Intelligent Global Sliding Mode Control Using Recurrent Feature Selection Neural Network for Active Power Filter

Hou, Shixi; Chu, Yundi; Fei, Juntao

doi:10.1109/tie.2020.3000098

Cited by 53 publications

(39 citation statements)

References 43 publications

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“…Hou and Fei [30] developed a meta-cognitive FNN (MCFNN) framework to estimate the uncertain term for the global sliding-mode control (GSMC) law, and the control performance was verified on an APF. Moreover, a recurrent feature selection neural network (RFSNN) was proposed to mimic the uncertain compensation current in an APF system in [31]. The RFSNN in [31] combining with the GSMC can reduce the computational burden of NN with full parameters adjustment and assure a high-performance current control.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, a recurrent feature selection neural network (RFSNN) was proposed to mimic the uncertain compensation current in an APF system in [31]. The RFSNN in [31] combining with the GSMC can reduce the computational burden of NN with full parameters adjustment and assure a high-performance current control. An adaptive type-2 FNN control system with an uncertainty compensator was proposed to enhance the ability representing system uncertainties and the performance of power quality improvement for an APF in [32].…”

Section: Introductionmentioning

confidence: 99%

“…An adaptive type-2 FNN control system with an uncertainty compensator was proposed to enhance the ability representing system uncertainties and the performance of power quality improvement for an APF in [32]. However, intelligent strategies in [28]- [31] just works as auxiliary controllers to approximate unknown functions or estimate system uncertainties for improving the control performance, and an extra compensation controller is required in [32]. As a result, the complexity of the whole control system will be increased.…”

Section: Introductionmentioning

confidence: 99%

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Design of Adaptive Fuzzy-Neural-Network-Imitating Sliding-Mode Control for Parallel-Inverter System in Islanded Micro-Grid

Yang

Wai

2021

IEEE Access

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In this study, an adaptive fuzzy-neural-network-imitating sliding-mode control (AFNNISMC) is developed for a parallel-inverter system in an islanded micro-grid (MG) via a master-slave current sharing strategy. For ensuring the system-level stability, an entire dynamic model is constructed by viewing the parallel-inverter system as a whole. First, a total sliding-mode control (TSMC) scheme, and the TSMC plus an adaptive observer to form an adaptive TSMC (ATSMC) framework are designed for the parallelinverter system. Then, a four-layer fuzzy neural network (FNN) is investigated to imitate the TSMC law to improve the system robustness, overcome the drawback of the dependence on detailed system dynamics, and deal with the chattering phenomena caused by the TSMC. According to the Lyapunov stability theorem and the projection algorithm, network parameters in the FNN are regulated online by employing the approximation error between the FNN and the TSMC law to ensure the convergence of the network and the stability of the control system. Thereby, the performance of high power quality and high-precision current sharing between inverters can be guaranteed even if system uncertainties exist. Moreover, the proposed AFNNISMC system can achieve the seamless disconnection and re-connection of slave inverters from and into an energized parallel-inverter system, which improves the redundancy and operation flexibility. In addition, numerical simulations and experimental results are given to demonstrate the feasibility and effectiveness of the proposed AFNNISMC scheme. Furthermore, performance comparisons with the ATSMC strategy and a conventional proportional-integral control (PIC) framework are provided to verify the superiority of the proposed scheme.INDEX TERMS Total sliding-mode control (TSMC), fuzzy neural network (FNN), adaptive control, parallel-inverter system, islanded micro-grid (MG), master-slave current sharing.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Design of Adaptive Fuzzy-Neural-Network-Imitating Sliding-Mode Control for Parallel-Inverter System in Islanded Micro-Grid

Yang

Wai

2021

IEEE Access

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“…However, such efficient control approach has not been exploited for APF. On the other hand, soft computing technologies containing neural network (NN) and fuzzy system have been proposed to constitute well-designed uncertainty estimators for SMC [18][19][20][21], in which soft computing approach is mostly developed to represent non-linear dynamics.…”

Section: Introductionmentioning

confidence: 99%

Modified fuzzy neural network control using sliding mode technique for power quality improvement system with experimental verification

Hou

Chu

Fei

2020

IET Control Theory & Appl

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This study mainly focuses on the development of a second-order sliding mode-based meta-cognitive fuzzy neural network (MCFNN) control for power quality improvement, whereby an MCFNN is employed to learn modelling uncertainties. First, a second-order sliding mode control (SOSMC) is designed to track reference current for active power filter. Then, an MCFNN system with accurate approximation capability is further investigated to learn the unknown parts in SOSMC. Different from the existing predefined structure approaches, only necessary data can be extracted to adjust the structure and parameters of the networks in MCFNN. Subsequently, the Lyapunov stability analysis is presented to guarantee tracking performance and stability of the closed-loop system. Moreover, the excellent performance of the proposed MCFNN scheme is verified by simulation and experimental studies, and its remarkable characteristics are exhibited in comparison with other intelligent control schemes.

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“…e trajectory tracking accuracy is usually affected by system uncertainties. To solve this problem, numerous approaches have been reported, such as sliding mode control (SMC) [9][10][11][12][13][14], adaptive control [15,16], model predictive control [17][18][19][20], fuzzy control [21,22], and neural networks [23][24][25]. However, the aforementioned works have some disadvantages, which make them difficult to be implemented in practice [8].…”

Section: Introductionmentioning

confidence: 99%

Finite-Time Terminal Sliding Mode Tracking Control of a VTOL UAV via the Generalized NDOB

Wang

Cheng

2020

Mathematical Problems in Engineering

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In this paper, we propose a finite-time sliding mode trajectory tracking control methodology for the vertical takeoff and landing unmanned aerial vehicle (VTOL UAV). Firstly, a system error model of trajectory tracking task is established based on Rodrigues parameters by considering both external and internal uncertainties. According to the cascade property, the system model is divided into translational and rotational subsystems, and a hierarchical control structure is hence proposed. Then, a finite-time generalized nonlinear disturbance observer (NDOB) is proposed, based on which the finite-time convergence result of equivalent disturbance estimation can be acquired. Finally, by introducing a tan-type compensator into the traditional terminal sliding mode control (SMC), the finite-time convergence result of the closed-loop control system is acquired based on Lyapunov stability analysis. Simulation results show the effectiveness of the proposed methodology.

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Intelligent Global Sliding Mode Control Using Recurrent Feature Selection Neural Network for Active Power Filter

Cited by 53 publications

References 43 publications

Design of Adaptive Fuzzy-Neural-Network-Imitating Sliding-Mode Control for Parallel-Inverter System in Islanded Micro-Grid

Design of Adaptive Fuzzy-Neural-Network-Imitating Sliding-Mode Control for Parallel-Inverter System in Islanded Micro-Grid

Modified fuzzy neural network control using sliding mode technique for power quality improvement system with experimental verification

Finite-Time Terminal Sliding Mode Tracking Control of a VTOL UAV via the Generalized NDOB

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