An adaptive neural network-based controller for car driving simulators

Mạnh, Cường Nguyễn; Manh, Tien Ngo; Kim, Duyen Ha Thi; Van, Quyen Nguyen; Nguyễn, Tùng Lâm

doi:10.1080/00207179.2021.1980823

Cited by 6 publications

(8 citation statements)

References 47 publications

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“…According to Tien et al (2019) and Manh et al (2021), the dynamic model based on Euler-Lagrange form is described as follows…”

Section: System Dynamics Modelingmentioning

confidence: 99%

“…For the given system, nonlinear model-based controllers, including backstepping and SMC, can achieve the control target of reference tracking, provided that accurate mathematical model is available. A modern control method using radial basis function neural network with Lyapunov-based adaptive law as by Manh et al (2021) not only copes with this kind of problem but eliminates the drawbacks of these controllers. However, the constraints of system states, control inputs, and experimental parameters are not considered, and thus the actuator constraint and even the stability cannot be satisfied.…”

Section: System Dynamics Modelingmentioning

confidence: 99%

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Adaptive fuzzy Lyapunov-based model predictive control for parallel platform driving simulators

Mạnh

Nguyen

Duy

et al. 2022

Transactions of the Institute of Measurement and Control

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The paper proposes an adaptive Lyapunov-based nonlinear model predictive control (MPC) to cope with the problems in nonlinear systems subjecting to system constraints and unknown disturbances of the parallel car driving simulator. Commonly, standard nonlinear controllers could guarantee the overall system stability for the parallel structure. However, the constraints tend to impact the control performance and stability adversely. Therefore, MPC plays a vital role in the proposed technique to explicitly consider all the practical constraints and simultaneously enhance the system’s robustness. Nevertheless, the accuracy of the modeling process has a significant effect on the MPC performance, and thus, the convergence cannot be guaranteed in the presence of the model uncertainties. To overcome this problem, by the merit of the fuzzy adaptive law, the control system takes the disturbances and unmodelled parameters into account. Moreover, the feasibility and stability of the approach, which is the fundamental problem of MPC, are ensured according to the Lyapunov-based nonlinear controller, backstepping aggregated with sliding mode control (SMC), and hence inherit advantages of these controls. Simulation results show the efficiency and superior constituted controllers of the proposed method.

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“…According to Tien et al (2019) and Manh et al (2021), the dynamic model based on Euler-Lagrange form is described as follows…”

Section: System Dynamics Modelingmentioning

confidence: 99%

Section: System Dynamics Modelingmentioning

confidence: 99%

Adaptive fuzzy Lyapunov-based model predictive control for parallel platform driving simulators

Mạnh

Nguyen

Duy

et al. 2022

Transactions of the Institute of Measurement and Control

Self Cite

View full text Add to dashboard Cite

show abstract

“…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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“…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. The research carried out associated RBFNN and Fuzzy Logic with designing an adaptive controller based on the DSC approach to ensure high precision of robot movement under the serious effects of disturbances.…”

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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An adaptive neural network-based controller for car driving simulators

Cited by 6 publications

References 47 publications

Adaptive fuzzy Lyapunov-based model predictive control for parallel platform driving simulators

Adaptive fuzzy Lyapunov-based model predictive control for parallel platform driving simulators

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

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

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