针对锚杆钻臂井下自动控制过程中存在的环境恶劣、地质复杂等特殊工况,需着重考虑PID的参数选取,为此采用随机分形搜索算法(SFS)对PID参数进行整定。由于随机分形搜索算法存在局部搜索能力不足、易陷入局部最优等问题,在面对机械臂待整定参数多及参数间耦合严重时,其整定效果不佳。因此,引入混沌初始化、正余弦策略、自适应K值选取策略以提高算法的局部搜索能力以及全局搜索速度;采用柯西、差分定向变异策略使算法跳出局部最优。通过标准测试函数测试表明改进后的随机分形搜索算法收敛速度及精度均有一定提高。最后通过锚杆钻臂参数整定仿真实验表明:改进后随机分形搜索算法整定的PID参数总体适应度值提高21%;各关节的量化指标表示改进后随机分形搜索算法跳出了局部最优,提高了整体寻优精度与速度。

As the anchor drill boom is operated in special working conditions with harsh environment and complex geology, it is necessary to consider the parameter selection of PID, in the underground automatic control of anchor drill boom. For this reason, the stochastic fractal search algorithm ( SFS) was used for the PID parameters setting. Due to the problems of insufficient local search capability and likelihood of falling into local optimum, the random fractal search algorithm is ineffective facing the multiple parameters to be calibrated and serious coupling between parameters of the robotic arm. Therefore, chaotic initialization, positive cosine strategy, and adaptive K value selection strategy were introduced to improve the local search capability and global search speed of the algorithm; Cauchy and differential directional variation strategies were adopted to make the algorithm jump out of the local optimum. It was verified by the standard test function that the improved stochastic fractal search algorithm has optimized the convergence speed and accuracy. Finally, according to the simulation experiment of anchor drill boom parameters setting, the overall adaptability value of the PID parameters set by the improved algorithm is increased by 21%; and the quantitative indexes of the joints indicate that the improved algorithm jumps out of the local optimum, and enhances the overall searching accuracy and speed.