改进鱼群算法在模拟机运动洗出优化中的应用

飞行模拟器是模拟和复现真实飞行活动的重要设备, 一直以来模拟器的模拟效果也是备受关注的. 但是, 基于经典洗出算法来还原运动轨迹的运动平台存在参数设置保守、模拟效果不佳等问题, 因此本文提出一种基于改进人工鱼群算法的滤波器参数优化方法. 该方法基于人体前庭感知误差模型得到相应的目标函数, 再利用改进后的鱼群算法对滤波器中的自然截止频率进行寻优, 最后通过在Simulink中建立的仿真模型对优化后的滤波器参数进行仿真验证. 结果表明: 相比于经典洗出算法与基本人工鱼群算法, 经改进后算法得到的新参数在算法洗出中可以有效提高运动的感知效果, 减小运动误差, 并且能够节约更多的运动空间.

Application of Improved Fish Swarm Algorithm in Optimization of Simulator Motion Washout

Flight simulators are important equipment for simulating and reproducing real flight activities, and the simulation effects of simulators have been attracting wide attention. However, the motion platform based on the classical washout algorithm for restoring motion trajectories faces problems such as conservative parameter settings and poor simulation effects. Therefore, this study proposes a filter parameter optimization method based on an improved artificial fish swarm algorithm. Specifically, by the human vestibular perception error model, the corresponding objective function is obtained; then, the improved fish swarm algorithm is used to optimize the natural cut-off frequency in the filter; finally, the optimized filter parameters are simulated and verified through the simulation model built on Simulink. The results show that compared with those of the classical washout algorithm and the basic artificial fish swarm algorithm, the new parameters obtained by the improved algorithm can effectively improve the motion perception effect during the algorithm washout, reduce the motion error, and save more motion space.