【摘要】 飞行员驾驶直升机完成目标跟踪任务时，可以通过视觉和运动等线索感知当前飞行状态从而产生相应的控制行为。为研究飞行员在不同运动反馈方式下的控制行为，在无运动反馈和有滚转角运动反馈两种情况下进行了飞行员人在回路仿真试验，并提出一种遗传算法和高斯-牛顿法的混合优化算法用于飞行员模型参数估计。通过方差分析的方法探讨了不同运动反馈方式对飞行员控制行为以及飞行员模型参数的影响。研究结果表明：利用混合优化算法得到的飞行员模型参数，能够有效表示真实的飞行员控制行为与动力学特性；在不同的运动反馈方式下，飞行员会调整其控制策略，从而提高在目标跟踪任务中的表现并调节控制活动。更多还原

【Abstract】 During helicopter operations for target tracking tasks, pilots perceive the current flight state through visual and motion cues, thereby generating corresponding control behaviors. To study pilot control behavior under different motion feedback modes, this paper conducts pilot-in-the-loop simulation experiments under two conditions: Without motion feedback and with roll angle motion feedback. A hybrid optimization algorithm combining genetic algorithms and the Gauss-Newton method is proposed for pilot model parameter estimation. Furthermore, the effects of different motion feedback modes on pilot control behavior and pilot model parameters are explored using variance analysis. The research results indicate that the pilot model parameters obtained using the hybrid optimization algorithm can effectively represent the pilot’s actual control behavior and dynamic characteristics. Under different motion feedback modes, pilots adjust their control strategies to improve performance in target tracking tasks and regulate their control activities.更多还原