Recurrent Fuzzy Neural Cerebellar Model Articulation Network Fault-Tolerant Control of Six-Phase Permanent Magnet Synchronous Motor Position Servo Drive

Lin, Faa-Jeng; Sun, I-Fan; Yang, Kai-Jie; Chang, J. S.

doi:10.1109/tfuzz.2015.2446535

Cited by 103 publications

(66 citation statements)

References 50 publications

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“…The designed SMO (5) will internally estimate the back EMF (13) several times within a speed control cycle to reduce the ripples of the estimated back EMF, and then externally calculate the position and the velocity of the rotor (14) and (15) once to obtain accurate estimations.…”

Section: Iterative Sliding Mode Observermentioning

confidence: 99%

“…In order to reduce the chattering effect in the sliding mode behavior, a sigmoid function replaces a traditional switching function. In addition, because of their good characteristics of having parallel distributed architecture and the ability to identify nonlinear system dynamics and to learn, generalize, and adapt to a new environment, ANNs have attracted much attention to more engineering applications recently [13][14][15][16]. Sensorless motor control is one of excellent example.…”

Section: Introductionmentioning

confidence: 99%

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Sliding Mode and Neural Network Control of Sensorless PMSM Controlled System for Power Consumption and Performance Improvement

Wang

Tsai

2017

Energies

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This paper deals with the design of sliding mode control and neural network compensation for a sensorless permanent magnet synchronous motor (PMSM) controlled system that is able to improve both power consumption and speed response performance. The position sensor of PMSM is unreliable in harsh environments. Therefore, the sensorless control technique is widely proposed in industry. A sliding mode observer can estimate the rotor angle and has the robustness to load disturbance and parameter variations. However, the sliding mode observer is not conducive to standstill and low speed conditions because the amplitude of the back EMF is almost zero. As a result, this paper combines an iterative sliding mode observer (ISMO) and neural networks (NNs) as an angle compensator to improve the above problems. A dsPIC30F6010A-based PMSM sensorless drive system is implemented to validate the proposed algorithm. The simulation and experimental results prove its effectiveness.

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Section: Iterative Sliding Mode Observermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sliding Mode and Neural Network Control of Sensorless PMSM Controlled System for Power Consumption and Performance Improvement

Wang

Tsai

2017

Energies

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“…where the inequality in (A3) has used (8), the expressions of g 1 and g 2 are shown in (12) and (13). As ‖S(k À d)‖> 2(1 + α)β(X, U eq )/[(1 À α)γ], g 1 > 0 and g 2 1 À g 2 > 0 are achieved.…”

Section: Appendixes Appendix a (The Proof Of Theorem 1)mentioning

confidence: 99%

Adaptive Recurrent Neural Network Enhanced Variable Structure Control for Nonlinear Discrete Mimo Systems

Hwang

Hung

2017

Asian Journal of Control

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The stability and performance of robust control for a nonlinear complex dynamic system deteriorates due to large uncertainties. To address this, a discrete variable structure control (DVSC) outside of a convex set is designed to force the operating point into a convergent set, which is verified by Lyapunov stability theory. Due to the dynamic features of the lumped uncertainties, they are learned on-line by a recurrent neural network (RNN) in a specific convex set containing the convergent set of the DVSC. Between this convergent set and the convex set, a switching mechanism determines whether a learning RNN for the lumped uncertainties is executed. An adaptive recurrent-neural-network enhanced discrete variable structure control is then established to improve both transient and steady performances. Simulations, including compared examples and application to the trajectory tracking of a mobile robot, validate the effectiveness and robustness of the proposed control.

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“…Thus, the delta adaptation law in the following is adopted in order to increase the online learning rate of the network parameters [24]:…”

Section: Online Learning Algorithmsmentioning

confidence: 99%

A Three-Phase Four-Leg Inverter-Based Active Power Filter for Unbalanced Current Compensation Using a Petri Probabilistic Fuzzy Neural Network

2017

Self Cite

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Abstract:A three-phase four-leg inverter-based shunt active power filter (APF) is proposed to compensate three-phase unbalanced currents under unbalanced load conditions in grid-connected operation in this study. Since a DC-link capacitor is required on the DC side of the APF to release or absorb the instantaneous apparent power, the regulation control of the DC-link voltage of the APF is important especially under load variation. In order to improve the regulation control of the DC-link voltage of the shunt APF under variation of three-phase unbalanced load and to compensate the three-phase unbalanced currents effectively, a novel Petri probabilistic fuzzy neural network (PPFNN) controller is proposed to replace the traditional proportional-integral (PI) controller in this study. Furthermore, the network structure and online learning algorithms of the proposed PPFNN are represented in detail. Finally, the effectiveness of the three-phase four-leg inverter-based shunt APF with the proposed PPFNN controller for the regulation of the DC-link voltage and compensation of the three-phase unbalanced current has been demonstrated by some experimental results.

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Recurrent Fuzzy Neural Cerebellar Model Articulation Network Fault-Tolerant Control of Six-Phase Permanent Magnet Synchronous Motor Position Servo Drive

Cited by 103 publications

References 50 publications

Sliding Mode and Neural Network Control of Sensorless PMSM Controlled System for Power Consumption and Performance Improvement

Sliding Mode and Neural Network Control of Sensorless PMSM Controlled System for Power Consumption and Performance Improvement

Adaptive Recurrent Neural Network Enhanced Variable Structure Control for Nonlinear Discrete Mimo Systems

A Three-Phase Four-Leg Inverter-Based Active Power Filter for Unbalanced Current Compensation Using a Petri Probabilistic Fuzzy Neural Network

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