基于混沌PSO的SCARA机器人参数辨识  

Parameter Identification of SCARA Robot Based on PSO Algorithm of Chaos

作　　者：丰非 扈宏杰[1] 

机构地区：[1]北京航空航天大学自动化科学与电气工程学院,北京100191

出　　处：《自动化与仪表》2017年第12期14-18,共5页Automation & Instrumentation

摘　　要：该文基于Denavit-Hartenberg参数方法和Lagrange方程分别建立了平面关节型SCARA机器人的运动学模型和动力学模型。将机器人的动力学模型用观测矩阵和待辨识参数矩阵表述。在优化了激励轨迹的前提下,采用一种基于混沌粒子群(CPSO)的参数辨识算法,辨识动力学模型中的待辨识参数,利用混沌特性来提高种群的多样性和粒子搜索的遍历性,从而提高了参数的辨识精度和收敛速度。通过Matlab仿真实验,表明与传统最小二乘和基本PSO方法相比,该方法具有明显的有效性。The kinematic model and dynamic model of selective compliance assembly robot arm（SCARA） robot have been established based on Denavit-Hartenberg method and Lagrange equation. Then the model is simplified to reduce the computation,the kinetic equation is transformed into a linear form to get the observation matrix and the parameters to be identified. An incentive trajectory is designed to finish the parameter identification. A chaos particle swarm （CPSO） algorithm is introduced to overcome the problem of premature convergence,CPSO uses the properties of ergodicity,stochastic property,and regularity of chaos to lead particles＇ exploration,the accuracy of parameter identification and convergence rate have been improved. Through the Matlab simulation test,this algorithm is more reliable and efficient than the least square method and basic PSO method.

关 键 词：平面关节型机器人 动力学建模 参数辨识 混沌PSO算法 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置] TP273[自动化与计算机技术—控制科学与工程]