Adaptive Dynamic Surface Control for Car Driving Simulator based on Artificial Neural Network

Tien, Kiem Nguyen; Kim, Duyen Ha Thi; Manh, Tien Ngo; Mạnh, Cường Nguyễn; Bach, Ngoc Pham Van; Quang, Hiep Do

doi:10.1109/morse48060.2019.8998749

Cited by 3 publications

(2 citation statements)

References 12 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%

“…Besides, the combination of backstepping and SMC is another strategy to enhance the performance by taking advantage of both controllers (Thi et al, 2019;Wang et al, 2020). In order to solve the phenomenon of ''explosion of terms,'' the dynamic surface control (DSC) technique has been proposed (Qinyang et al, 2017;Tien et al, 2019), in which multiple sliding surfaces combined with the backstepping technique and a low-pass filter for each computation step are leveraged. In addition, there are some other effective methods, such as fuzzy techniques, which are capable of estimating virtual signals and minimizing chattering (Zhang et al, 2020).…”

Section: Introductionmentioning

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: Introductionmentioning

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

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An adaptive neural network-based controller for car driving simulators

Mạnh

Manh

Kim

et al. 2021

International Journal of Control

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Dynamics Model and Design of SMC-type-PID Control for 4DOF Car Motion Simulator

Ngoc¹,

Thanh²

2020

Adv. sci. technol. eng. syst. j.

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4-DOF car motion simulator helps to simulate real-life experiences that drivers do not have the opportunity to access the real environment. The dynamics equation of 4-DOF car motion simulator is a very complex problem with many uncertain parameters, so it requires intelligent control algorithms. Sliding mode controller (SMC) can achieve good tracking performance and robustness to the disturbances, but SMC has worse stability and reliability than PID controller. As a most widely used controller, PID controller has many obvious advantages, but it has poorer tracking performance than SMC. In this paper, a control method of sliding mode type PID controllers is proposed to fully combine the advantages of the two controllers. In this study, based on the dynamics equation of 4-DOF car motion simulators the author develop two algorithms to control sliding mode control type PID (SMC-type-PID) and sliding mode controller type PID with GA optimization for 4-DOF car motion simulator. Firstly, the authors used Lyapunop theory to prove the stability of the system, next presenting the simulation results of two control algorithms with different uncertain components and comparing them to find and demonstrate the effectiveness of the new control method applied to the 4-DOF car motion simulator.

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Adaptive Dynamic Surface Control for Car Driving Simulator based on Artificial Neural Network

Cited by 3 publications

References 12 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

An adaptive neural network-based controller for car driving simulators

Dynamics Model and Design of SMC-type-PID Control for 4DOF Car Motion Simulator

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