Adaptive Global Sliding-Mode Control for Dynamic Systems Using Double Hidden Layer Recurrent Neural Network Structure

Chu, Yundi; Fei, Juntao

doi:10.1109/tnnls.2019.2919676

Cited by 205 publications

(108 citation statements)

References 32 publications

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“…where, parameters D, M and  will be unknown [1][2][3][4][5][6]. The proposed enhanced NN to estimate and adjust them by: 22) is written as follows: is an approximate error of weight vector.…”

Section: Adaptive Neuro-fuzzy Networkmentioning

confidence: 99%

“…Besides, to modify the control system efficiency, a fuzzy system based on the upper bound estimation and observation error is applied. In [1][2][3][4][5][6], the ship's dynamic positioning controller scheme was developed with the supposition that the motion formulations could be considered as a set of fixed rotating angles. In 1990, nonlinear control theories were employed to design DP controller where linear theories had been removed.…”

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confidence: 99%

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Dynamic modeling and adaptive controlling in GPS-intelligent buoy (GIB) systems based on neural-fuzzy networks

et al. 2020

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Recently, various relations and criteria have been presented to establish a proper relationship between control systems and control Global Positioning System (GPS)-intelligent buoy system. Given the importance of controlling the position of buoys and the construction of intelligent systems, in this paper, dynamic system modeling is applied to position marine buoys through the improved neural network with a backstepping technique. This study aims at developing a novel controller based on adaptive fuzzy neural network to optimally track the dynamically positioned vehicle on water with unavailable velocities and unidentified control parameters. In order to model the network with the proposed technique, uncertainties and the unwanted disturbances are studied in the neural network. The presented study aims at developing a neural controlling which applies the vectorial back-stepping technique to the surface ships, which have been dynamically positioned with undetermined disturbances and ambivalences. Moreover, the objective function is to minimize the output error for the neural network (NN) based on closed-loop system. The most important feature of the proposed model for the positioning buoys is its independence from comparative knowledge or information on the dynamics and the unwanted disturbances of ships.The numerical and obtained consequences demonstrate that the controller system can adjust the routes and the position of the buoys to the desired objective with relatively few position errors.

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Section: Adaptive Neuro-fuzzy Networkmentioning

confidence: 99%

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confidence: 99%

Dynamic modeling and adaptive controlling in GPS-intelligent buoy (GIB) systems based on neural-fuzzy networks

et al. 2020

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“…Theorem 1. If the modified control law (29), with the nonsingular terminal sliding surface (7) and the adaptive law of the DRFNN designed as (30)-(34), is applied to the gyroscope system defined by (5), then the system's tracking error can converge to origin in a finite time, and the unknown system uncertainties can be estimated online by the DRFNN with high robustness:…”

Section: Assumptionmentioning

confidence: 99%

“…Lee and Teng proposed a complex fuzzy neural network structure and expanded the basic ability of the FNN to cope with complicated nonlinear problem [27]. A new output feedback neural structure which has two hidden layers is proposed in [28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Novel Fuzzy Neural Nonsingular Terminal Sliding Mode Control of MEMS Gyroscope

Wang

Fei²

2019

Complexity

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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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“…Furthermore, the comparison study between the fractional-order controller and integer-order one is also conducted in order to demonstrate the better performance of the proposed controller in total harmonic distortion (THD), a significant index to evaluate the current quality in the smart grid.Adaptive fractional fuzzy sliding mode controls and adaptive fuzzy-neural fractional finite-time sliding controllers are developed for active power filters [19][20][21]. In the nonlinear systems, unknown nonlinearities can be approximated by intelligent methods such as fuzzy systems [22][23][24][25][26][27][28][29] and neural networks [30][31][32][33][34][35]. Intelligent control methods have been investigated for dynamic systems.…”

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confidence: 99%

Adaptive Backstepping Fractional Fuzzy Sliding Mode Control of Active Power Filter

2019

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An adaptive fractional-order fuzzy control method for a three-phase active power filter (APF) using a backstepping and sliding mode controller is developed for the purpose of compensating harmonic current and stabilizing the DC voltage quickly. The dynamic model of APF is changed to an analogical cascade system for the convenience of the backstepping strategy. Then a fractional-order sliding mode surface is designed and a fuzzy controller is proposed to approximate the unknown term in the controller, where parameters can be adjusted online. The simulation experiments are conducted and investigated using MATLAB/SIMULINK software package to verify the advantage of the proposed controller. Furthermore, the comparison study between the fractional-order controller and integer-order one is also conducted in order to demonstrate the better performance of the proposed controller in total harmonic distortion (THD), a significant index to evaluate the current quality in the smart grid.

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Adaptive Global Sliding-Mode Control for Dynamic Systems Using Double Hidden Layer Recurrent Neural Network Structure

Cited by 205 publications

References 32 publications

Dynamic modeling and adaptive controlling in GPS-intelligent buoy (GIB) systems based on neural-fuzzy networks

Dynamic modeling and adaptive controlling in GPS-intelligent buoy (GIB) systems based on neural-fuzzy networks

Novel Fuzzy Neural Nonsingular Terminal Sliding Mode Control of MEMS Gyroscope

Adaptive Backstepping Fractional Fuzzy Sliding Mode Control of Active Power Filter

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