636 交流伺服系统的转动惯量辨识及调节器参数自整定
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No.4Apr.2021第4期2021年4月组合机床与自动化加工技术Modular Machine Tool & Automatic Manufacturing Techninue文章编号:1001 -2265(2021)04 -0096 -04DOI : 10.13462/j. cnki. mmtamt. 2021.04. 023伺服系统转动惯量辨识及控制器PI 参数优化孙彦瑞,苏成志(长春理工大学机电工程学院,长春130000)摘要:在机器人运行时,为了使伺服电机在最优性能下达到目标速度、在工作过程中有着更强的抗 扰动能力,并避免出现震荡、谐振的状况,从而造成机器人运行时动态稳定性严重降低。
提出一种 基于非线性动态学习因子的粒子群优化算法,对普通粒子群优化算法进行改进。
该算法以伺服系 统控制模型中的速度控制器为核心,实时辨识负载转动惯量值,使伺服系统内部控制参数根据实际 工况调节;运用该辨识值,通过计算得到速度控制PI 参数值,并实时修正速度控制器PI 参数值。
MATLAB/SIMULINK 仿真结果表明,与传统的粒子群优化算法相比,无论在电机启动过程中、还是 负载扰动下,该方法都具有更快的响应速度、更高的控制精度以及更强的抗干扰能力。
关键词:转动惯量;非线性动态学习因子;粒子群优化算法;速度控制器PI 参数中图分类号:TH166 ;TG506 文献标识码:AServo System Inertia IdenhPcahon and Controller PI Parameter OptimizationSUN Yan-rui , SU Cheng-zhi(School of Mechanical and Electrical Engineering , Changchun Univvrsity of Science and Technolo/y , Changchun 130000, Ch/ia )Abstrach : During the operation of the robot , in order to make the servo motor achieve the target speed un der the optimal performance , and have stronger anti-disirbance ability in the working proces s , and to a void the prob —m of vibration and resonance , resulting in a serous reduction in the dynamic stability of the robot. The coniol model of servo motor is analyzed , and a particle swarm optimization algorithm based on nonlmear dynamic learning factor is proposed. The algorithm ties the speed conioller in the servo system coniol model as the core , and can identify the loadz moment of inertia in real time , so that the internaicontrol parameters of the s ervo system can be adjusted according to the acial condbions. By using the i dentification value , the PI parameter value of the speed control is obtained through calculation , and the PI parameter value of the speed conioller is corrected in real time. The results of MATLAB/SIMULINK sim ulation show that compared with the traditional pakWle swarm optimization algorithm , this method has fas ter response speed , higher control accuracy and stronger anti-interference ability , whether in the motorsha+hing p+oce s o+unde+hheload dishu+bance.Key wois : moment of inertia ; nonlinear dynamic learning factor ; particle swarm optimization tgoriim ; speed conho l e+PIpa+amehe+0引言机器人在运行时,每个轴的负载转动惯量与负载 扭矩随着机器人的姿态的变化而变化;伺服系统对负 载转动惯量的辨识精度、辨识快慢,决定着伺服系统运 行的稳定性、精确性与快速性。