Adaptive neural nonsingular terminal sliding mode control for MEMS gyroscope based on dynamic surface controller

Lei, Dandan; Fei, Juntao

doi:10.1007/s13042-017-0643-2

Cited by 15 publications

(4 citation statements)

References 21 publications

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“…For achieving the control performance, some important finitetime control strategies are presented in [15][16][17][18]. In [19], a nonsingular terminal sliding mode (NTSM) controller was proposed by introducing a nonsingular terminal sliding mode controller, which ensures the control system could reach the sliding surface and converge to equilibrium point in a finite period of time. Zhang et al [20,21] proposed an integral terminal sliding mode control scheme with the neural network for the MEMS gyroscope in the presence of system nonlinearity.…”

Section: Introductionmentioning

confidence: 99%

Disturbance Observer-Based Sliding Mode Control for MEMS with Prescribed Performance

Wang

Song

Dong

et al. 2022

Mathematical Problems in Engineering

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In this study, we study the performance guaranteed tracking problem for the uncertain MEMS gyroscope dynamics. A fixed-time nonlinear function and the transformed error are introduced to establish the preset time control property for the closed-loop system. Then, a funnel sliding mode controller is presented to stabilize the system. To compensate for the uncertainty and external disturbance, a novel lumped disturbance observer is proposed. Different from most existing compensation control results for the MEMS gyroscope with antidisturbance that can only achieve uniformly ultimately bounded, the proposed control scheme is able to ensure that the system errors converge to the preassigned compact sets within a given time rather than infinite time and the system outputs track the desired command asymptotically. Simulation verification also confirms the effectiveness of the proposed approach.

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Section: Introductionmentioning

confidence: 99%

Disturbance Observer-Based Sliding Mode Control for MEMS with Prescribed Performance

Wang

Song

Dong

et al. 2022

Mathematical Problems in Engineering

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“…Rahmani (Rahmani, 2018) presented the fractional-order integral TSMC combined with proportional–integral–derivative (PID) control for the stabilization of MEMS gyroscope. In Lei and Fei (2018), a NTSMC is presented for the MEMS gyroscope, which is combined with a radial basis function (RBF) neural networks. In Rahmani et al (2018), a new super-twisting proportional–integral–derivative–sliding mode control (PID–SMC) is proposed for the stabilization of MEMS gyroscope.…”

Section: Introductionmentioning

confidence: 99%

Nonsingular terminal sliding mode control for micro-electro-mechanical gyroscope based on disturbance observer: Linear matrix inequality approach

Jafari

Mobayen

Roth

et al. 2021

Journal of Vibration and Control

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The aim of this article is to design a nonsingular terminal sliding mode control method based on disturbance observer for the stabilization of the micro-electro-mechanical systems under lumped perturbation. By using the nonsingular terminal sliding mode control scheme, the state trajectories of the system achieve the switching surface and approach to the origin in the finite time. Also, by utilizing the disturbance observer, the finite-time convergence of disturbance error is assured. In the process of design, the optimized coefficients of the sliding surface are calculated in the form of linear matrix inequality. Simulation results for a micro-electro-mechanical gyroscope are illustrated to exhibit the validity of the planned approach in comparison with the other methods.

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“…21 A modified adaptive neural DSC for morphing aircraft with input and output constraints was investigated by Wu et al 22 Adaptive neural controllers based on dynamic surface controller for MEMS gyroscope was derived by Lei and Fei. 23 Adaptive NN with sliding-mode controllers have been developed for dynamic system by Fei and colleagues 24,25 and Chu and Fei. 26 Motivated by the previous research, this article proposed a Lyapunov-based adaptive double neural network with a dynamic surface control (DNNDSC) for a microgyroscope to achieve the trajectory tracking because conventional controller cannot realize a desired dynamic behavior.…”

Section: Introductionmentioning

confidence: 99%

Adaptive double neural network control for micro-gyroscope based on dynamic surface controller

Fang

Lei

Fei

2019

Advances in Mechanical Engineering

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An adaptive double neural network with a dynamic surface control scheme is investigated for a micro-gyroscope in the presence of manufacturing errors and disturbances. A dynamic surface controller, where a first-order filter is introduced in each step, is proposed to reduce the parameters and the computational complexity. One radical basis function neural network is used to approximate the lumped dynamics of the micro-gyroscope. Another radical basis function neural network is designed to approximate a sliding-mode controller to compensate the approximation errors in order to weaken the influence of the approximation errors. Simulation studies prove the effectiveness of the proposed strategy, demonstrate the proposed strategy could reduce the chattering, shorten the tracking time, and improve the tracking performance.

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Adaptive neural nonsingular terminal sliding mode control for MEMS gyroscope based on dynamic surface controller

Cited by 15 publications

References 21 publications

Disturbance Observer-Based Sliding Mode Control for MEMS with Prescribed Performance

Disturbance Observer-Based Sliding Mode Control for MEMS with Prescribed Performance

Nonsingular terminal sliding mode control for micro-electro-mechanical gyroscope based on disturbance observer: Linear matrix inequality approach

Adaptive double neural network control for micro-gyroscope based on dynamic surface controller

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