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State estimation based on kinematic model and relay feedback stability arising in controlled mechanical systems.

机译:基于运动学模型的状态估计和受控机械系统中产生的继电器反馈稳定性。

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摘要

This thesis considers two particular issues arising in the controlled mechanical systems: state estimation based on kinematic models using more sensors and the unified approach to understand the nonlinear dynamics coming from the Coulomb friction.;The recent advances in sensor technologies raise basic questions on how to synthesize the information from different sensors synergistically and how to implement new sensors in a reliable and cost-effective manner. The first part of this thesis deals with these issues from the state estimation point of view by exploiting the sensor-based estimation method called the kinematic Kalman filter (KKF). The KKF refers to the Kalman filter applied to a kinematic model. Being independent of physical parameters, the KKF is immune to modeling uncertainties and parameter variations. In its simplest form, the KKF combines an encoder with an accelerometer to provide a robust and accurate velocity estimation. Detailed error analysis of the KKF shows its superiority to the model-based scheme in its tolerance to the resolution of the encoder. We then extend the basic idea of the KKF to a general rigid body motion leading to the formulation of the multi-dimensional KKF (MD-KKF). The MD-KKF combines the measurements from the vision sensor, accelerometers and gyroscopes to remedy the inherent limitations of the vision sensor, i.e., the slow sampling rate and the latency. The main ideas are verified on two experimental testbeds: the instrumented indirect drive (IID) setup and the NSK two-link planar robot.;In control of mechanical systems with drive trains, the Coulomb friction is an important nonlinearity not only as the source of tracking error but also as the cause of instability generating limit cycles. The second part of this thesis focuses on the latter. First, we show that a series of drive trains with multiple Coulomb friction sources can be formulated as a class of relay feedback systems characterized by the zero DC gain property and the positivity of the first Markov parameter. Then, employing the piecewise quadratic Lyapunov (PWQL) function and the integral quadratic constraint (IQC), new sufficient conditions are developed to guarantee the pointwise global stability. The theoretical results are verified by simulation and experimental results with a single link flexible joint mechanism. The results are useful to design mechanical systems free from the limit cycle.
机译:本文考虑了受控机械系统中出现的两个特殊问题:使用更多传感器的基于运动学模型的状态估计以及理解库仑摩擦产生的非线性动力学的统一方法。协同合成来自不同传感器的信息,以及如何以可靠且经济高效的方式实现新传感器。本文的第一部分通过利用基于运动的卡尔曼滤波器(KKF)的基于传感器的估计方法,从状态估计的角度解决了这些问题。 KKF是指应用于运动学模型的卡尔曼滤波器。由于不受物理参数的影响,KKF不受建模不确定性和参数变化的影响。 KKF以最简单的形式将编码器和加速度计结合在一起,以提供可靠而准确的速度估算。 KKF的详细误差分析显示,它在基于编码器的分辨率的容忍度方面优于基于模型的方案。然后,我们将KKF的基本思想扩展到一般的刚体运动,从而形成多维KKF(MD-KKF)。 MD-KKF结合了视觉传感器,加速计和陀螺仪的测量结果,以弥补视觉传感器的固有局限性,即缓慢的采样率和延迟。主要思想已在两个实验测试台上得到验证:仪表式间接驱动(IID)装置和NSK两连杆平面机器人。;在带传动系统的机械系统的控制中,库仑摩擦不仅是引起非线性的重要原因,而且还具有重要的非线性。跟踪误差,而且还因为不稳定而产生极限循环。本文的第二部分侧重于后者。首先,我们表明,具有多个库仑摩擦源的一系列传动系统可以公式化为一类继电器反馈系统,其特征是零直流增益特性和第一马尔可夫参数的正性。然后,利用分段二次Lyapunov(PWQL)函数和积分二次约束(IQC),开发了新的充分条件来保证逐点全局稳定性。理论结果通过单连杆柔性关节机构的仿真和实验结果得到验证。结果对于设计不受极限循环影响的机械系统很有用。

著录项

  • 作者

    Jeon, Soo.;

  • 作者单位

    University of California, Berkeley.;

  • 授予单位 University of California, Berkeley.;
  • 学科 Engineering Mechanical.;Engineering Robotics.
  • 学位 Ph.D.
  • 年度 2007
  • 页码 143 p.
  • 总页数 143
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 机械、仪表工业;
  • 关键词

  • 入库时间 2022-08-17 11:39:15

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