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

Lin, Chih‐Hong

doi:10.6113/jpe.2014.14.5.1008

Cited by 3 publications

(4 citation statements)

References 39 publications

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“…When the RGPNN approximation properties cannot be ensured, the observer control system is able to action in this case. Differentiating equation (8) and by use of equations (7), (9), (10) and (11), the error equation can be acquired by…”

Section: Argpnn With Mpso Control Systemmentioning

confidence: 99%

“…Remark 1. The key point of the proposed design is to utilize the Lyapunov function for constructing the proposed ARGPNN with MPSO control system shown in equation (11), which reduces the input dimensions of the RGPNN.…”

Section: Performancementioning

confidence: 99%

“…Many artificial neural networks can realize a lot of nonlinear identifications and dynamic controls 9,10 owing to better learning ability. Besides, the functionlink neural networks [11][12][13][14] were posed to cut down the computing complexity. Moreover, to save computing cost and reduce longer iterative-training procedure, the good approximated ability of parameters by use of the Gegenbauer polynomials neural networks (GPNNs) 15,16 was posed.…”

Section: Introductionmentioning

confidence: 99%

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Admixed recurrent Gegenbauer polynomials neural network with mended particle swarm optimization control system for synchronous reluctance motor driving continuously variable transmission system

Lin

Chang

2019

Proceedings of the Institution of Mechanical Engineers, Part I:

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To cut down influence of nonlinear time-varying uncertainty action in a synchronous reluctance motor driving continuously variable transmission system, an admixed recurrent Gegenbauer polynomials neural network with mended particle swarm optimization control system is posed for improving control performance. The admixed recurrent Gegenbauer polynomials neural network with mended particle swarm optimization control system involves an observer control, a recurrent Gegenbauer polynomial neural network control and a remunerated control. The weights of recurrent Gegenbauer polynomials neural network controller are regulated by using the adaptive law and the gradient descent technology. The remunerated control with a reckoned law is derived and computed by means of the Lyapunov stability theorem so as to pledge stability of the control system. Likewise, to speedup convergence of weights in the recurrent Gegenbauer polynomial neural network, the mended particle swarm optimization algorithm is used for regulating two kinds of learning rates. At last, three kinds of experimental results are demonstrated to confirm the usefulness of the put forward control system with comparative studies.

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Section: Argpnn With Mpso Control Systemmentioning

confidence: 99%

Section: Performancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Admixed recurrent Gegenbauer polynomials neural network with mended particle swarm optimization control system for synchronous reluctance motor driving continuously variable transmission system

Lin

Chang

2019

Proceedings of the Institution of Mechanical Engineers, Part I:

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“…The flux position in the d-q coordinates can be determined with Hall sensors. In (4), (8) determined by a closed-loop control. The rotor flux is only produced in the d-axis, whereas the current vector is generated in the q-axis for field-oriented control.…”

Section: Configuration Of Lsm Drive Systemmentioning

confidence: 99%

A Backstepping Control of LSM Drive Systems Using Adaptive Modified Recurrent Laguerre OPNNUO

Lin¹

2016

Journal of Power Electronics

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The good control performance of permanent magnet linear synchronous motor (LSM) drive systems is difficult to achieve using linear controllers because of uncertainty effects, such as fictitious forces. A backstepping control system using adaptive modified recurrent Laguerre orthogonal polynomial neural network uncertainty observer (OPNNUO) is proposed to increase the robustness of LSM drive systems. First, a field-oriented mechanism is applied to formulate a dynamic equation for an LSM drive system. Second, a backstepping approach is proposed to control the motion of the LSM drive system. With the proposed backstepping control system, the mover position of the LSM drive achieves good transient control performance and robustness. As the LSM drive system is prone to nonlinear and time-varying uncertainties, an adaptive modified recurrent Laguerre OPNNUO is proposed to estimate lumped uncertainties and thereby enhance the robustness of the LSM drive system. The on-line parameter training methodology of the modified recurrent Laguerre OPNN is based on the Lyapunov stability theorem. Furthermore, two optimal learning rates of the modified recurrent Laguerre OPNN are derived to accelerate parameter convergence. Finally, the effectiveness of the proposed control system is verified by experimental results.

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A Six-Phase CRIM Driving CVT using Blend Modified Recurrent Gegenbauer OPNN Control

Lin¹

2016

Journal of Power Electronics

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Because the nonlinear and time-varying characteristics of continuously variable transmission (CVT) systems driven by means of a six-phase copper rotor induction motor (CRIM) are unconscious, the control performance obtained for classical linear controllers is disappointing, when compared to more complex, nonlinear control methods. A blend modified recurrent Gegenbauer orthogonal polynomial neural network (OPNN) control system which has the online learning capability to come back to a nonlinear time-varying system, was complied to overcome difficulty in the design of a linear controller for six-phase CRIM driving CVT systems with lumped nonlinear load disturbances. The blend modified recurrent Gegenbauer OPNN control system can carry out examiner control, modified recurrent Gegenbauer OPNN control, and reimbursed control. Additionally, the adaptation law of the online parameters in the modified recurrent Gegenbauer OPNN is established on the Lyapunov stability theorem. The use of an amended artificial bee colony (ABC) optimization technique brought about two optimal learning rates for the parameters, which helped reform convergence. Finally, a comparison of the experimental results of the present study with those of previous studies demonstrates the high control performance of the proposed control scheme.

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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

Cited by 3 publications

References 39 publications

Admixed recurrent Gegenbauer polynomials neural network with mended particle swarm optimization control system for synchronous reluctance motor driving continuously variable transmission system

Admixed recurrent Gegenbauer polynomials neural network with mended particle swarm optimization control system for synchronous reluctance motor driving continuously variable transmission system

A Backstepping Control of LSM Drive Systems Using Adaptive Modified Recurrent Laguerre OPNNUO

A Six-Phase CRIM Driving CVT using Blend Modified Recurrent Gegenbauer OPNN Control

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