自适应飞机驾驶员最优控制模型研究及应用

针对驾驶员最优控制模型(OCM)无法反映飞行员在未知环境下渐进适应过程这一缺点,采用自适应状态估计理论对OCM进行改进,建立了基于自适应状态估计的驾驶员最优控制模型(MOCM-AE),给出了算法流程。通过对比飞行试验数据和未知扰动下的着舰应用,对模型进行了评估。通过人机闭环仿真进行了飞行试验再现,得到了人机闭环频域曲线。对比结果表明,与OCM相比MOCM-AE的频域特性曲线与试验更为吻合。在着舰应用中,引入低空紊流作为未知扰动进行着舰仿真,结果表明OCM强烈依赖于先验经验,而MOCM-AE无论是否具有先验经验,无论是否存在未知扰动,均能取得良好着舰效果。在未知扰动和舰尾流影响下,MOCM-AE比传统OCM着舰精度提高59%,着舰点分布范围缩小29%,这体现了飞行员对未知环境的适应能力。

Research and application of adaptive optimal control pilot model

The traditional optimal control pilot model (OCM) is based on Kalman filter which cannot reveal the pilot behavior in time varying disturbance of unknown environment. To overcome the omissions of OCM, a Modified optimal control pilot model based on adaptive state estimate (MOCM-AE) is developed, and the algorithm implementation is given. By magnitude and phase comparison in frequency domain, the MOCM-AE is testified through flight test reproduction. And the MOCM-AE is more identical to the measurements than OCM. In carrier landing application, the OCM depends on the priori experience grievously, and the MOCM-AE shakes off the fetters of the prior knowledge. In unknown low altitude disturbance, it improves the landing accuracy by 59% and reduces the touchdown point dispersion scope by 29%. The advantage are shown in the model evaluation and MOCM-AE is more proper to describe the pilot behavior in unknown environment.