德尔塔机器人的智能容错控制:用于增强轨迹跟踪的混合优化方法
Carlos Domínguez1, Claudio Urrea1
1Electrical Engineering Department, Faculty of Engineering, University of Santiago of Chile (USACH), Las Sophoras 165, Estación Central, Santiago 9170124, Chile.
Sensors (Basel, Switzerland)
|April 28, 2025
概括
本研究介绍了Delta型机器人的主动故障耐受控制 (AFTC),以提高故障下的性能. 该系统实现了完美的故障诊断,并减少了性能退化,提高了机器人系统的可靠性.
科学领域:
- 机器人和控制系统 机器人和控制系统
- 故障诊断和耐受性控制
- 并行操纵器 并行操纵器
背景情况:
- 德尔塔型机器人表现出动力学复杂性和多驱动器依赖性,使它们容易因故障而降低性能.
- 现有的耐故障控制方法可能无法充分解决Delta型并行机器人的复杂故障场景.
研究的目的:
- 为Delta型平行机器人开发一种新的主动故障耐受控制 (AFTC) 策略.
- 将先进的故障诊断系统与强大的控制策略相结合,以减轻性能退化.
- 在故障条件下提高复杂机器人系统的轨迹跟踪精度.
主要方法:
- 使用混合特征提取算法的故障诊断系统,该算法结合了波形散射网络 (WSN),主要组件分析 (PCA),线性差异分析 (LDA) 和元学习 (ML).
- 一个混合优化框架,集成遗传算法和梯度下降来重新配置Type-2模糊控制器以实现容错控制.
- 实时识别和分类单元和多元组件故障 (执行器,传感器).
主要成果:
- 故障诊断系统在四个分类器实现了完美的准确性.
- 建议的AFTC方法有效地将关键性能退化降低到中等水平,即使在多个故障的情况下.
- 重新配置的Type-2模糊控制器在保持机器人的性能方面表现出了强性.
结论:
- 开发的AFTC战略对于Delta型平行机器人来说是强大而高效的.
- 集成故障诊断和控制系统显著提高了故障条件下的可靠性和性能.
- 这种方法有可能提高复杂的机器人系统的轨迹跟踪精度,这些系统面临不利的操作条件.
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