基于人工蜂群算法优化采煤机伺服系统PID参数  

Optimization of PID parameters of hydraulic servo system based on artificial bee colony algorithm

作　　者：郭松林 王光辉 GUO Songlin;WANG Guanghui(School of Electrical ＆ Control Engineering,Heilongjiang University of Science ＆ Technology,Harbin 150022,China)

机构地区：[1]黑龙江科技大学电气与控制工程学院,哈尔滨150022

出　　处：《工业仪表与自动化装置》2018年第2期29-32,39,共5页Industrial Instrumentation & Automation

基　　金：国家重大科学仪器设备开发专项(2012YQ150213)

摘　　要：针对采煤机电液伺服系统中PID控制器的参数寻优问题,利用人工蜂群算法来优化PID参数。人工蜂群算法通过模拟群蜂寻找花蜜的过程,将误差绝对值和控制输入平方项的时间积分作为优化目标,经过迭代寻优计算得到系统最优控制量。通过典型函数测试,对比分析遗传算法和粒子群优化算法,人工蜂群算法具有较好的全局收敛能力。结果表明:人工蜂群算法用于采煤机电液伺服系统的参数调节,比遗传算法和粒子群优化算法收敛速度快,超调量小,具有更好的动态响应性能,验证了该方案的可行性和有效性。This paper aims to add ress the parameter optimization of PID controller in electro-hydraulic servo system of coal mining machine,and proposes to optimize the PID parameters by artificial bee colony algorithm（ ABC）. The artificial bee colony algorithm was viewed as a process of seeking nectar by simulating peaks,the integration for absolute error and the square of control input were used as optimization goals,and the optimal control quantity was calculated through iterative optimization. Through the typical function test,it has better global convergence ability by contrasting genetic algorithm and particle swarm optimization algorithm. The simulation results show that this method,applied to the parameter adjustment of hydraulic servo system of coal miner,has better dynamic response performance than genetic algorithm and particle swarm optimization algorithm,which verifies the feasibility and effectiveness of the scheme.

关 键 词：人工蜂群算法 PID控制器 粒子群优化算法 遗传算法 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]