Adaptive fault-tolerant automatic train operation using RBF neural networks

Gao, Shigen; Dong, Hairong; Ning, Bin; Chen, Yao; Sun, Xubin

doi:10.1007/s00521-014-1705-y

Cited by 38 publications

(25 citation statements)

References 23 publications

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“…Tracking control algorithm of target speed curve is the second important problem in ATO system research [16,17]. The actual speed needs to be constantly adjusted in the operating process of the train to make it track the target speed curve closely, so the requirement of the control performance for the speed controller is very high [18].…”

Section: Km H S Mmentioning

confidence: 99%

Research on Multi-Objective Optimization and Control Algorithms for Automatic Train Operation

Liu

Wang

2019

Energies

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The automatic train operation (ATO) system of urban rail trains includes a two-layer control structure: upper-layer control and lower-layer control. The upper-layer control is to optimize the target speed curve of ATO, and the lower-layer control is the tracking by the urban rail train of the optimal target speed curve generated by the upper-layer control according to the tracking control strategy of ATO. For upper-layer control, the multi-objective model of urban rail train operation is firstly built with energy consumption, comfort, stopping accuracy, and punctuality as optimization indexes, and the entropy weight method is adopted to solve the weight coefficient of each index. Then, genetic algorithm (GA) is used to optimize the model to obtain an optimal target speed curve. In addition, an improved genetic algorithm (IGA) based on directional mutation and gene modification is proposed to improve the convergence speed and optimization effect of the algorithm. The stopping and speed constraints are added into the fitness function in the form of penalty function. For the lower-layer control, the predictive speed controller is designed according to the predictive control principle to track the target speed curve accurately. Since the inflection point area of the target speed curve is difficult to track, the softness factor in the predictive model needs to be adjusted online to improve the control accuracy of the speed. For this paper, we mainly improve the optimization and control algorithms in the upper and lower level controls of ATO. The results show that the speed controller based on predictive control algorithm has better control effect than that based on the PID control algorithm, which can meet the requirements of various performance indexes. Thus, the feasibility of predictive control algorithm in an ATO system is also verified.

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Section: Km H S Mmentioning

confidence: 99%

Research on Multi-Objective Optimization and Control Algorithms for Automatic Train Operation

Liu

Wang

2019

Energies

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“…The single-point-mass model, which treats the train as a single particle, can describe the complete characteristics of the train, simplify the train modeling, and achieve good real-time performance, so it has been widely applied in the tracking control of HSTs [2,4,8,11,16,[24][25][26]. Considering train dynamics in the longitudinal direction, the force diagram of a single-point-mass model of HSTs is depicted in Figure 1.…”

Section: Description Of Train Dynamical Model Without Actuator Faultsmentioning

confidence: 99%

“…As a result, passive fault-tolerant control can compensate for fault effects much faster. Thus, many efforts on passive fault-tolerant tracking control for HSTs have been addressed [10][11][12][13][14]. In [10], a fault-tolerant controller using a so-called virtual-parameter-based backstepping adaptive control method was designed.…”

Section: Introductionmentioning

confidence: 99%

“…It is fully parameter independent and can guarantee closed-loop tracking stability of the train system. In [11], in the face of actuator faults with unknown amplitudes and occurrence time, an adaptive fault-tolerant control scheme based on radial basis function (RBF) neural networks (NN) was adopted and asymptotical position and speed tracking was achieved. In [12], two novel adaptive fuzzy fault-tolerant control schemes with proportion-integral (PI)-based sliding mode were investigated to deal with the actuator failures of HSTs with uncertain parameters, unknown non-linear dynamics, and external disturbances.…”

Section: Introductionmentioning

confidence: 99%

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Model-Independent Adaptive Fault-Tolerant Tracking Control for High-Speed Trains with Actuator Saturation

Wang

2019

Applied Sciences

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This paper investigates the fault-tolerant tracking control problem of high-speed trains (HSTs) subject to unknown model parameters with unavailable uncertainties, unmeasurable additional disturbance, and unpredictable actuator faults constrained by actuator saturation. An adaptive passive fault-tolerant tracking control strategy based on variable-gain proportion-integral-derivative (PID)-type sliding mode surface is proposed to handle the problem. Unknown model parameters, gains of the PID-type sliding mode surface, and upper bounds of the lumped system uncertainty which includes additional disturbance, modeling uncertainties, and uncertainties resulting from actuator faults, are estimated online by adaptive technology. The input saturation (actuator output saturation) constraint is handled by introducing an auxiliary signal. The proposed controller can compensate for the effects of the lumped uncertainty and the actuator faults effectively. Moreover, the controller is model-independent, which means it requires no prior knowledge of model parameters and upper bounds of the lumped uncertainty, and does not depend upon fault detection and diagnosis module. The asymptotic stability of the closed-loop train system is demonstrated by Lyapunov theory. Good fault-tolerant tracking capacity, effective anti-actuator saturation ability, and strong robustness of the proposed controller are verified via numerical simulation.

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“…ν 1i and ν 2i are small constants. δ 11 , δ 21 , δ 12 and δ 22 are small constants introduced in [40], which can prevent W * i andε i from drifting to become very large. The following statements hold:…”

Section: Remarkmentioning

confidence: 99%

Neural Adaptive Sliding-Mode Control of a Vehicle Platoon Using Output Feedback

et al. 2017

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This paper investigates the output feedback control problem of a vehicle platoon with a constant time headway (CTH) policy, where each vehicle can communicate with its consecutive vehicles. Firstly, based on the integrated-sliding-mode (ISM) technique, a neural adaptive sliding-mode control algorithm is developed to ensure that the vehicle platoon is moving with the CTH policy and full state measurement. Then, to further decrease the measurement complexity and reduce the communication load, an output feedback control protocol is proposed with only position information, in which a higher order sliding-mode observer is designed to estimate the other required information (velocities and accelerations). In order to avoid collisions among the vehicles, the string stability of the whole vehicle platoon is proven through the stability theorem. Finally, numerical simulation results are provided to verify its effectiveness and advantages over the traditional sliding-mode control method in vehicle platoons.

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Adaptive fault-tolerant automatic train operation using RBF neural networks

Cited by 38 publications

References 23 publications

Research on Multi-Objective Optimization and Control Algorithms for Automatic Train Operation

Research on Multi-Objective Optimization and Control Algorithms for Automatic Train Operation

Model-Independent Adaptive Fault-Tolerant Tracking Control for High-Speed Trains with Actuator Saturation

Neural Adaptive Sliding-Mode Control of a Vehicle Platoon Using Output Feedback

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