Handling qualities prediction based on 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, a Modified Optimal Control Pilot Model based on Adaptive State Estimate (AOCM) is developed. The pilot models are utilized to reproduce the flight test by pilot-aircraft closed-loop simulation. In frequency domain, the AOCM is testified by magnitude and phase comparison. In time domain, some unknown disturbance is led into the simulation, the attitude tracking results show that AOCM performs better than OCM. Both the evaluations in frequency domain and time domain show the merit of AOCM, and reflect the pilot character of environment adaption. AOCM and OCM are utilized in Handling Qualities(HQ) prediction, in all the four HQ test cases, with modified weighting matrixes, AOCM gets more accurate HQ rating prediction than OCM, it's identical to the flight test pilot rating. The result shows the HQ prediction is critical corresponded to the ratio of Qy/R. Accurate HQ prediction is based on the precisely Qy/R ratio chosen. And Qy/R ratio is corresponded to the aircraft dynamics, as the aircraft dynamics degrades the value of Qy/R should increase. It denotes that the pilot will pay more attention in flight observation while he keeps the same attention in controlling as before. Thus, the heavier mental workload degrades the pilot rating. Yet this assumption is still need to be testified.