基于粒子群算法的挖掘机PD控制器设计  

Design of Excavator PD Controller Based on Particle Swarm Optimization Algorithm

作　　者：周有明 刘凯磊 强红宾 康绍鹏 ZHOU You-ming;LIU Kai-lei;QIANG Hong-bin;KANG Shao-peng(School of Mechanical Engineering,Jiangsu University of Technology,Changzhou 213001,Jiangsu,China;Sinomach Changlin Co.,Ltd,Changzhou 213136,Jiangsu,China;Research Center of Fluid Machinery Engineering and Technology,Jiangsu University,Zhenjiang 212013,Jiangsu,China)

机构地区：[1]江苏理工学院机械工程学院,江苏常州213001 [2]国机重工集团常林有限公司,江苏常州213136 [3]江苏大学流体机械工程技术研究中心,江苏镇江212013

出　　处：《机械研究与应用》2023年第4期63-66,共4页Mechanical Research & Application

基　　金：国家自然科学基金资助项目:基于机液压差补偿的负载口独立控制系统主被动柔顺控制(编号:51805228);江苏省高等学校自然科学基金项目:液压并联车载主动减振平台复合柔顺控制策略研究(编号:22KJB460021);常州市科技支撑计划项目:基于机电液耦合系统的大型高速运载装备行驶非线性动力学行为研究及应用(社会发展)(编号:CE20209002);常州市领军型创新人才引进培育项目资助:复杂工况下多轴线运载装备智能连杆转向系统研究及应用(编号:CQ20210093);江苏省研究生科研与实践创新计划项目:液压挖掘机自动挖掘轨迹规划与多执行器协同运动控制(编号:SJCX21_1323)。

摘　　要：针对挖掘机精确轨迹控制问题,构建了挖掘机动力学模型,搭建了Simulink仿真环境下挖掘机动力学模块、PD控制系统模块和可自动调用的PD参数输入模块;通过PSO最优搜寻得到PD权重参数,然后在PSO程序中定义挖掘机动力学模型,最后在动态控制下进行一个闭环快速自适应整定联合仿真。仿真结果表明,采用基于粒子群算法的挖掘机PD控制器与传统的试凑法都能达到期望的轨迹控制,且都能较好地贴近理论值,但基于粒子群算法的挖掘机PD控制器能够快速自适应整定。在挖掘轨迹起始过程中,相较于试凑法中K_(P)和K_(V)组合为200-5,粒子群算法的PD控制器要收敛近75%,降低了系统的稳态误差,大大提高了液压挖掘机的稳定性和精确性。Aiming at the problem of precise trajectory control of excavator,the dynamics model for excavator is built,and the automatic call of excavator dynamics module,PD control system module and PD parameter input module is built under the Simulink simulation environment.The PD weight parameters are obtained through the PSO optimal search.The excavator dynamics model is defined in the PSO program,and a closed-loop fast adaptive tuning joint simulation is carried out under the dynamic control.The simulation results show that the excavator PD controller based on the particle swarm optimization algorithm can achieve the desired trajectory control compared with the traditional trial and error method,and both of them are close to the theoretical value;but the excavator PD controller based on the particle swarm optimization algorithm can quickly adapt to the adjustment,and it converges by 75% compared with the 200-5 combination of K_(P) and K_(V) in the trial and error method in the initial process of the excavation trajectory,which reduces the steady-state error of the system.It greatly improves the stability and accuracy of hydraulic excavator.

关 键 词：挖掘机 动力学 PD控制器 粒子群算法 

分 类 号：TP273.2[自动化与计算机技术—检测技术与自动化装置]