凿岩机器人钻臂定位控制交叉精英反向粒子群算法  

Crossover elite opposition-based particle swarm optimization algorithm for positioning control of rock drilling robotic drilling arm

作　　者：黄开启[1] 陈荣华[1] 丁问司[2] HUANG Kai-qi;CHEN Rong-hua;DING Wen-si(School of Mechanical and Electrical Engineering, Jiangxi University of Science and Technology, Ganzhou Jiangxi 341000, China;School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou Guangdong 510640, China)

机构地区：[1]江西理工大学机电工程学院,江西赣州341000 [2]华南理工大学机械与汽车工程学院,广东广州510640

出　　处：《控制理论与应用》2017年第3期303-311,共9页Control Theory & Applications

基　　金：国家自然科学基金项目(11272122);广东省部产学研重大项目(2012A090300011);江西省科技厅对外合作重点项目(20123BBE50085)资助~~

摘　　要：在利用粒子群优化算法(particle swarm optimization,PSO)进行凿岩机器人钻臂定位过程中,存在收敛速度慢和易于陷入局部最优解等问题.为此,提出一种交叉精英反向粒子群优化算法(crossover elite opposition-based particle swarm optimization,CEOPSO)并给出算法的流程.建立凿岩机器人钻臂运动学模型并对其逆向运动学进行求解.将交叉算子引入EOPSO中,采用自适应惯性权重和交叉概率参数控制技术,在维护粒子个体与最优解之间信息交换的基础上,增加粒子个体之间的信息交换,提高算法的全局搜索能力和钻臂定位效率.仿真结果表明,CEOPSO的平均位置误差和平均姿态误差均小于PSO和EOPSO算法,且迭代过程平稳,可以有效提高凿岩机器人钻臂的定位控制性能.In the positioning process of rock drilling robotic drilling arm using particle swarm optimization(PSO)algorithm,there are some problems,such as low convergence speed,tending to be trapped in local optimal solution,etc..In order to solve these problems,a crossover elite opposition-based particle swarm optimization(CEOPSO)algorithm is presented and the algorithm flow is given in this paper.The kinematics model of drilling arm is established,and the inverse kinematics is solved by using the CEOPSO algorithm.The crossover operator is introduced into EOPSO.The adaptive inertia weight and the crossover probability parameter control technologies are adopted.On the basis of maintaining the information exchange between the individual and the optimal solution,the global searching ability of the algorithm and the positioning efficiency of drilling arm are improved by increasing the information exchange between the individual particles.Simulation results show that the average position error and mean posture error of CEOPSO are less than those of PSO and EOPSO,and its iterative process is stable.The positioning and control performance of rock drilling robotic drilling arm can be improved effectively.

关 键 词：凿岩机器人 钻臂 定位控制 粒子群优化 精英反向学习 交叉算子 运动学逆解 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TP242[自动化与计算机技术—控制科学与工程]