摘要
针对AGV(Automated Guided Vehicle)行走不平滑的问题,在研究路径对AGV行走性能影响的基础上,建立AGV几何模型,并对其进行运动学分析,结合Bezier曲线的特点和优势,运用改进的粒子群优化(improved Particle Swarm Optimization,improved PSO)算法,规划其最优理想运动路径;将模糊逻辑与PID算法结合,构建改进模糊PID控制算法,并对AGV运动偏差进行校正。利用MATLAB软件绘制其实际运动轨迹,比较角速度、偏角及路径偏差,评估误差与精度。结果表明,所规划的路径可以满足AGV运行轨迹的要求,提出的改进模糊PID控制算法可有效提升AGV偏差校正的能力,并提高响应的速度,降低系统的稳态误差,较好地满足现场工作环境对AGV实时性和稳定性的要求。
Aiming at the problem that AGV(Automated Guided Vehicle)does not walk smoothly,based on the study of the influence of the path on the walking performance of AGV,the AGV geometric model was established,and its kinematic analysis was carried out.Combined with the characteristics and advantages of Bezier curve,the improved Particle Swarm Optimization(improved PSO)algorithm was used to plan the optimal ideal motion path;the fuzzy logic and PID algorithm were combined to build an improved fuzzy PID control algorithm,and the AGV motion deviation was corrected.Using MATLAB software to draw the actual trajectory,the angular velocity,deflection angle and path deviation were compared,and the error and accuracy were evaluated.The results show that the planned path can meet the requirements of AGV running track,the improved fuzzy PID control algorithm can effectively improve the ability of AGV deviation correction,improve the response speed,reduce the steady-state error of the system,and better meet the requirements of real-time and stability of AGV in the field working environment.
作者
袁鹏
周军
杨子兵
吴迪
皇攀凌
YUAN Peng;ZHOU Jun;YANG Zibing;WU Di;HUANG Panling(School of Mechanical Engineering,Shandong University,Jinan 250061,China;Key Laboratory of High Efficiency and Clean Mechanical Manufacture,Ministry of Education,Shandong University,Jinan 250061,China;National Experimental Teaching Demonstration Center of Mechanical Engineering,Shandong University,Jinan 250061,China;Shandong Alesmart Intelligent Technology Co.,Ltd.,Jinan 250063,China)
出处
《现代制造工程》
CSCD
北大核心
2021年第4期26-32,共7页
Modern Manufacturing Engineering
基金
山东省重点研发计划项目(2017CXGC0903)
山东省重点研发计划项目(2019JZZY010453)
山东大学泉城产业领军人才项目(2018031)。
