基于扩展卡尔曼过的非线性模型预测控制,用于具有多个约束和避开障碍的未完成系统.
IEEE transactions on cybernetics
|March 3, 2025
概括
本研究引入了对未完善系统的新控制方法,通过解决约束和避开障碍来提高性能和安全性. 该方法将扩展的卡尔曼过与非线性模型预测控制相结合,以实现稳健的操作.
科学领域:
- 机器人和控制系统 机器人和控制系统
- 应用数学 应用数学 应用数学
背景情况:
- 低质量的系统的控制输入比自由度要少,使控制复杂化.
- 现有的方法在优化短暂性能和确保安全约束方面往往不足.
- 传感器噪声和避开障碍物是实际应用中的重大挑战.
研究的目的:
- 开发一个先进的控制方法,以解决在稳定状态和过渡性能的局限性.
- 确保准确的定位,同时管理多个系统和安全限制,包括避开障碍.
- 为了减轻传感器噪声对控制性能的影响.
主要方法:
- 建议采用基于卡尔曼过的扩展非线性模型预测控制 (EKF-NMPC) 策略.
- 为了有效避免障碍,将人工潜力场纳入成本函数.
- 动态重量系数的分配和与EKF手柄传感器噪声的联合应用.
主要成果:
- 该EKF-NMPC方法成功地确保了准确的定位,多个约束,同时避免障碍.
- 通过人工潜力场和动态加权来实现有效的避免碰撞.
- 该方法在复杂的低功率系统 (如顶部和塔式起重机) 上表现出强大的性能,即使有传感器噪声.
结论:
- 开发的控制方法是第一个同时解决全状态约束,复合变量约束,控制输入约束和在未达标系统中避免障碍的控制方法.
- 拟议的方法在控制复杂的低值系统方面取得了重大进展,提高了安全性和性能.
- 在顶部和塔式起重机上的验证证实了该方法的实际适用性和有效性.
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