A fast system estimation algorithm for a discontinuous dynamical model with coefficients coupling

IF 8.9 1区 工程技术 Q1 ENGINEERING, MECHANICAL
Binghang Xiao, Jianzhe Huang, Zhongliang Jing
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引用次数: 0

Abstract

For a discontinuous dynamical system with multi-degree-of-freedom, the coefficients of the established model may be coupled and the response can be also non-smooth, which bring difficulties to estimating the system under fast convergence requirement. In this paper, a nonlinear observer based on super-twisting sliding mode method is designed to evaluate the difference between the prediction of the model and the actual system dynamics. The pseudoinverse and Newton-Raphson iteration are firstly integrated to achieve the fast and reliable estimation of the coefficients of the model including the coupled ones. The event-triggered mechanism is specifically designed to detect the singularity of the dynamics for such a discontinuous system, such that the measured response which is used for the proposed algorithm can be divided into different segments. The experimental validation is conducted, which shows that the proposed method can gain more accurate estimations than pseudoinvese-based comparison method. The switching time between free motion and collision, as well as the collision force can also be predicted accurately based on the model with the estimated coefficients.
具有系数耦合的不连续动态模型的快速系统估计算法
对于一个多自由度的不连续动力系统,所建立的模型的系数可能是耦合的,响应也可能是非光滑的,这给快速收敛要求下的系统估计带来了困难。本文设计了一种基于超扭转滑模方法的非线性观测器来评估模型预测与实际系统动力学之间的差异。首先将伪逆和牛顿-拉夫森迭代相结合,实现了包括耦合系数在内的模型系数的快速可靠估计。事件触发机制专门用于检测这种不连续系统的动力学奇异性,从而将用于所提算法的测量响应划分为不同的段。实验验证表明,该方法比基于伪投资的比较方法能获得更精确的估计。基于估计系数的模型还可以准确地预测自由运动与碰撞之间的切换时间以及碰撞力。
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来源期刊
Mechanical Systems and Signal Processing
Mechanical Systems and Signal Processing 工程技术-工程:机械
CiteScore
14.80
自引率
13.10%
发文量
1183
审稿时长
5.4 months
期刊介绍: Journal Name: Mechanical Systems and Signal Processing (MSSP) Interdisciplinary Focus: Mechanical, Aerospace, and Civil Engineering Purpose:Reporting scientific advancements of the highest quality Arising from new techniques in sensing, instrumentation, signal processing, modelling, and control of dynamic systems
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