提出可同时应对部分与完全故障的Mecanum机器人容错控制方法
Fault Tolerant Control of Mecanum Wheeled Mobile Robots
- 用后验概率实时估计故障参数，动态调整控制策略
- 在多种故障场景下均能保持机器人稳定运动
- 适合对可靠性要求高的移动机器人应用
Mecanum轮移动机器人（MWMRs）极易受执行器故障影响，导致性能下降甚至任务失败。现有容错控制（FTC）方案主要针对电机卡死等完全故障，忽略扭矩衰减等部分故障。本文提出一种兼顾两类故障的FTC策略，通过后验概率实时学习故障参数，并将预设故障对应的控制律按概率加权聚合，生成最终控制指令，确保在不同故障程度下系统仍具鲁棒性与安全性。仿真结果表明该方法在多种故障场景中均有效。
原文摘要 · Abstract (English)
Mecanum wheeled mobile robots (MWMRs) are highly susceptible to actuator faults that degrade performance and risk mission failure. Current fault tolerant control (FTC) schemes for MWMRs target complete actuator failures like motor stall, ignoring partial faults e.g., in torque degradation. We propose an FTC strategy handling both fault types, where we adopt posterior probability to learn real-time fault parameters. We derive the FTC law by aggregating probability-weighed control laws corresponding to predefined faults. This ensures the robustness and safety of MWMR control despite varying levels of fault occurrence. Simulation results demonstrate the effectiveness of our FTC under diverse scenarios.
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