Fuzzy Multiple Hidden Layer Recurrent Neural Control of Nonlinear System Using Terminal Sliding-Mode Controller

Fei, Juntao; Chen, Yun; Liu, Lunhaojie; Fang, Yunmei

doi:10.1109/tcyb.2021.3052234

Cited by 116 publications

(58 citation statements)

References 38 publications

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“…To deal with the uncertain of nonlinear systems, the universal approximation theories of neural networks (NNs) and fuzzy logic systems (FLSs) can be employed to approach the unknown nonlinear functions. 17 For example, the authors proposed a fuzzy double hidden layer recurrent neural network approximation technology in Refs., [18][19][20] which can be regarded as a combination of a fuzzy NN and a RBFNN to improve the accuracy of a nonlinear approximation, so it has the advantages of these two NNs. Aiming at the field of fractional order systems control, Liu et al 21 designed adaptive fuzzy backstepping control method is proposed for uncertain fractional order chaotic systems including unknown external disturbance and input saturation.…”

Section: Introductionmentioning

confidence: 99%

Observer-based adaptive neural network backstepping sliding mode control for switched fractional order uncertain nonlinear systems with unmeasured states

Chen

Cao

Yuan

et al. 2021

Measurement and Control

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This paper proposes an observer-based adaptive neural network backstepping sliding mode controller to ensure the stability of switched fractional order strict-feedback nonlinear systems in the presence of arbitrary switchings and unmeasured states. To avoid “explosion of complexity” and obtain fractional derivatives for virtual control functions continuously, the fractional order dynamic surface control (DSC) technology is introduced into the controller. An observer is used for states estimation of the fractional order systems. The sliding mode control technology is introduced to enhance robustness. The unknown nonlinear functions and uncertain disturbances are approximated by the radial basis function neural networks (RBFNNs). The stability of system is ensured by the constructed Lyapunov functions. The fractional adaptive laws are proposed to update uncertain parameters. The proposed controller can ensure convergence of the tracking error and all the states remain bounded in the closed-loop systems. Lastly, the feasibility of the proposed control method is proved by giving two examples.

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Section: Introductionmentioning

confidence: 99%

Observer-based adaptive neural network backstepping sliding mode control for switched fractional order uncertain nonlinear systems with unmeasured states

Chen

Cao

Yuan

et al. 2021

Measurement and Control

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“…The development of NDOB on pendulum system can be found in [39]. Otherwise, some advanced disturbance compensations based on a neural network system were investigated in [40][41][42][43][44]. To simplify the procedure of the design of a disturbance observer, this study proposed a new DOB to scope the disturbance and uncertainty of a MEMS gyroscope under the conjunction of an unknown disturbance in exogenous form.…”

Section: Introductionmentioning

confidence: 99%

Robust Observer Based on Fixed-Time Sliding Mode Control of Position/Velocity for a T-S Fuzzy MEMS Gyroscope

Giap

Nguyen

et al. 2021

IEEE Access

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“…An adaptive command filtered neuro-fuzzy controller is developed for fractional-order nonlinear systems with unknown control directions and input quantization in [32]. Some neural network strategies with fuzzy systems have been investigated for nonlinear systems [33]- [40].…”

Section: Introductionmentioning

confidence: 99%

Double Recurrent Perturbation Fuzzy Neural Network Fractional-Order Sliding Mode Control of Micro Gyroscope

2021

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In this paper, a fractional-order sliding mode control method based on a double recurrent perturbation fuzzy neural network (DRPFNN) is proposed for a micro gyroscope system with parameter uncertainty and external disturbance. The DRPFNN is used to realize the adaptive estimation of the unknown part, which ensures that the controller is independent of the accurate mathematical model. The sine-cosine perturbation membership function is used to deal with the uncertainty of rules in neural network, increasing the accuracy, reduce the calculation load, and simplifying computational complexity. In addition, double recurrent links are added to transmit more information with superior dynamic ability. Then, the proposed control system is composed of a DRPFNN estimator and a fractional-order sliding mode controller (FSMC). The fractional calculus operator is introduced into the sliding surface to improve the controller flexibility. The parameter adaptive laws in the neural network are designed to be adaptively stabilized to the optimal value. Finally, simulation studies verify the effectiveness of the proposed control method, showing it can obtain higher control accuracy and enhance robustness than the traditional sliding mode control method. INDEX TERMSDouble recurrent perturbation fuzzy neural network; Recurrent neural network; Sliding mode control; Fractional order; Micro gyroscope.

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Fuzzy Multiple Hidden Layer Recurrent Neural Control of Nonlinear System Using Terminal Sliding-Mode Controller

Cited by 116 publications

References 38 publications

Observer-based adaptive neural network backstepping sliding mode control for switched fractional order uncertain nonlinear systems with unmeasured states

Observer-based adaptive neural network backstepping sliding mode control for switched fractional order uncertain nonlinear systems with unmeasured states

Robust Observer Based on Fixed-Time Sliding Mode Control of Position/Velocity for a T-S Fuzzy MEMS Gyroscope

Double Recurrent Perturbation Fuzzy Neural Network Fractional-Order Sliding Mode Control of Micro Gyroscope

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