基于PSO的模糊PID牙轮钻机钻进控制系统研究  

Research on Fuzzy PID Drilling Control System for Cone Drills Based on Particle Swarm Optimization(PSO)

作　　者：吴坎 肖峻[1] 解天源 WU Kan;XIAO Jun;XIE Tianyuan(School of Mechanical and Electronic Engineering,Wuhan University of Technology,Wuhan 430070,China;School of International Education,Hubei University of Technology,Wuhan 430068,China)

机构地区：[1]武汉理工大学机电工程学院,湖北武汉430070 [2]湖北工业大学底特律绿色工业学院,湖北武汉430068

出　　处：《数字制造科学》2024年第4期278-283,共6页

摘　　要：为了适应各种负载工况和地质条件,设计了一种高效、低能耗的牙轮钻机钻进控制系统,降低了钻机振动,提高了其自动化水平。提出一种利用粒子群算法(PSO)对模糊PID参数进行优化的方法,通过结合PSO算法的迭代寻优能力,选取最优的模糊PID控制器中的量化因子和比例因子,再经模糊控制调整PID控制参数。利用Amesim建立钻进回转小车基于PSO的模糊PID控制系统模型,仿真结果表明:基于PSO的模糊PID控制的调节时间为0.66 s,比模糊PID控制减小57.7%,比传统PID控制减小66.2%,且稳态误差最小为0.01%,具有更良好的响应速度和稳定性,提高了控制系统的性能及对工况变化的适应能力。To adapt to varied load and geological conditions,this study designs a high-efficiency,low-energy-consumption drilling control system for cone drills,aimed at reducing drill vibrations and enhancing automation.A Particle Swarm Optimization(PSO)-based method is proposed to optimize the parameters of a fuzzy PID controller.By leveraging the iterative optimization capability of PSO,the optimal quantization and scaling factors for the fuzzy PID controller are selected,followed by fuzzy adjustments to the PID control parameters.A PSO-based fuzzy PID control system model is developed using Amesim.Simulation results indicate that the PSO-based fuzzy PID control achieves an adjustment time of 0.66 s,representing a reduction of 57.7%compared to fuzzy PID control and 66.2%compared to traditional PID control,with a minimum steady-state error of 0.01%.This approach demonstrates superior response speed,stability,and improved control performance,enhancing adaptability to varying working conditions.

关 键 词：粒子群算法 模糊PID 钻进控制系统 AMESIM 仿真 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置] TD422.1[自动化与计算机技术—控制科学与工程]