Adaptive Fuzzy Super‐Twisting Sliding Mode Control for Microgyroscope

Fei, Juntao; Feng, Zuren

doi:10.1155/2019/6942642

Cited by 64 publications

(36 citation statements)

References 28 publications

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“…Substituted controller (27) into system (9) and obtained equivalent to system (14). According to Lemma 4, system (9) can converge in fixed-time under the design of adaptive fixed-time controller (27), sliding mode fixed-time disturbance observer (24), and adaptive law (30).…”

Section: Comprehensive Design Of Adaptive Fixed-time Slidingmentioning

confidence: 99%

“…In order to get rid of the dependence of the convergence time of the system on the initial state and improve the convergence rapid, fixed-time observers have been proposed and further applied in literatures [27,29]. Unfortunately, in the above research results, with the assumption that the upper bound of disturbance is known, the biggest superiority of super-twisting algorithm is that it can effectively solve the chattering problem of the control system and enable the system to converge in a limited time and only require the derivative of the disturbance to be bounded [30]. It can be seen that the disturbance observer designed based on the fixed-time convergent super twisting algorithm also possesses the advantages mentioned above.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Sliding Mode Fixed‐Time Tracking Control Based on Fixed‐Time Sliding Mode Disturbance Observer with Dead‐Zone Input

Wang

et al. 2019

Complexity

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Aiming at the problem of fixed-time trajectory tracking control for high-order dynamic systems with external time-varying disturbance and input dead-zone, an adaptive fixed-time sliding mode control algorithm is proposed by employing a fixed-time sliding mode disturbance observer (FTSMDO) and high-order fixed-time sliding mode algorithm. Firstly, a FTSMDO is presented for the problem that estimating the compound disturbance is composed of input dead-zone and time-varying external disturbance in the higher-order dynamic system, which cannot be measured accurately. Furthermore, for the case that the total disturbance of the system has an unknown upper bound, the corresponding adaptive law is designed to estimate the unknown upper bound, and the fixed-time controller is designed based on FTSMDO algorithm to make all state variables converge in a fixed-time. Based on Lyapunov technique, the fixed-time convergence performance of the proposed algorithm is proved. The effectiveness of the presented fixed-time control algorithm is verified by simulating the depth tracking control of the underactuated underwater vehicle.

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Section: Comprehensive Design Of Adaptive Fixed-time Slidingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive Sliding Mode Fixed‐Time Tracking Control Based on Fixed‐Time Sliding Mode Disturbance Observer with Dead‐Zone Input

Wang

et al. 2019

Complexity

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“…Its significant advantage is its strong robustness to uncertain parameters and external disturbances. Therefore, it has been widely used in aerospace, robot control, and chemical control [1][2][3][4][5][6][7][8]. However, the traditional sliding mode control takes the linear sliding mode as the "sliding mode" of the system.…”

Section: Introductionmentioning

confidence: 99%

Nonsingular Fast Terminal Sliding Mode Tracking Control for a Class of Uncertain Nonlinear Systems

Chen

Wei

Huang

2019

Journal of Control Science and Engineering

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Aiming at the tracking control problem of a class of uncertain nonlinear systems, a nonsingular fast terminal sliding mode control scheme combining RBF network and disturbance observer is proposed. The sliding mode controller is designed by using nonsingular fast terminal sliding mode and second power reaching law to solve the problem of singularity and slow convergence in traditional terminal sliding mode control. By using the universal approximation of RBF network, the unknown nonlinear function of the system is approximated, and the disturbance observer is designed by using the hyperbolic tangent nonlinear tracking differentiator (TANH-NTD) to estimate the interference of the system and enhance the robustness of the system. The stability of the system is proved by the Lyapunov principle. The numerical simulation results show that the method can shorten the system arrival time, improve the tracking accuracy, and suppress the chattering phenomenon.

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“…Subjected to the SMC, time delay compensation has been synthesized into the robust control strategy to provide faster convergence with negligible chattering [11]. Fei and Feng proposed a super-twisting sliding mode control algorithm applied in the gyroscope system, which can weaken the effect of chattering [12]. Although the speed of asymptotic convergence can be controlled by changing the sliding surface parameters, the conventional sliding mode tracking error still has difficulty in arriving to convergence bound in finite time [8].…”

Section: Introductionmentioning

confidence: 99%

Novel Fuzzy Neural Nonsingular Terminal Sliding Mode Control of MEMS Gyroscope

Wang

Fei²

2019

Complexity

Self Cite

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This paper attempts to improve the robustness and rapidity of a microgyroscope sensor by presenting a double-loop recurrent fuzzy neural network based on a nonsingular terminal sliding mode controller. Compared with the traditional control method, the proposed strategy can obtain faster dynamic response speed and lower steady-state error with high robustness in the presence of system uncertainties and external disturbances. A nonlinear terminal sliding mode controller is designed to guarantee finite-time high-precision convergence of the sliding surface and meanwhile to eliminate the effect of singularity. Moreover, an exponential approach law is used to accelerate the convergence rate of the system to the sliding surface. For suppressing the chattering, the symbolic function in the ideal sliding mode is replaced by the saturation function. To suppress the effect of model uncertainties and external disturbances, a double-loop recurrent fuzzy neural network is introduced to approximate and compensate system nonlinearities for the gyroscope sensor. At the same time, the double-loop recurrent fuzzy neural network can effectively accelerate the speed of parameter learning by introducing the adaptive mechanism. Simulation results indicate that the control system with the proposed controller is easily implemented, and it has higher tracking precision and considerable robustness to model uncertainties compared with the existing controllers.

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Adaptive Fuzzy Super‐Twisting Sliding Mode Control for Microgyroscope

Cited by 64 publications

References 28 publications

Adaptive Sliding Mode Fixed‐Time Tracking Control Based on Fixed‐Time Sliding Mode Disturbance Observer with Dead‐Zone Input

Adaptive Sliding Mode Fixed‐Time Tracking Control Based on Fixed‐Time Sliding Mode Disturbance Observer with Dead‐Zone Input

Nonsingular Fast Terminal Sliding Mode Tracking Control for a Class of Uncertain Nonlinear Systems

Novel Fuzzy Neural Nonsingular Terminal Sliding Mode Control of MEMS Gyroscope

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