters and External Disturbances

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