Hybrid recurrent wavelet neural network control of PMSM servo-drive system for electric scooter

Lin, Chih-Hong

doi:10.1007/s12555-012-0190-2

Cited by 39 publications

(49 citation statements)

References 26 publications

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“…The voltage equations of the V-belt CVT system driven by PMSM in the synchronously rotating reference frame can be described as following as [5][6][7][28][29][30]: …”

Section: Structure Of the V-belt Cvt System Driven By Pmsmmentioning

confidence: 99%

“…Using (29), (30), (31) and (34), then (37) can be obtained is guaranteed so that the output tracking error will converge to zero as 0 t → . This completes the proof of the theorem.…”

Section: Third Layer: Output Layermentioning

confidence: 99%

“…Due to the V-belt CVT system driven by PMSM with many nonlinear dynamics [28][29][30], therefore the hybrid recurrent Chebyshev NN control system is developed to ensure the control performance of robustness in this paper. The hybrid recurrent Chebyshev NN control system has fast learning property and good generalization capability.…”

Section: Introductionmentioning

confidence: 99%

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PMSM Servo Drive for V-Belt Continuously Variable Transmission System Using Hybrid Recurrent Chebyshev NN Control System

Lin¹

2015

Journal of Electrical Engineering and Technology

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-Because the wheel of V-belt continuously variable transmission (CVT) system driven by permanent magnet synchronous motor (PMSM) has much unknown nonlinear and time-varying characteristics, the better control performance design for the linear control design is a time consuming job. In order to overcome difficulties for design of the linear controllers, a hybrid recurrent Chebyshev neural network (NN) control system is proposed to control for a PMSM servo-driven V-belt CVT system under the occurrence of the lumped nonlinear load disturbances. The hybrid recurrent Chebyshev NN control system consists of an inspector control, a recurrent Chebyshev NN control with adaptive law and a recouped control. Moreover, the online parameters tuning methodology of adaptive law in the recurrent Chebyshev NN can be derived according to the Lyapunov stability theorem and the gradient descent method. Furthermore, the optimal learning rate of the parameters based on discrete-type Lyapunov function is derived to achieve fast convergence. The recurrent Chebyshev NN with fast convergence has the online learning ability to respond to the system's nonlinear and timevarying behaviors. Finally, to show the effectiveness of the proposed control scheme, comparative studies are demonstrated by experimental results.

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“…The voltage equations of the V-belt CVT system driven by PMSM in the synchronously rotating reference frame can be described as following as [5][6][7][28][29][30]: …”

Section: Structure Of the V-belt Cvt System Driven By Pmsmmentioning

confidence: 99%

“…Using (29), (30), (31) and (34), then (37) can be obtained is guaranteed so that the output tracking error will converge to zero as 0 t → . This completes the proof of the theorem.…”

Section: Third Layer: Output Layermentioning

confidence: 99%

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PMSM Servo Drive for V-Belt Continuously Variable Transmission System Using Hybrid Recurrent Chebyshev NN Control System

Lin¹

2015

Journal of Electrical Engineering and Technology

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“…Owing to electric scooter with highly nonlinear dynamics [33][34][35][36][37][38][39][40][41][42], the many controllers may not provide satisfactory control performance when operated over a wide range of operating conditions. In [33][34], the hybrid recurrent fuzzy neural network (RFNN) control system is developed for PMSM driven wheel of electric scooter.…”

Section: Chih-hong Linmentioning

confidence: 99%

“…The speed and current tracking responses of the hybrid RWNN control system using rotor flux estimator have better performances than the hybrid RFNN control system from experimental results. Moreover, the hybrid RWNN control system proposed by [40][41] is developed for PMSM driven electric scooter in order to acquire better control performance for nonlinear uncertainties. In [40], the comparative studies of control performances for the hybrid RNN control system and the hybrid RWNN control system were implemented at two cases, i.e., 1200 rpm case and 2400 rpm case.…”

Section: Chih-hong Linmentioning

confidence: 99%

Dynamic Response of Novel Adaptive Modified Recurrent Legendre Neural Network Control for PMSM Servo-Drive Electric Scooter

Lin

2015

Automatika

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Original scientific paperBecause an electric scooter driven by permanent magnet synchronous motor (PMSM) servo-driven system has the unknown nonlinearity and the time-varying characteristics, its accurate dynamic model is difficult to establish for the design of the linear controller in whole system. In order to conquer this difficulty and raise robustness, a novel adaptive modified recurrent Legendre neural network (NN) control system, which has fast convergence and provide high accuracy, is proposed to control for PMSM servo-driven electric scooter under the external disturbances and parameter variations in this study. The novel adaptive modified recurrent Legendre NN control system consists of a modified recurrent Legendre NN control with adaptation law and a remunerated control with estimation law. In addition, the online parameter tuning methodology of the modified recurrent Legendre NN control and the estimation law of the remunerated control can be derived by using the Lyapunov stability theorem and the gradient descent method. Furthermore, the modified recurrent Legendre NN with variable learning rate is proposed to raise convergence speed. Finally, comparative studies are demonstrated by experimental results in order to show the effectiveness of the proposed control scheme.Key words: Permanent magnet synchronous motor, Legendre neural network, Lyapunov stability Dinamički odziv nove adaptivne modificirane povratne Legendrove neuronske mreže upravljanja sinkronim motorom s permanentnim magnetima za električni skuter. S obzirom da električni skuter pogonjen servo sustavom sa sinkroni motor s permanentnim magnetima ima nelinearnu dinamiku i vremenski promjenjive parametre, njegov dinamički model nije jednostavno odrediti u svrhu dizajniranja linearnog regulatora. Kako bi se riješio taj problem te povećala robusnost predložen je sustav upravljanja korištenjem adaptivne modificirane povratne Legendrove neuronske mreže za upravljanje skuterom pogonjenim servo sustavom sa sinkronim motorom uz prisustvo vanjskog poremećaja i promjenjivih parametara. Predloženo upravljanje ima brzu konvergenciju i visoku preciznost. Sustav upravljanja sastoji se od modificirane povratne Legendrove neuronske moreže s adaptivnim zakonom upravljanja i estimacijom. Dodatno, on-line podešavanje parametara takvog sustava može se dobiti korištenjem Ljapunovljevog teorema o stabilnosti sustava i gradijente metode. Modificirana povratne Legendrove neuronska mreža s promjenjivim vremenom učenja predložena je za povećanje brzine konvergencije. Ispravnost predložene sheme upravljanja provjerena je eksperimentalno.Ključne riječi: sinkroni motor s permanentnim magnetima, Legendrova neuronska mreža, stabilnost po Ljaponovu

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Modelling and control of six‐phase induction motor servo‐driven continuously variable transmission system using blend modified recurrent Gegenbauer orthogonal polynomial neural network control system and amended artificial bee colony optimization

Lin

2016

Int J Numerical Modelling

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Because the nonlinear and time-varying characteristics of the continuously variable transmission system operated using a six-phase copper rotor induction motor are unknown, improving the control performance of the linear control design is time-consuming. To capture the nonlinear and dynamic behaviour of the six-phase copper rotor induction motor servo-driven continuously variable transmission system, a blend modified recurrent Gegenbauer orthogonal polynomial neural network (NN) control system, which has the online learning capability to return to the nonlinear time-varying system, was developed. The blend modified recurrent Gegenbauer orthogonal polynomial NN control system can perform overseer control, modified recurrent Gegenbauer orthogonal polynomial NN control, and recompensed control. Moreover, the adaptation law of online parameters in the modified recurrent Gegenbauer orthogonal polynomial NN is based on the Lyapunov stability theorem. The use of amended artificial bee colony optimization yielded two optimal learning rates for the parameters, which helped improve convergence. Finally, comparison of the experimental results of the present study with those of previous studies demonstrated the high control performance of the proposed control scheme.

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Hybrid recurrent wavelet neural network control of PMSM servo-drive system for electric scooter

Cited by 39 publications

References 26 publications

PMSM Servo Drive for V-Belt Continuously Variable Transmission System Using Hybrid Recurrent Chebyshev NN Control System

PMSM Servo Drive for V-Belt Continuously Variable Transmission System Using Hybrid Recurrent Chebyshev NN Control System

Dynamic Response of Novel Adaptive Modified Recurrent Legendre Neural Network Control for PMSM Servo-Drive Electric Scooter

Modelling and control of six‐phase induction motor servo‐driven continuously variable transmission system using blend modified recurrent Gegenbauer orthogonal polynomial neural network control system and amended artificial bee colony optimization

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