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

该项目解决了在机器的所有操作条件下，基于摩擦诊断和运动控制的精确摩擦过程模型的需求。由于磨损、温度、湿度、润滑剂条件和负载而引起的摩擦特性的缓慢或突然变化可能导致控制摩擦过程模型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。本研究开发了具有非平稳滑动或滚动摩擦的机械系统的估计方法，以检测和补偿摩擦过程模型的变化。为了实现这一目标，开发了一种非基于模型和基于模型的估计的协同组合，将摩擦力观测与摩擦过程模型识别分离开来。对于摩擦力的观测，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。摩擦观察者将使用物理系统的模型（除了摩擦模型）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侧重于最大限度地提高高速，重复，运动控制的精度，用于各种应用，如半导体制造，定位硬盘读/写头和机床位置/速度控制。在基于模型的轴承状态监测的背景下，将通过仿真来证明使用估计方法来增强基于摩擦的诊断。

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.