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ADOB:一个可靠机器人系统的现场友好的控制框架,通过强大和适应性控制的互补集成来实现可靠的机器人系统
Jangyeon Park1, Kwanho Yu2, Jungsu Choi1,2
1Humanics Co., Ltd., Gyeongsan 38541, Republic of Korea.
这项研究引入了自适应干扰观察器 (ADOB),以增强机器人在不确定性下的控制. ADOB通过将干扰拒绝和参数识别分开来提高可靠性,减少机器人系统中的模型不确定性和跟踪错误.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 控制系统工程 控制系统工程
- 机械电子学是什么意思 机械电子学
背景情况:
- 机器人系统面临着有限的计算能力,环境不确定性和动态变化的挑战.
- 基于模型的控制通常是不切实际的,因为模型的不确定性和识别成本.
- 现有的稳健和自适应的控制方法可能会遭受干扰排斥和参数估计之间的干扰.
研究的目的:
- 开发一个适应性干扰观察器 (ADOB),集成干扰观察和在线参数适应.
- 为了克服在联合干扰观察器 (DOB) 和参数适应算法 (PAA) 中发现的功能干扰问题.
- 提高机器人控制系统在不确定的条件下运行的可靠性和性能.
主要方法:
- 提出了一个适应性干扰观察器 (ADOB),将DOB与基于递归最小平方 (RLS) 的PAA集成在一起.
- 实现了双过结构,以分离干扰拒绝和参数识别过程.
- 利用超稳定性理论进行稳定性分析,结合参数变化的平滑机制.
主要成果:
- 在实验机器人系统中证明了模型不确定性和跟踪错误的降低.
- 与传统的DOB方法相比,ADOB显示了更好的性能.
- 成功分离了干扰补偿和参数估计,减轻了相互掩盖.
结论:
- 拟议的ADOB有效地将在线参数适应与干扰观测集成在一起,以实现强大的机器人控制.
- 双过方法确保了稳定性,并在不确定的动态环境中提高了性能.
- ADOB为提高实际机器人系统的可靠性提供了一个有希望的解决方案.
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