No abstract

A self-designing flight control system (SDFCS) could provide a cost-effective means for developing controllers for new aircraft by eliminating analyst-intensive design of numerous individual controllers, each optimized for a single flight condition. Additionally, the SDFCS could improve the capabilities of existing aircraft by enhancing control performance in new flight regimes such as high angle-of-attack or post-stall maneuvers. Finally, the SDFCS could automatically reconfigure the control system to account for sudden changes such as may result from airframe and/or effector impairment(s).Rapid identification of time-varying, nonlinear plants is an important enabling technology for most SDFCS concepts. In this paper, the authors present a modified sequential least squares (MSLS) parameter identification method and compare its performance to that of standard RLS techniques using a simulated nonlinear F-16 with multiaxes thrust-vectoring (MATV) aircraft. It is shown that MSLS offers significant improvement in performance over conventional RLS parameter identification by providing: (1) a recursive estimation algorithm that penalizes noisy estimates and is less subject to ill-conditioning as its forgetting factor is reduced, (2) detection of airframe and effector impairments and corresponding adjustments of the algorithm settings, and (3) an intelligent supervisor that injects a minimum level of effector random activity to ensure identifiability.