基于差分进化算法的自行高炮随动系统PID参数整定

针对传统PID参数整定效率低且无法考虑构件间力元等因素对参数整定的影响问题，建立随动系统机电联合仿真模型，利用智能优化算法整定PID参数。建立了考虑行进间车体姿态扰动的随动控制系统模型，并结合上装虚拟样机建立随动系统的机电联合仿真模型，采用实车试验方法验证联合仿真模型的正确性。在此基础上，以时间乘以误差绝对值积分为优化目标函数，利用差分进化算法对PID参数进行整定，与原模型控制参数和遗传算法整定的参数进行比较。联合仿真结果表明：与原模型控制参数相比，使用差分进化算法整定的PID参数进行仿真，火力线控制误差的均方根值和标准差分别减少24.06%和25.20%，且收敛速度比遗传算法快；该建模方法和参数整定方法有效可行，对火力线控制精度优化具有理论参考价值。

PID Parameter Tuning of Self-propelled Antiaircraft Gun Servo System Based on Differential Evolution Algorithm

The traditional PID parameter tuning efficiency is low and the influence of factors such as force elements among components on the parameter tuning can not be considered. An electromechanical co-simulation model of servo system is established, and the PID parameters are tuned using intelligent optimization algorithm. A servo control system model considering the attitude disturbance of moving body is established, and an electromechanical joint simulation model of servo system is established by combining with the upper mounting virtual prototype. The method of real vehicle test is used to verify the correctness of the joint simulation model. On this basis, the integral of time multiplied by the absolute value of error is used as the optimization objective function, and the PID parameters are tuned using the differential evolution algorithm (DE), and compared with the original model control parameters and the genetic algorithm (GA) tuning parameters. The joint simulation results show that,compared with the original model control parameters, the RMS value and standard deviation of the axis of firepower control error are reduced by 24.06% and 25.20%, respectively, by using the PID parameters tuned by the differential evolution algorithm for simulation, and the convergence speed is faster than that of genetic algorithm. The modeling method and parameter tuning method are effective and feasible, and have theoretical reference value for the optimization of the control accuracy axis of firepower.