Positioning control method for drilling arm of bolt drilling rig

Algebraic and geometric methods are commonly used to realize drilling arm positioning control of bolt drilling rig. However, there are some problems such as low efficiency, no solution, multiple solutions, or poor universality. Using particle swarm optimization (POS) algorithm for positioning control of the drilling arm has the advantages of simple programming, strong search performance and good fault tolerance. But it is easy to fall into the local optimal solution. At present, the drilling arm positioning control based on improved PSO algorithm has low overall optimization efficiency and long optimization time. In order to solve the above problems, a chaotic crossover elite mutation opposition-based PSO (CEMOPSO) algorithm is designed by introducing chaos initialization, crossover operation, mutation operation and extreme value perturbation based on elite opposition-based PSO (EOPOS) algorithm. The method uses standard test functions to test PSO algorithm, EOPSO algorithm, CEOPSO algorithm and CEMOPSO algorithm. The results show that CEMOPSO has the best stability, precision and convergence speed. The motion model of the drilling arm of the bolt drilling rig is established. The CEMOPSO algorithm is used to control the drilling arm positioning. The simulation of the control performance is carried out in Matlab. The results show that under the same iteration times and error precision constraints, the position error and posture error of the drilling arm have a very fast convergence rate from the initial iteration when using the CEMOPSO algorithm. The position error and posture error are smaller than those of the other three algorithms. The error curve is smooth, and the maximum position error is 0.005 m and the maximum posture error is 0.005 rad. When the position error is 1 mm and the posture error is 0.01 rad, the average iteration number of the CEMOPSO algorithm is 343. When the position error is 0.1 mm and the posture error is 0.001 rad, the average iteration number is 473. Under the same positioning precision, the convergence speed and stability of the CEMOPSO algorithm are better than those of the other three algorithms. The results meet the requirements of engineering application. The higher the accuracy of the solution, the better it is.