Neural Network Based Adaptive Control of Web Transport Systems

Mạnh, Cường Nguyễn; Van, Manh Tran; Duc, Dinh Nguyen; Tung, Lam Nguyen; Tien, Dung Pham; Thi, Ly Tong

doi:10.1109/icsse.2019.8823550

Cited by 10 publications

(2 citation statements)

References 6 publications

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“…It is more powerful and superior to PID controllers because it reduces dependence on model inaccuracies and external disturbances. So there has been much research to develop this SMC, and there are many remarkable achievements [13][14][15][16]. In [17], the authors used a SMC to bring the synchronizing error of the induction To overcome these inadequacies, the research team has developed a sliding mode controller integrated moment of inertia observer.…”

Section: Introductionmentioning

confidence: 99%

A sliding mode controller integrated moment of inertia observer of web handling systems

2023

jsthaui

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This study presents web handling systems with the characteristic of a changing moment of inertia and operating radius of rolls. Commonly, sliding mode control (SMC) is used to control the web tension of this system due to its robustness against modeling imprecision. However, this method still needs to consider the components of uncertainty, precisely the moment of inertia. A sliding mode controller integrated moment of inertia observer is the novel of the paper. The stability of the controlled system is proved by using Lyapunov's stability theory, also presented in this paper. The simulation results are compared to clarify that the proposed controller has reliable performance.

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Section: Introductionmentioning

confidence: 99%

A sliding mode controller integrated moment of inertia observer of web handling systems

2023

jsthaui

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“…The authors innovated a Backstepping-based Control structure incorporated with an RBF network for roll inertia change estimation. 45 An RBF Neural Network-based Backstepping Sliding Mode Control (RBFNN-BSMC) 46,47 in which changeable inertia of rolls is approximated for the adaptive ability of control system was addressed to reach tracking and flexibility goals. Also, an equivalent control architecture was suggested as in the study, 48 of which remarkable innovation is to estimate the derivative of virtual control signal such that the "explosion of terms" phenomenon is successfully eliminated.…”

Section: Introductionmentioning

confidence: 99%

Model‐free hierarchical control with fractional‐order sliding surface for multisection web machines

Dang

Nguyen

Tran

et al. 2022

Adaptive Control & Signal

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Due to the nonlinearities and dynamical properties of multisection web machines, it is essential to accomplish an appropriate mathematical model as well as design a compatible control scheme. A generalized fully driven web winding model is constructed taking unpredictable changes of moment of inertia and radius of rolls into consideration. In this article, a fractional‐order sliding surface algorithm‐based hierarchical control frame is applied to multisection web machines to deal with matched and mismatched uncertainties and disturbances. In addition, the Radius Basis Function neural network is deployed to estimate necessary system dynamics and the proposed control scheme taking advantage of first‐order low‐pass filters to produce virtual control signals without complicated calculation and attenuate the explosion of terms phenomenon. Finally, the semi‐global stability of the entire system is determined using Lyapunov's stability theory. The simulation results are presented to clarify the superior performance of robust control algorithm and approximation law in the presence of nonideal elements.

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