Robust wavelet-neural-network sliding-mode control system for permanent magnet synchronous motor drive

El-Sousy, Fayez F. M.

doi:10.1049/iet-epa.2009.0229

Cited by 93 publications

(46 citation statements)

References 29 publications

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“…A general supervised gradient descent backpropagation learning algorithm, in which gradient vectors is recursively calculated by deriving an energy function with respect to a parameter of the network, is adopted to online train parameters of WNN [8,[19][20][21][22]. The energy function is defined as…”

Section: Online Adaptive Wnn Training Algorithmmentioning

confidence: 99%

“…It designs sliding manifold by using a lower order model to achieve the control objective that state variable trajectories are forced to reach a sliding manifold in finite time and stay on the manifold for all future time and motion on the manifold is independent of matched uncertainties in the same time [7]. Therefore it represents good characteristics such as insensitivity to parameter variations, external load disturbance rejection and fast dynamic response [8]. Yet sliding mode control suffers from drawbacks that a highfrequency oscillatory output phenomenon called chattering caused by high-speed switching used in sliding mode, and it is vulnerable to measurement noise [9].…”

Section: Introductionmentioning

confidence: 99%

“…It is proved that WNN has more superiority over the other neural networks in rapid learning and system identification which are suitable for handling nonlinearity and uncertainties of real-time servo control system [12][13][14]. Papers [8,[15][16] present that sliding mode control combined with WNN which can grants robust performance and obtain precise response. However, the accurate current regulation has not been considered which is important for high precision PMSM drive control.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Wavelet Neural Network Backstepping Sliding Mode Tracking Control for PMSM Drive System

Liu

2014

Automatika

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Original scientific paperThis paper presents a wavelet neural network backstepping sliding mode controller (WNNBSSM) for permanentmagnet synchronous motor (PMSM) position servo control system. Backstepping sliding mode (BSSM) is utilized to guarantee favorable tracking performance and stability of the whole system, meanwhile, wavelet neural network (WNN) is used for approximating nonlinear uncertainties. The designed controller combined the merits of the backstepping sliding mode control with robust characteristics and the WNN owning the capability of artificial neural networks for online learning and the capability of wavelet decomposition for identification. An observed error compensator is developed to compensate the estimated error of the WNN and the adaptive law is derived according to Lyapunov theorem. The effectiveness of the proposed controller is investigated in simulation under different operating conditions. The simulation results demonstrate the proposed WNNBSSM controller can provide precise tracking performance and robust characteristics despite unknown parameter uncertainties and load disturbance. Moreover, an implemental wavemaker system is established to verify the effectiveness of the proposed control algorithm.Key words: Backstepping, PMSM position servo system, Robustness, Sliding mode control, Wavelet neural networkAdaptivno slijedno upravljanje u kliznom režimu rada s valićnom neuronskom mrežom za sustav s PMSM elektromotornim pogonom. U radu je predstavljen povratnokoračni regulator u kliznom režimu rada za servo sustav pozicioniranja s motorom s permanentnim magnetima (PMSM) zasnovan na valićnim neuronskim mrežama (WNNBSSM). Povratnokoračni klizni režim rada (BSSM) korišten je za jamčenje željenih performansi slije enja i stabilnost cijelog sustava, dok je valićna neuronska mreža (WNN) korištena za aproksimaciju nelinearnih nesigurnosti. Sintetizirani regulator povezuje prednosti i robusne karakteristike povratnokoračnog kliznog režima upravljanja te WNN sa sposobnostima umjetnih neuronskih mreža za online učenje i mogućnost valićne dekompozicije za identifikaciju. Razvijen je kompenzator pogreške estimacije WNN i adaptivni upravljački zakon prema Ljapunovljevom teoremu. Djelotvornost predloženog regulatora istražena je kroz simulacije u različitim uvjetima rada. Rezultati simulacija pokazuju da predloženi WNNBSSM može osigurati precizna svojstva slije enja i robusne karakteristike unatoč nepoznatim nesigurnostima parametara i poremećaja tereta. Uz to, razvijen je izvedbeni sustav generatora valova za provjeru djelotvornosti predloženog upravljačkog algoritma.Ključne riječi: povratnokoračni regulator, PMSM servo sustav za pozicioniranje, robusnost, upravljanje s kliznim režimom rada, valićna neuronska mreža

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Section: Online Adaptive Wnn Training Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Adaptive Wavelet Neural Network Backstepping Sliding Mode Tracking Control for PMSM Drive System

Liu

2014

Automatika

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“…This is a major constraint for industrial applications. Furthermore, the NFC algorithms of [22][23] are quite complex when the reference models of the online selftuning algorithm are utilized.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, the maximum torque per ampere (MTPA) control is combined with the proposed controller to maximize the torque generation in the constant torque region. Unlike the previous online selftuning algorithms [17][18][19][20][21][22][23] in which the quadratic cost function to be minimized includes only the speed error, the proposed control technique chooses the time derivative of the quadratic Lyapunov function as the cost function to be minimized. Thus, the global stability analysis can be simple and systematic.…”

Section: Introductionmentioning

confidence: 99%

Neuro-Fuzzy Control of Interior Permanent Magnet Synchronous Motors: Stability Analysis and Implementation

Dang¹,

Vu²,

Choi³

et al. 2013

Journal of Electrical Engineering and Technology

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-This paper investigates a robust neuro-fuzzy control (NFC) method which can accurately follow the speed reference of an interior permanent magnet synchronous motor (IPMSM) in the existence of nonlinearities and system uncertainties. A neuro-fuzzy control term is proposed to estimate these nonlinear and uncertain factors, therefore, this difficulty is completely solved. To make the global stability analysis simple and systematic, the time derivative of the quadratic Lyapunov function is selected as the cost function to be minimized. Moreover, the design procedure of the online self-tuning algorithm is comparatively simplified to reduce a computational burden of the NFC. Next, a rotor angular acceleration is obtained through the disturbance observer. The proposed observer-based NFC strategy can achieve better control performance (i.e., less steady-state error, less sensitivity) than the feedback linearization control method even when there exist some uncertainties in the electrical and mechanical parameters. Finally, the validity of the proposed neuro-fuzzy speed controller is confirmed through simulation and experimental studies on a prototype IPMSM drive system with a TMS320F28335 DSP.

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Model Reduction and Controller Scheme Development of Permanent Magnet Synchronous Motor Drives in the Delta DomainUsing a Hybrid Firefly Technique

Ganguli

Srivastava

Kaur

et al. 2022

Handbook of Intelligent Computing and Optimization for Sustainable Development

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Robust wavelet-neural-network sliding-mode control system for permanent magnet synchronous motor drive

Cited by 93 publications

References 29 publications

Adaptive Wavelet Neural Network Backstepping Sliding Mode Tracking Control for PMSM Drive System

Adaptive Wavelet Neural Network Backstepping Sliding Mode Tracking Control for PMSM Drive System

Neuro-Fuzzy Control of Interior Permanent Magnet Synchronous Motors: Stability Analysis and Implementation

Model Reduction and Controller Scheme Development of Permanent Magnet Synchronous Motor Drives in the Delta DomainUsing a Hybrid Firefly Technique

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