Application of the Model Predictive Control with Constraint Tightening for ATO System

Wang, Longsheng; Xu, Haibo; Zou, Changfu; Yang, Guang

doi:10.14257/ijca.2015.8.11.24

Cited by 1 publication

(1 citation statement)

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“…Regarding predictive control optimization algorithms, an MPC algorithm considering multiple performance indicators and constraints is introduced to reduce energy waste and alleviate train periodic vibrations in [33]. In [34], a constraint-tightening-based MPC algorithm is presented, achieving speed tracking within the automatic train protection (ATP) limits. A switching cost function MPC algorithm is investigated [35], which switches cost functions in the train control problem based on operation demands.…”

Section: Introductionmentioning

confidence: 99%

Stability-Enhanced Model Predictive Control for Urban Rail Transit Train

Wang,

Xing,

Wang

2024

IEEE Access

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Automatic train operation (ATO) control is a pivotal part of urban rail transit development, where designing the dynamics models and controllers for the ATO control scenarios presents a formidable challenge. To begin with, considering the fundamental resistances encountered by trains during the operation process, including elemental running resistance and time-varying slope resistance, we treat relative distance and relative speed between train carriages as state variables in the control modeling. Considering changes in traction/braking outputs as control variables, we formulate a meticulous dynamics model for urban rail transit trains (URTT). Furthermore, a stability-enhanced model predictive control (SEMPC) approach is proposed for ATO control in URTT, with a terminal term being added to the control objective design for stability requirements. This approach anticipates the future dynamic behaviors of the control system, yielding a stable and convergent predictive controller for the ATO system. Lastly, utilizing operational data from a specific urban rail line as an illustrative example, we conduct comparative analyses of the operational control performance among various controllers in scenarios of the single section, multi-section, and disturbance. Experimental results demonstrate that the proposed SEMPC controller exhibits superior performance to the compared controllers for ATO control in URTT.INDEX TERMS Automatic train operation (ATO), urban rail transit train (URTT), stability-enhanced model predictive control (SEMPC), speed control.

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Section: Introductionmentioning

confidence: 99%