基于通用自适应镇定器的非线性混沌系统摩擦力辨识
Identifying Friction in a Nonlinear Chaotic System Using a Universal Adaptive Stabilizer
摘要：
本文提出了一种摩擦模型参数识别程序，可以与高度非线性和混沌系统。本研究所选用的系统是一个被动驱动的倾斜古田摆，这是已知的具有高度非线性和耦合模型。摆是倾斜的，以确保存在一个稳定的平衡配置，其所有的自由度，和链接的重量是唯一的外力施加到系统。建立了摆的非线性分析模型，并从文献中选取了考虑静摩擦、动摩擦、粘滞摩擦和Stribeck效应的连续摩擦模型。设计了一种高增益的通用自适应稳定器（UAS）观测器，利用关节角测量值辨识摩擦模型参数。该方法进行了测试，在模拟和实验装置上验证。尽管系统的高度非线性，该方法被证明收敛到精确的参数值，在模拟中，并产生定性的参数幅度在实验中的拟合优度平均约为85%。模拟与实验结果之间的差异归因于摩擦模型的局限性。所提出的方法的主要优点是显着减少计算需求和所需的时间相对于传统的基于优化的识别例程。所提出的方法产生了超过99%的估计时间减少，而在每一个测试中进行的优化方法是相当准确的。另一个优点是，该方法可以很容易地适应其他模型的实验数据。
英文摘要：
This paper proposes a friction model parameter identification routine that can work with highly nonlinear and chaotic systems. The chosen system for this study is a passively-actuated tilted Furuta pendulum, which is known to have a highly non linear and coupled model. The pendulum is tilted to ensure the existence of a stable equilibrium configuration for all its degrees of freedom, and the link weights are the only external forces applied to the system. A nonlinear analytical model of the pendulum is derived, and a continuous friction model considering static friction, dynamic friction, viscous friction, and the stribeck effect is selected from the literature. A high-gain Universal Adaptive Stabilizer (UAS) observer is designed to identify friction model parameters using joint angle measurements. The methodology is tested in simulation and validated on an experimental setup. Despite the high nonlinearity of the system, the methodology is proven to converge to the exact parameter values, in simulation, and to yield qualitative parameter magnitudes in experiments where the goodness of fit was around 85\% on average. The discrepancy between the simulation and the experimental results is attributed to the limitations of the friction model. The main advantage of the proposed method is the significant reduction in computational needs and the time required relative to conventional optimization-based identification routines. The proposed approach yielded more than 99\% reduction in the estimation time while being considerably more accurate than the optimization approach in every test performed. One more advantage is that the approach can be easily adapted to fit other models to experimental data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
