摘要
研究矿井提升机自动化控制中精度不高且控制性能不稳定问题;目前的粒子群算法用于自动化控制系统时,存在早熟和算法参数难确定的缺陷,参数镇定效果较差;针对以上弊端,提出了细菌觅食粒子群优化算法的矿井自动化控制算法,改进算法将矿井自动化控制的主要参数作为算法输入变量,利用细菌趋化、繁殖以及驱散过程对粒子群算法的解进行优化,然后根据粒子群算法框架进行粒子更新,有效地提高了粒子群算法的全局求解能力,解决了矿井提升机控制准确度不高稳定性不好的难题;仿真结果表明,该算法具有较好的镇定效果,且算法控制精度比传统算法提高24.3%,具有较好的适用性。
The mine hoist automation control accuracy is not high and the control performance is not stable. The particle swarm optimiza tion algorithm is used to automatic control system, the defects of early maturity and algorithm parameters are difficult to determine, parame ter calming effect is poorer. In view of the above disadvantages, proposed the bacteria foraging particle swarm optimization algorithm of the mine automation control algorithm, improved algorithm to mine automation control input variables as main parameters in the algorithm, u sing bacterial chemotaxis, reproduction and disperse process of particle swarm optimization algorithm is optimized, and then based on particle swarm optimization algorithm framework particle update, effectively improve the global solving ability of particle swarm optimization algorithm, to solve the mine hoist control accuracy is not high the problem of poor stability. The simulation results show that the proposed algo rithm has good stabilization effect, control precision is 24.30% higher than that of traditional algorithm and the algorithm, has good applicability.
出处
《计算机测量与控制》
北大核心
2014年第1期113-115,共3页
Computer Measurement &Control
关键词
细菌觅食粒子群
矿井提升机
控制
细菌趋化
bacteria foraging particle swarm
mine hoist
control
bacterial chemotaxis
