Fractional Sliding-Mode Control for Microgyroscope Based on Multilayer Recurrent Fuzzy Neural Network

Fei, Juntao; Wang, Zhe; Liang, Xiao; Feng, Zuren; Xue, Yuncan

doi:10.1109/tfuzz.2021.3064704

Cited by 83 publications

(39 citation statements)

References 38 publications

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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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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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“…The combination of sliding mode control and adaptive control obtains an adaptive sliding mode control method, which is widely used in the control of non-linear systems [6], [7], [8], [9], [10]. In the design of sliding mode controller, fractional integral and derivative can be used to obtain robustness and finite time stability [11].…”

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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“…In case of discrete singular systems subjected to randomly occurring mixed time-delays adaptive robust SMC is formulated in [29]. Recently, fractional sliding mode control for micro gyroscope has been implemented in [30] while fractional-order sliding-mode control with application to active power filter has been designed in [31]. Supertwisting SMC is a higher order controller which has the property to reduce chattering to almost zero [32], [33], an undesirable phenomenon unbearable especially for brain tumor system.…”

Section: Introductionmentioning

confidence: 99%

Variable Structure Based Control for the Chemotherapy of Brain Tumor

et al. 2021

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Brain Tumor is an unwanted mass when cells grow abnormally. It puts pressure on certain areas of the brain which dangerously affects its functioning. For severe tumors like malignant, surgery is not a good option because they do not have definite boundaries and are closely attached with the other healthy tissues of the brain. So chemotherapy is preferable in this case to avoid damaging the healthy cells. The main purpose of this research is to suggest a controller for drug injection so that the tumor cells may reach the desired reference value of zero. In this paper, variable structure based nonlinear control algorithms: sliding mode control, integral sliding mode control, double integral sliding mode control and super-twisting sliding mode controllers have been proposed to reduce the tumor cells, maintain a safe number of healthy cells, keep the immune cells above a certain value and ensure suitable amount of chemotherapy drug. Lyapunov based stability theory has been used to prove stability of SMC, ISMC, DISMC and ST-SMC. MATLAB/Simulink environment has been used to analyze the performance of the proposed controllers on the basis of chattering, rate of convergence, undershoot/overshoot etc. and on the basis of these results drug through most suitable controller is advised. Among the proposed controllers, due to its better convergence, reduced chattering and less amount of drug used, ST-SMC is suggested for the chemotherapy of brain tumor. INDEX TERMSBrain tumor, Sliding mode control (SMC), Integral sliding mode control (ISMC), Double integral sliding mode control (DISMC), Super-twisting sliding mode control (ST-SMC), Chemotherapy, Cancer.

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Fractional Sliding-Mode Control for Microgyroscope Based on Multilayer Recurrent Fuzzy Neural Network

Cited by 83 publications

References 38 publications

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

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

Variable Structure Based Control for the Chemotherapy of Brain Tumor

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