摘要
为了使平面并联机器人在闭环控制系统中具有更稳定的控制性能。对此,这里构建了平面五杆并联机器人动力学模型及其控制系统模型,给出了控制系统的增益矢量p。对机器人系统模型进行离线PID控制优化,通过控制增益来设计控制系统中的增益矢量p,从而实现非线性单目标动态优化(NLMODOP)。在NLMODOP中加入动态约束,采用带约束处理机制的差分进化(DE)算法求解平面并联机器人的非线性规划问题,进而处理不稳定的动态优化。对机器人模型中的五个连杆进行仿真实验,并对有无DE算法控制的仿真结果进行了比较。结果表明:相比于无DE算法,采用DE算法下的机器人系统模型的连杆跟踪位移基本无跟踪误差。说明基于差分进化算法的平面并联机器人离线PID控制优化具有较好的控制精度和跟踪性能。
In order to make the planar parallel robot have more stable control performance in the closed loop control system.The dynamic model of planar five-bar parallel robot and its control system model are established,and the gain vector p of the con-trol system is given.The off-line PID control optimization of the robot system model is carried out,and the gain vector P in the control system is designed through the control gain,so as to realize the nonlinear single objective dynamic optimization(NLMODOP).Dynamic constraints are added to NLMODOP,and differential evolution(DE)algorithm with constraint process-ing mechanism is used to solve nonlinear programming problems of planar parallel robots,and then unstable dynamic optimi-zation is dealt with.The simulation experiment of five connecting rods in the robot model is carried out,and the simulation results with or without DE algorithm control are compared.The results show that compared with the de-free algorithm,the connecting rod tracking displacement of the robot system model using DE algorithm has basically no tracking error.It shows that the off-line PID control optimization of planar parallel robot based on differential evolution algorithm has better control accuracy and track-ing performance.
作者
刘一扬
王春燕
LIU Yi-Yang;WANG Chun-yan(College of Mechanical and Electrical Engineering,Zhengzhou Institute of Finance and Economics,He’nan Zhengzhou 450000,China;Department of Mechanical and Electrical Engineering,Hebei Vocational College of Rail Transporta‐tion,Hebei Shijiazhuang 050000,China)
出处
《机械设计与制造》
北大核心
2024年第5期156-160,共5页
Machinery Design & Manufacture
基金
河南省科技攻关重点项目(232102220075)。
关键词
平面五杆并联机器人
离线PID控制优化
非线性单目标动态优化
差分进化算法
Planar Five-Bar Parallel Robot
Off-Line Pid Control Optimization
Nonlinear Single Objective Dy-namic Optimization
Differential Evolutionary Algorithm
