Adaptive Sliding Mode Tracking Algorithm for Heavy-Haul Trains Under Actuator Saturation Constraints
1. Preeti Patel, Saveetha Engineering College (Autonomous), Tamil Nadu, Student, India
2. Saravanan M.P., Saveetha Engineering College (Autonomous), Tamil Nadu, Professor, India
Heavy-haul trains require exact control mechanisms to ensure safety, stability, and efficiency. However, when actuators reach their saturation limits, the control system's performance can degrade, leading to instability and tracking errors. In this paper, we propose an adaptive sliding mode tracking algorithm designed to handle the effects of actuator saturation. The algorithm dynamically adjusts control inputs to maintain stability, minimize tracking errors, and improve robustness against external disturbances. We demonstrate the algorithm's effectiveness in various actuator saturation scenarios through simulations.
Heavy-haul trains Actuator saturation Sliding Mode Control (SMC) Adaptive control Robust tracking Saturation constraints Nonlinear control Dynamic control input Train control systems Trajectory tracking
This paper presented an adaptive sliding mode tracking algorithm to address actuator saturation in heavy-haul trains. The proposed method dynamically adjusts the control input based on the saturation level, ensuring robust tracking performance and system stability under varying operating conditions. The simulation results verify that the algorithm outperforms conventional SMC and adaptive control strategies in terms of both accuracy and robustness. Future work will focus on experimental validation and real-time implementation in heavy-haul train systems.
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The author handled all aspects of the study, including its design, data collection, analysis, and manuscript preparation.
This work did not receive any specific grant from funding agencies in the public, commercial, or non-profit sectors for its research, authorship, or publication.
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I acknowledge the support and expertise of those who helped with this research and manuscript, and thank the peer reviewers for their valuable insights.
Data sharing is not relevant to this study.