Real-time parameter identification for self-designing flight control

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 ifs 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.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>