Multi-Loop Recurrent Neural Network Fractional-Order Terminal Sliding Mode Control of MEMS Gyroscope

Fei, Juntao; Wang, Zhe

doi:10.1109/access.2020.3022675

Cited by 10 publications

(9 citation statements)

References 30 publications

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“…The parameters settings of the FOPID and FOPID-GWO, are listed in Table 12. The hyper-parameters of the proposed FOPID-FOAC are given in Table (13).…”

Section: Inverted Pendulum Systemmentioning

confidence: 99%

“…On the other side, the fractional calculus was efficiently incorporated into the field of neural networks. For instant, FO-neural networks have been conducted for time series prediction [77], nonlinear system modeling and control [1,13,36]. Besides, a new fractional derivative operator with sigmoid function as the kernel was proposed in [33].…”

Section: Introductionmentioning

confidence: 99%

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Optimal fractional-order PID controller based on fractional-order actor-critic algorithm

Shalaby

El-Hossainy²,

Abozalam

et al. 2022

Neural Comput & Applic

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In this paper, an online optimization approach of a fractional-order PID controller based on a fractional-order actor-critic algorithm (FOPID-FOAC) is proposed. The proposed FOPID-FOAC scheme exploits the advantages of the FOPID controller and FOAC approaches to improve the performance of nonlinear systems. The proposed FOAC is built by developing a FO-based learning approach for the actor-critic neural network with adaptive learning rates. Moreover, a FO rectified linear unit (RLU) is introduced to enable the AC neural network to define and optimize its own activation function. By the means of the Lyapunov theorem, the convergence and the stability analysis of the proposed algorithm are investigated. The FO operators for the FOAC learning algorithm are obtained using the gray wolf optimization (GWO) algorithm. The effectiveness of the proposed approach is proven by extensive simulations based on the tracking problem of the two degrees of freedom (2-DOF) helicopter system and the stabilization issue of the inverted pendulum (IP) system. Moreover, the performance of the proposed algorithm is compared against optimized FOPID control approaches in different system conditions, namely when the system is subjected to parameter uncertainties and external disturbances. The performance comparison is conducted in terms of two types of performance indices, the error performance indices, and the time response performance indices. The first one includes the integral absolute error (IAE), and the integral squared error (ISE), whereas the second type involves the rising time, the maximum overshoot (Max. OS), and the settling time. The simulation results explicitly indicate the high effectiveness of the proposed FOPID-FOAC controller in terms of the two types of performance measurements under different scenarios compared with the other control algorithms.

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“…The parameters settings of the FOPID and FOPID-GWO, are listed in Table 12. The hyper-parameters of the proposed FOPID-FOAC are given in Table (13).…”

Section: Inverted Pendulum Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal fractional-order PID controller based on fractional-order actor-critic algorithm

Shalaby

El-Hossainy²,

Abozalam

et al. 2022

Neural Comput & Applic

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show abstract

“…Owing to the ability of radial basis function (RBF) neural networks (NN) for the approximation of every nonlinear function with any accuracy [3,4], the RBF NN is employed to approximate system uncertainties in [5,6]. To further improve the approximation performance of NN, finite time learning [7] and multiloop recurrent NN [8] are proposed. For the external disturbances, when the upper bounds of the disturbances are known, a robust controller is designed in [9,10] to suppress the influence of disturbances.…”

Section: Introductionmentioning

confidence: 99%

Recursive Integral Terminal Sliding Mode Control of MEMS Gyroscopes via Composite Neural Learning and Disturbance Observer

Zhang¹,

Wang

2022

Preprint

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The recursive integral terminal sliding mode control based on disturbance observer and composite neural learning is studied in this paper to control the dynamics of MEMS gyroscopes in the presence of system uncertainties and external disturbances. To achieve more accurate approximation accuracy of system uncertainties, the composite neural learning is employed, where the updating law of the weight vector is designed by the tracking error and the prediction error constructed by serial-parallel estimation model. To further improve the system robustness, the recursive integral terminal sliding mode controller is utilized, where faster convergence can be obtained by forcing the system state to start from the sliding mode manifold at the initial time. To deal with external disturbances with unknown upper bounds, the disturbance observer is designed. Furthermore, simulations results are demonstrated to verify that faster convergence and higher tracking accuracy can be achieved under the proposed method.

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“…An adaptive sliding mode control and fuzzy compensator were introduced for the MEMS gyroscope in [3]. The investigations of neural networks for a MEMS gyroscope can be found in [4][5][6][7]. Wang et al [8] proposed the control of the z-axis of a MEMS gyroscope by using adaptive fractionalorder sliding mode control.…”

Section: Introductionmentioning

confidence: 99%

“…However, the settling time remains high, and there exists overshoot values. Furthermore, among the previously published studies [1][2][3][4][5][6][7][8][9], few investigated disturbance observers. To estimate the disturbance of a MEMS system, a neural network was used to archive the goal [56].…”

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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Multi-Loop Recurrent Neural Network Fractional-Order Terminal Sliding Mode Control of MEMS Gyroscope

Cited by 10 publications

References 30 publications

Optimal fractional-order PID controller based on fractional-order actor-critic algorithm

Optimal fractional-order PID controller based on fractional-order actor-critic algorithm

Recursive Integral Terminal Sliding Mode Control of MEMS Gyroscopes via Composite Neural Learning and Disturbance Observer

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

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