Dynamic simulation model for hybrid electric scooters

Tseng, Chyuan-Yow; Lue, Yeou-Feng; Lin, Yu-Wei; Siao, Jheng-Cin; Tsai, Chien‐Hsiung; Fu, Lung-Ming

doi:10.1109/isie.2009.5222809

Cited by 23 publications

(57 citation statements)

References 8 publications

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“…The discrete-type Lyapunov function can be selected as (36) and the change in the Lyapunov function is obtained by (37 …”

Section: Parameter Tuning Methodology and Convergencementioning

confidence: 99%

“…Then, the dynamic equation of a V-belt CVT driven electric scooter using a PMSM servo drive [34][35][36][37] can be represented as:…”

Section: Structure Of a V-belt Cvt Drivenmentioning

confidence: 99%

“…In electric scooters [34]- [37] with nonlinear uncertainties, the hybrid recurrent fuzzy NN controller may not provide satisfactory control performance when operated over a wide range of operating conditions. In [34], a hybrid recurrent fuzzy NN controller using a rotor flux estimator was used to control the PMSM without a shaft encoder to drive electric scooter.…”

Section: Introductionmentioning

confidence: 99%

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A PMSM Driven Electric Scooter System with a V-Belt Continuously Variable Transmission Using a Novel Hybrid Modified Recurrent Legendre Neural Network Control

Lin¹

2014

Journal of Power Electronics

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An electric scooter with a V-belt continuously variable transmission (CVT) driven by a permanent magnet synchronous motor (PMSM) has a lot of nonlinear and time-varying characteristics, and accurate dynamic models are difficult to establish for linear controller designs. A PMSM servo-drive electric scooter controlled by a novel hybrid modified recurrent Legendre neural network (NN) control system is proposed to solve difficulties of linear controllers under the occurrence of nonlinear load disturbances and parameters variations. Firstly, the system structure of a V-belt CVT driven electric scooter using a PMSM servo drive is established. Secondly, the novel hybrid modified recurrent Legendre NN control system, which consists of an inspector control, a modified recurrent Legendre NN control with an adaptation law, and a recouped control with an estimation law, is proposed to improve its performance. Moreover, the on-line parameter tuning method of the modified recurrent Legendre NN is derived according to the Lyapunov stability theorem and the gradient descent method. Furthermore, two optimal learning rates for the modified recurrent Legendre NN are derived to speed up the parameter convergence. Finally, comparative studies are carried out to show the effectiveness of the proposed control scheme through experimental results.

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“…The discrete-type Lyapunov function can be selected as (36) and the change in the Lyapunov function is obtained by (37 …”

Section: Parameter Tuning Methodology and Convergencementioning

confidence: 99%

“…Then, the dynamic equation of a V-belt CVT driven electric scooter using a PMSM servo drive [34][35][36][37] can be represented as:…”

Section: Structure Of a V-belt Cvt Drivenmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A PMSM Driven Electric Scooter System with a V-Belt Continuously Variable Transmission Using a Novel Hybrid Modified Recurrent Legendre Neural Network Control

Lin¹

2014

Journal of Power Electronics

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“…Then the dynamic equation of PMSM servo-drive electric scooter [33][34][35][36][37][38][39][40][41][42][43][44] can be represented as…”

Section: Structure Of Pmsm Servo-drive Elec-tric Scooter Systemmentioning

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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“…A V-belt continuously variable transmission (CVT) [1][2] is typically composed of two hydraulically, or spring, actuated variable radii pulleys and a chain, or metal pushing, belt. To launch a vehicle from rest, the input pulley radius will be smaller than the output pulley radius, resulting in a speed reduction and torque multiplication transmitted to the drive shaft.…”

Section: Introductionmentioning

confidence: 99%

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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Dynamic simulation model for hybrid electric scooters

Cited by 23 publications

References 8 publications

A PMSM Driven Electric Scooter System with a V-Belt Continuously Variable Transmission Using a Novel Hybrid Modified Recurrent Legendre Neural Network Control

A PMSM Driven Electric Scooter System with a V-Belt Continuously Variable Transmission Using a Novel Hybrid Modified Recurrent Legendre Neural Network Control

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

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

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