CAJ | 학술논문

针对标准PSO算法在优化过程后期易过早收敛问题，提出一种改进PSO算法，并对电力推进无人潜艇感应推进电机进行参数辨识。改进PSO算法通过对学习因子的调节，使粒子在优化过程初期具有较强的全局搜索能力，在优化过程后期快速收敛于全局最优解。改进PSO算法以感应推进电机的dq轴实际输出电流、电压作为参数辨识系统的输入，将电机的实际输出电流和电气模型的观测电流之间的差方和作为目标函数。通过实验，将改进粒子群优化算法、标准粒子群优化算法和遗传算法所辨识出的参数进行对比，结果表明，改进PSO算法可以获得更准确的感应推进电机辨识参数。
침대표준PSO산법재우화과정후기역과조수렴문제，제출일충개진PSO산법，병대전력추진무인잠정감응추진전궤진행삼수변식。개진PSO산법통과대학습인자적조절，사입자재우화과정초기구유교강적전국수색능력，재우화과정후기쾌속수렴우전국최우해。개진PSO산법이감응추진전궤적dq축실제수출전류、전압작위삼수변식계통적수입，장전궤적실제수출전류화전기모형적관측전류지간적차방화작위목표함수。통과실험，장개진입자군우화산법、표준입자군우화산법화유전산법소변식출적삼수진행대비，결과표명，개진PSO산법가이획득경준학적감응추진전궤변식삼수。
Aiming at the problem of standard PSO algorithm easy to converge at the end stage of optimization process , the paper proposed an advanced particle swarm optimization ( PSO) for parameter estimation of marine induction propul-sion motor in electric propulsion unmanned submarine .The advanced PSO modified the learning coefficients so as to im-prove the global search capability in the early stage of the op-timization process , and then converged particles to the global optimum at the end stage .The advanced PSO algorithm used the difference between the measurements of the dq-axis cur-rents of induction propulsion motor and the estimation currents of electrical model as the objective function.Estimated parameterscomparison of induction propulsion motor among theadvanced PSO algorithm, the standard PSO algorithm and geneticalgorithm show that the advanced PSO algorithm can getmore accurate estimated parameters of induction propulsionmotor.