基于IPSO的大螺旋钻机钻进控制系统优化研究

Research on Optimization of Drilling Control System of Large Screw Drill Based on Improved Particle Swarm Optimization Algorithm

针对大螺旋钻机钻进控制系统PID环境适应性差、响应速度慢、调整时间长等问题,提出一种使用改进粒子群优化算法(IPSO)对PID进行参数优化的方法。首先对大螺旋钻机钻进控制系统部件进行适当简化,推导液压推进油缸传递函数。然后通过引入随迭代次数增加而递减的惯性因子和学习因子,实现对粒子群优化算法的改进,利用基准函数进行测试,证明IPSO收敛速度、精度与全局寻优能力均优于常规优化算法。选用性能指标中的绝对值时间积分作为IPSO适应值函数,建立IPSO优化PID参数流程。最后进行仿真分析:分别使用IPSO和传统Z-N整定法对PID参数进行优化,结果表明IPSO优化后的PID参数控制效果最好。

Aiming at the problems of poor environmental adaptability,slow response speed and long adjustment time of PID in drilling control system of large screw drill,a method of PID parameter optimization using improved particle swarm optimization(IPSO)algorithm is proposed.Firstly,the components of the drilling control system of the large screw drill are properly simplified,and the transfer function of the hydraulic propulsion cylinder is derived.By introducing the inertia factor and learning factor which decrease with the increase of iteration times,the particle swarm optimization algorithm is improved.The benchmark function is used to test,and it is proved that the convergence speed,accuracy and global optimization ability of IPSO are better than the conventional optimization algorithm.The absolute value time integral in the performance index is selected as the IPSO fitness function,and the IPSO PID parameter flow is established.Finally,the simulation analysis is carried out:IPSO and traditional Z-N tuning methods are used to optimize the PID parameters respectively.The results show that the PID parameter control effect after IPSO optimization is the best.