CLC number: TP242

Control Methods Study of Rail-Mounted W-Beam Guardrail Inspection Robot

挂轨式波形梁护栏检测机器人的控制方法研究

School of Electronic and Control Engineering, Chang'an University, Xi'an 710064, Shaanxi, China

^† Corresponding author. E-mail: hfwang@chd.edu.cn

Received: 8 January 2025

Abstract

To address the limitations of traditional manual highway guardrail inspections, this paper proposes an obstacle-crossing and collaborative tracking control method for a rail-mounted robot. Static and dynamic analyses verify the robot's structural reliability and driving feasibility. Based on the leader-follower model, a triangular collaborative tracking model is developed, and a linear time-varying model predictive controll (LTV-MPC) is designed to achieve smooth and precise collaborative control. For obstacle crossing, an acceleration reference model and a gradient-based adaptive law are proposed, leading to a model reference adaptive controll (MRAC) that effectively suppresses vibrations and ensures synchronous control. Simulation results show that the MPC achieves a 0.415% overshoot and a 0.344 m steady-state accuracy, while also reducing the intensity of speed fluctuations by 35%. The MRAC ensures smooth obstacle-crossing speeds and adaptive strategy switching, validating the reliability and practicality of the rail-mounted robot under complex working conditions.

摘要

为解决传统人工巡检方式在高速公路护栏检测中的局限性，本文设计了一种挂轨式检测机器人越障与协同跟踪控制方法。通过静力学与动力学分析验证了机器人结构可行性及行驶可靠性。基于领航者-跟随者模型建立三角协同跟踪模型，并设计线性时变模型预测控制器(LTV-MPC)，实现平稳精准的协同控制；针对实际越障需求，提出加速参考模型并以梯度法推导得到自适应律，构建模型参考自适应控制器(MRAC)，有效抑制振动并保障协同控制。仿真结果表明，MPC控制器可实现0.415%的超调量与0.344m的稳态精度，同时速度波动强度降低35%；MRAC保证了平稳的越障速度和自适应策略切换，验证了所设计挂轨式检测机器人在复杂工况下的可靠性与实用性。