Composite model reference adaptive sliding mode controller for automatic train operation

Muniandi, Ganesan; Ezhilarasi, D.

doi:10.1049/iet-cta.2019.0038

Cited by 12 publications

(6 citation statements)

References 25 publications

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“…Herein, observer/controller parameters are adjusted online, while the system stability is proven through a formal proof based on Lyapunov method. Among the different control techniques, Siding Mode Control (SMC) is widely recognized as one of the most efficient control scheme when dealing with control of nonlinear uncertain system along with disturbances/uncertainties rejection [15]. Along this line, a robust adaptive nonsingular terminal sliding mode (NTSM) control strategy, with an online estimation of the unknown parameters of the sliding manifold, is proposed in [16].…”

Section: A Related Workmentioning

confidence: 99%

A GoA4 Control Architecture for the Autonomous Driving of High-Speed Trains Over ETCS: Design and Experimental Validation

Barruffo,

Caiazzo,

Petrillo

et al. 2024

IEEE Trans. Intell. Transport. Syst.

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Section: A Related Workmentioning

confidence: 99%

A GoA4 Control Architecture for the Autonomous Driving of High-Speed Trains Over ETCS: Design and Experimental Validation

Barruffo,

Caiazzo,

Petrillo

et al. 2024

IEEE Trans. Intell. Transport. Syst.

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“…Usually, the solution used as recommended speed profile is selected according to the required running time. For the discontinue distribution of Pareto front solutions in each objective axis, the solution with nearest running time is selected as the recommended speed profile which is expressed as (24). RSP is the solution of recommended speed profile, P is the obtained Pareto front solutions set, pi is the Pareto solution indexes, Tpi is the running time of pith solution, Treq is the required running time.…”

Section: Recommended Speed Profilementioning

confidence: 99%

“…To deal with the unpredictable factors, the adaptive controller is proposed to tune control parameters. There are many adaptive controllers that involve complicated models of train running control [22]- [24]. It should be noted that the exact model of the nonlinear system, which is hard to be constructed, is the precondition of effective adaptive control.…”

Section: Introductionmentioning

confidence: 99%

Integrated Optimal Design of Speed Profile and Fuzzy PID Controller for Train With Multifactor Consideration

Zhu

Liu³

et al. 2020

IEEE Access

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Speed profile design and tracking control are two important issues to ensure the performance of the automatic train operation system. Aiming to realize the maximum optimization probability of train running, this paper constructs an integrated optimal method through speed profile design and fuzzy PID controller design. In the recommended speed profile design, three typical running strategies are included in the proposed comprehensive scheme to extend solution space, and the constructed train performance simulation is combined with NSGA-II algorithm to solve such multiobjective problem. As for recommended speed profile tracking, in order to improve the tracking performance of the train, which is a nonlinear system, a fuzzy PID controller is designed to adaptively adjust PID gains. Note that multiple indicators of punctually, energy consumption, stopping accuracy and comfort are all considered in the integrated design. Taking Beijing Subway Line 8 as the numerical example, the results show that the design of speed profile and fuzzy PID controller are both effective, and energy consumption saves about 10.40% with other better indicators compared with the original data.

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“…To improve the robustness during trajectory tracking, some scholars apply sliding mode control to the design of controller. Even under complex disturbances, the train speed and position error can be reduced to zero within a limited time, but the performance of energy conservation and is not optimized [12] . Based on the LQR optimal control method, paper [13] realizes the speed tracking error of less than 0.6km/s and the parking error of less than 30cm.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective and multi-stage trajectory tracking controller for high-speed train based on weight optimization

Feng,

Sun,

et al. 2024

Ninth International Symposium on Sensors, Mechatronics, and Automation System (ISSMAS 2023)

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The trajectory tracking is the core function to realize automatic operation of high-speed train. This paper designed a model predictive controller under consideration of multiple optimal objectives to achieve comprehensive control performance of accurate tracking, passenger comfort and energy conservation. By linearizing the cost function and constraints, the control optimization model is transformed into a mixed-integer quadratic programming model, which is solved by the Gurobi solver. There are several stages in the whole process of train operation with different priorities of multiple objectives. To adapt the different priorities of multiple objectives, this paper proposed a multi-objective and multi-stage (MOMS) MPC controller based on multi-objective particle swarm optimization algorithm and a selection strategy of optimal weight coefficient. The simulation results show that the controller designed in this paper can optimize the weight in different scenarios to improve comprehensive control performance.

show abstract

Composite model reference adaptive sliding mode controller for automatic train operation

Cited by 12 publications

References 25 publications

A GoA4 Control Architecture for the Autonomous Driving of High-Speed Trains Over ETCS: Design and Experimental Validation

A GoA4 Control Architecture for the Autonomous Driving of High-Speed Trains Over ETCS: Design and Experimental Validation

Integrated Optimal Design of Speed Profile and Fuzzy PID Controller for Train With Multifactor Consideration

Multi-objective and multi-stage trajectory tracking controller for high-speed train based on weight optimization

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