RBF Neural Network Sliding Mode control for Aeronautical Remote Sensing Stable Platform

Wang, Zhuo; Li, Yuanchun; Gao, Shuo; Meng, Linghe; Ran, Tonghuan

doi:10.1109/cac51589.2020.9326726

Cited by 2 publications

(2 citation statements)

References 12 publications

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“…The RBF is used to adjust the controller parameters of the SMC or to fit the nonlinear part of the controlled object. Nowadays, RBFSMC is mainly used in Ship's Heading [27], robotics [28,29], and aeronautical remote sensing stable platforms [30]. Motivated by the above studies, a sliding-mode controller using the RBF neural network (RBFNN) structure is proposed for the current control of MMC.…”

Section: Introductionmentioning

confidence: 99%

RBF Neural Network-Based Sliding Mode Control for Modular Multilevel Converter with Uncertainty Mathematical Model

Yang

Fang

2022

Energies

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For medium and high-powered applications, modular multilevel converters have become the most promising converter application. In this paper, a sliding mode controller based on an RBF neural network is proposed for a modular multilevel converter. The RBF neural network is designed to approximate the uncertainty mathematical model of a modular multilevel converter. The main innovation of the proposed method is that it does not require any model parameters and control parameters during the whole control process. This means that parameter changes caused by the external environment will not influence the controller performances. Finally, by comparing with a conventional PI controller, the simulation proves the feasibility and effectiveness of the proposed control method. In addition, the experimental results show that the grid-side current can become stable immediately while the active power is stabilized after 20 ms when the set value is changed.

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Section: Introductionmentioning

confidence: 99%

RBF Neural Network-Based Sliding Mode Control for Modular Multilevel Converter with Uncertainty Mathematical Model

Yang

Fang

2022

Energies

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“…As neural networks (NNs) can fit arbitrary nonlinear functions, they are widely used in nonlinear systems [22,23]. Nowadays, NNs are mainly used in gas-oil-water three-phase flows [24], the mechanical properties of concrete [25], and aeronautical remote sensing stable platforms [26]. Motivated by the above studies, a fractional-order sliding mode controller using an RBF neural network (RBF NN) is proposed for the current control of an DTMC in this article.…”

Section: Introductionmentioning

confidence: 99%

RBF Neural Network Fractional-Order Sliding Mode Control with an Application to Direct a Three Matrix Converter under an Unbalanced Grid

Yang

Fang

et al. 2022

Sustainability

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This paper presents a fractional-order sliding mode control scheme based on an RBF neural network (RBFFOSMC) for a direct three matrix converter (DTMC) operating under unbalanced grid voltages. The RBF neural network (RBF NN) is designed to approximate a nonlinear fractional-order sliding mode controller. The proposed method aims to achieve constant active power whilst maintaining a near unity input power factor. First, an opportune reference current is accurately generated according to the reference power and the RBFFOSMC is designed in a dq reference frame to achieve a perfect tracking of the input current reference. An almost constant active power, free of low-frequency ripples, is then supplied from the grid after compensating for the output voltage. Simulation and experimental studies prove the feasibility and effectiveness of the proposed control method.

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RBF Neural Network Sliding Mode control for Aeronautical Remote Sensing Stable Platform

Cited by 2 publications

References 12 publications

RBF Neural Network-Based Sliding Mode Control for Modular Multilevel Converter with Uncertainty Mathematical Model

RBF Neural Network-Based Sliding Mode Control for Modular Multilevel Converter with Uncertainty Mathematical Model

RBF Neural Network Fractional-Order Sliding Mode Control with an Application to Direct a Three Matrix Converter under an Unbalanced Grid

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