对操纵器进行基于双时间尺度非概率学可靠性的控制器优化,考虑到运动误差和磨损增长
Lei Wang1, Zheng Zhou2, Jiaxiang Liu2
1National Key Laboratory of Strength and Structural Integrity, Institute of Solid Mechanics, School of Aeronautic Science and Engineering, Beihang University, Beijing 100191, China; Aircraft and Propulsion Laboratory, Ningbo Institute of Technology, Beihang University, Ningbo 315100, China.
ISA transactions
|August 4, 2023
概括
本研究引入了一种新的优化方法,通过解决运动错误和磨损增长来提高操纵器系统性能. 该方法确保了对时间依赖和时间独立因素的可靠性,改善了控制和寿命.
科学领域:
- 机器人和控制系统 机器人和控制系统
- 机械工程 机械工程
- 可靠性工程可靠性工程
背景情况:
- 操纵器系统的性能受到运动误差和磨损增长的严重影响,影响控制精度和操作寿命.
- 现有的方法往往无法全面解决这些可靠性因素的多尺度时间性质.
研究的目的:
- 为操纵器控制器提出基于双时间尺度非概率可靠性 (DTSNPR) 的优化方法.
- 将运动误差的时间依赖可靠性 (TDR) 和磨损增长的时间独立可靠性 (TIR) 整合到一个统一的框架中.
- 提高非线性操纵器系统不确定性传播分析的精度和效率.
主要方法:
- 开发DTSNPR方法,同时评估和优化控制器,同时考虑运动误差和磨损增长.
- 采用自适应子间隔拼接方法 (ASICM) 来处理高度非线性不确定性传播问题.
- 在三个数值操纵系统上应用和验证拟议的方法.
主要成果:
- DTSNPR方法成功地确保了操纵器系统在预定义的可靠性级别下运行,无论是运动错误还是磨损增长.
- 与传统方法相比,ASICM在计算成本 (1%的蒙特卡洛) 和精度 (0.4%的误差) 中表现出显著的优势.
- 综合方法为优化不同时间尺度上的操纵器可靠性提供了一个强大的框架.
结论:
- 提出的基于DTSNPR的优化方法为评估和提高操纵器系统可靠性提供了全面的解决方案.
- ASICM提供了一种计算效率高,准确的方法,用于分析机器人系统中的非线性不确定性传播.
- 这项研究有助于在各种工业应用中开发更可靠,更持久的操纵系统.
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