Abstract :
The appeal of adaptive control to the aerospace domain should be attributed to the neural network models adopted in online adaptive systems for their ability to cope with the demands of a changing environment. However, continual changes induce uncertainty that limits the applicability of conventional validation techniques to assure the reliable performance of such systems. In this paper, we present several advanced methods proposed for verification and validation (V&V) of adaptive control systems, including Lyapunov analysis, statistical inference, and comparison to the well-known Kalman filters. We also discuss two monitoring tools for two types of neural networks employed in the NASA F-15 flight control system as adaptive learners: the confidence tool for the outputs of a Sigma-Pi network, and the validity index for the output of a Dynamic Cell Structure (DCS) network.
Keywords :
Kalman filters; Lyapunov methods; adaptive control; aircraft control; neural nets; Kalman filters; Lyapunov analysis; NASA F-15 flight control; Sigma-Pi network; adaptive aircraft control systems; adaptive control; aerospace domain; neural network; statistical inference; verification and validation; Adaptive control; Adaptive systems; Aerodynamics; Aerospace control; Distributed control; Monitoring; NASA; Neural networks; Programmable control; Uncertainty;
