Minimizing Nonlinear Process Model Uncertainty for Motion Control and Friction-Based Diagnostics

最小化运动控制和基于摩擦的诊断的非线性过程模型不确定性

• 批准号: 9721468
• 负责人: Laura Ray
• 金额: $ 17.83万
• 依托单位: Dartmouth College
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1998
• 资助国家: 美国
• 起止时间: 1998-06-01 至 2002-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9721468&HistoricalAwards=false
• 关键词: Minimizing; Nonlinear; Process; Model; Uncertainty

This project addresses the need for accurate friction process models during all operating conditions of a machine for friction- based diagnostics and motion control. Slow or sudden changes in friction characteristics due to wear, temperature, humidity, lubricant conditions, and loads can lead to undetected changes in the parameters of a governing friction process model. For model- based diagnostics, accurate friction process models are required to detect faults that may lead to machine failure. For motion control, friction process model uncertainty must be minimized to maximize performance. This research develops estimation methods for mechanical systems with nonstationary sliding or rolling friction to detect and compensate for changes in friction process models. To accomplish this, a synergistic combination of non- model-based and model-based estimation is developed to separate friction force observation from friction process model identification. For friction force observation, nonlinear estimators that do not require a structured friction model or direct measurement of friction force will be developed. Friction observers will use models of the physical system (apart from the friction model) and measured motion to estimate friction force through state extended filtering. Friction force observation will supply inputs for model-based, multiple-model estimation to identify a physically relevant friction model using Bayesian selection. Experiments combining estimation and control will focus on maximizing accuracy of high-speed, repetitive, motion control for diverse applications such as semiconductor manufacturing, positioning hard disk read/write heads, and machine tool position/velocity control. Use of the estimation methods to enhance friction based diagnostics will be demonstrated by simulation in the context of model-based condition monitoring of bearings.

该项目解决了在基于摩擦的诊断和运动控制机器的所有操作条件下对精确摩擦过程模型的需求。由于磨损、温度、湿度、润滑剂条件和负载引起的摩擦特性的缓慢或突然变化可能导致支配摩擦过程模型的参数的未检测到的变化。对于基于模型的诊断，需要精确的摩擦过程模型来检测可能导致机器故障的故障。对于运动控制，摩擦过程模型的不确定性必须最小化，以最大限度地提高性能。本研究针对具有非稳态滑动或滚动摩擦的机械系统，发展侦测与补偿摩擦过程模型变化的估测方法。为了实现这一点，开发了非基于模型和基于模型的估计的协同组合，以将摩擦力观测与摩擦过程模型识别分离。对于摩擦力观测，将开发不需要结构化摩擦模型或直接测量摩擦力的非线性估计器。摩擦观测器将使用物理系统的模型（除了摩擦模型之外）和测量的运动，通过状态扩展滤波来估计摩擦力。摩擦力观测将为基于模型的多模型估计提供输入，以使用贝叶斯选择识别物理相关的摩擦模型。结合估计和控制的实验将专注于最大限度地提高高速，重复，运动控制的精度，如半导体制造，定位硬盘读/写磁头，机床位置/速度控制等各种应用。使用的估计方法，以提高基于摩擦的诊断将证明模拟的情况下，基于模型的轴承状态监测。