基于量化状态系统的柔性关节机器人动力学求解方法

doi:10.3901/JME.2020.03.121

摘要/Abstract

摘要： 量化状态系统（Quantized state system，QSS）方法是一种新的数值积分方法，它与传统基于时间离散的积分方法不同，QSS方法对状态变量进行离散，依次计算每次状态变量跃迁所需要的时间，从而推进积分。针对柔性关节机器人动力学刚性方程的求解问题，提出一种基于量化状态系统的步进校正优化算法（Step correction optimization algorithm，SCOA）。它结合QSS方法及隐式算法中梯形积分法的思想，以有效提高动力学求解的精度和效率。通过对四自由度柔性关节机器人动力学刚性方程的仿真求解，证明了算法的可行性。将SCOA算法与传统数值积分方法和QSS方法从仿真效率与仿真精度两方面进行性能对比，结果表明，SCOA算法在保证仿真效率的同时能有效提高仿真精度，算法性能优于传统方法和QSS方法。

关键词: 量化状态系统, 动力学, 机器人, 数值积分, 仿真

Abstract: Quantized state system (QSS) is a new numerical integration method, which is different from the traditional time discretization integration methods. The QSS method discretizes state variables and calculates the time required for each transition of variables. To solve the dynamics stiff equation of flexible joint robot, a step correction optimization algorithm (SCOA) based on QSS was proposed. This algorithm combines the ideas of the QSS method and the implicit trapezoidal integral method to effectively improve the dynamics solving accuracy and efficiency. The feasibility of this algorithm is proved by the simulation of four-DOF flexible joint robot. The performance of this algorithm is compared with that of the traditional integration methods and QSS method in terms of simulation efficiency and simulation accuracy. The results show that SCOA algorithm can effectively improve the simulation accuracy while ensuring the simulation efficiency, and its performance is better than the traditional methods and QSS method.

Key words: quantized state system, dynamics, robot, numerical integration, simulation

中图分类号:

TP391

李志华, 吴晨佳, 江德, 樊志华, 倪敬. 基于量化状态系统的柔性关节机器人动力学求解方法[J]. 机械工程学报, 2020, 56(3): 121-129.

LI Zhihua, WU Chenjia, JIANG De, FAN Zhihua, NI Jing. The Dynamics Solving Method of Flexible Joint Robot Based on Quantized State System[J]. Journal of Mechanical Engineering, 2020, 56(3): 121-129.

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