DC MOTOR SYSTEM IDENTIFICATION BASED ON IMPROVED BAT ALGORITHM FOR DECREASING ENVIRONMENTAL POLLUTION DURING OIL AND GAS DRILLING

Oil drilling is a complex process. DC motors with good speed regulation characteristics, strong overload capacity, stable speed, and large starting torque can effectively reduce noise pollution and electromagnetic wave pollution, and can improve drilling efficiency and reduce costs. It is of great significance to study the system identification method based on the improved bat algorithm (ILSSIWBA) with high efficiency input/output ratio, energy saving and environmental protection. The bat algorithm has the problems of being easily trapped into a local optimum, slow convergence at a later stage, and unstable optimization results which is difficult to apply in actual system identification. In this paper, an improved bat algorithm is used to systematically identify the mathematical model of the motor. The iterative local search solves the problem that the bat algorithm has. The problem of the unstable optimization result of the bat algorithm is solved by the random inertia weight method. In addition, this study also provides a new idea for the problem that it is difficult to obtain accurate response for DC motors using static parameter estimation. The experimental results show that the error of the ILSSIWBA algorithm is significantly smaller than that of the static parameter estimation. The ILSSIWBA algorithm and the BA iterative process and the identification results show that the ILSSIWBA algorithm effectively improves the problems of the BA algorithm when used in system identification. This research has a good reference significance for effectively improving the motor noise pollution and electromagnetic wave pollution, improving the efficiency of oil drilling and saving costs.