Adaptive Controller Designed for High-Speed Train ATO System with Nonlinear and Uncertain Resistance

Luo, Huan; Xu, Hong

doi:10.4028/www.scientific.net/amm.300-301.1551

Cited by 3 publications

(6 citation statements)

References 11 publications

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“…For system (3) under the i.i.c. (6) and the control law proposed in ( 10)-( 12) with p > 0, q > 0, k b,2 + k r,2 + 𝜅 1 k b,1 ≤ k s,2 , 𝜅 2 > 0, the uniform convergence of the state tracking error e j,i (t), j = 1, 2 is guaranteed as i → ∞. Besides, the constraints on the system state will not be violated, that is, |s i (t) − s r (t)| < k s,1 and |v i (t)| < k s,2 in each trial of HST operation.…”

Section: Ilc Design and Analysismentioning

confidence: 99%

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Iterative learning control for high‐speed trains with velocity and displacement constraints

Huang

Chen

et al. 2022

Intl J Robust & Nonlinear

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In this article, a novel iterative learning control (ILC) scheme is presented for the operation control of high-speed train (HST), where the velocity and displacement of HST are strictly limited to ensure safety and comfort. The model of HST constructed in the article is practical in the sense that both parametric and nonparametric uncertainties of system are addressed simultaneously. Backstepping design with the newly proposed barrier Lyapunov function is incorporated in analysis to ensure the uniform convergence of the state tracking error and that the constraint requirements on velocity and displacement would not be violated during the whole operation process. In the end, a simulation study is presented to demonstrate the efficacy of the proposed ILC law.

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Section: Ilc Design and Analysismentioning

confidence: 99%

“…1 Many optimization techniques have been applied to the operation control of train to generate a desired speed profile. 2 Meanwhile, much effort has also been put towards the speed tracking control of HST, such as fuzzy logic control, 3,4 adaptive control, 5,6 neural network control 7 and so forth.…”

Section: Introductionmentioning

confidence: 99%

Iterative learning control for high‐speed trains with velocity and displacement constraints

Huang

Chen

et al. 2022

Intl J Robust & Nonlinear

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“…Case 1. The traditional adaptive control, developed by the basic backstepping technique [12], was used and the control law is generated by…”

Section: Simulationsmentioning

confidence: 99%

“… Case 1.The traditional adaptive control, developed by the basic backstepping technique , was used and the control law is generated by

({, \begin{matrix} u = \hat{m} [{\hat{θ}}^{T} φ (x_{2}) - ϕ (z, ψ)], \\ \dot{\hat{m}} = - γ [{\hat{θ}}^{T} φ (x_{2}) - ϕ (z, ψ)] z_{2}, \\ \dot{\hat{θ}} = - Γφ (x_{2}) z_{2}, \end{matrix})

with the following definitions

\begin{matrix} z_{1} = x_{1} - x_{⋆}, \\ z_{2} = l_{1} x_{1} + x_{2} - l_{1} x_{⋆} - {\overset{x}{true}}_{⋆}, \\ ψ = l_{1} x_{2} - l_{1} {\overset{x}{true}}_{⋆} - {\overset{x}{true}}_{⋆}, \\ ϕ (z, ψ) = z_{1} + l_{2} z_{2} + ψ . \end{matrix}

Case 2.The basic I&I adaptive control was used, and the only difference with the PPB based control was that no error transformation was employed by just defining z 1 = x 1 − x ⋆ . …”

Section: Simulationsmentioning

confidence: 99%

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Immersion and Invariance Based Robust Adaptive Control of High‐Speed Train with Guaranteed Prescribed Performance Bounds

Luo¹,

Xu²,

Liu³

2015

Asian Journal of Control

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This paper proposes a novel robust adaptive algorithm for train tracking control with guaranteed prescribed transient and steady-state performance. As speed increases, the inherent time-varying uncertainties and unmodeled dynamics in the longitudinal dynamics of a high-speed train seriously impacts the tracking performance of automatic train operation. To improve train operation performance, an estimator based on immersion and invariance technology is developed to recover the unknown and time-varying plant parameters, and it renders the estimation error converging to a bounded residual set exponentially while providing more freedom for the controller. After certain error transformation, the prescribed tracking performance is introduced into the controller design. Then, an input-to-stable stable controller is developed through the backstepping technique, and it is proven that stabilization of the transformed system is sufficient to guarantee the prescribed performance. Rigorous theoretical analysis for the presented algorithm is provided, and a series of simulation studies also are given to verify the effectiveness of it.

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Motion control and positioning system of multi-sensor tunnel defect inspection robot: from methodology to application

Keqiang

Zhong

Zhao³

et al. 2023

Sci Rep

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As the mileage of subway is increasing rapidly, there is an urgent need for automatic subway tunnel inspection equipment to ensure the efficiency and frequency of daily tunnel inspection. The subway tunnel environment is complex, it cannot receive GPS and other satellite signals, a variety of positioning sensors cannot be used. Besides, there are random interference, wheel and rail idling and creep. All the above results in poor performance of conventional speed tracking and positioning methods. In this paper, a multi-sensor motion control system is proposed for the subway tunnel inspection robot. At the same time, a trapezoidal speed planning and a speed tracking algorithm based on MPC (Model Predictive Control) are proposed, which simplify longitudinal dynamics model to overcome the complex and variable nonlinear problems in the operation of the maintenance robot. The optimal function of speed, acceleration and jerk constraint is designed to make the tunnel inspection robot achieve efficient and stable speed control in the subway tunnel environment. In this paper, the "INS (inertial navigation system) + Odometer" positioning method is proposed. The difference between the displacement measured by the inertial navigation system and the displacement calculated by the odometer is taken as the measurement value, which reduces the dimension of the conventional algorithm. The closed-loop Kalman filter is used to establish the combined positioning model, and the system error can be corrected in real time with higher accuracy. The algorithms were verified on the test line. The displacement target was set to be 1 km and the limit speed was 60 km/h. The overshooting error of the speed tracking algorithm based on trapezoidal velocity planning and MPC was 0.89%, and the stability error was 0.32%. It improved the accuracy and stability of the speed following, and was much better than the PID speed tracking algorithm. At the speed of 40 km/h, the maximum positioning error of the robot within 2 km is 0.15%, and the average error is 0.08%. It is verified that the multi-sensor fusion positioning algorithm has significantly improved the accuracy compared with the single-odometer positioning algorithm, and can effectively make up for the position error caused by wheel-rail creep and sensor error.

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Adaptive Controller Designed for High-Speed Train ATO System with Nonlinear and Uncertain Resistance

Cited by 3 publications

References 11 publications

Iterative learning control for high‐speed trains with velocity and displacement constraints

Iterative learning control for high‐speed trains with velocity and displacement constraints

Immersion and Invariance Based Robust Adaptive Control of High‐Speed Train with Guaranteed Prescribed Performance Bounds

Motion control and positioning system of multi-sensor tunnel defect inspection robot: from methodology to application

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