Dynamic Control Allocation through Kalman Filtering
Gianfranco Morani, Francesco Nebula, Federico Corraro and Marco Ariola
DOI : 10.3844/ajeassp.2019.46.56
American Journal of Engineering and Applied Sciences
Volume 12, Issue 1
Control Allocation (CA) in aviation is the problem of distributing the commands among the available actuation means, in order to ensure the achievements of the moments requested by the flight control laws. CA plays a key role in fault-tolerant control systems since it gives robust performances and stability also in presence of faults to one or more aircraft actuators. In this study, a new algorithm is proposed for Control Allocation under both static and dynamic constraints. The proposed algorithm aims at overcoming the most common limitations of the existing algorithms, most of which do not account for actuator dynamics (i.e. they compute a control command that might not be compatible with aircraft performance limitations) and rely on iterative methods. The proposed approach does not need an iterative procedure because it rearranges the CA as a state observer problem in which observer states are the actual commands to actuators and observer measurements are the requested moments by the flight control laws. The observer is implemented through a Kalman Filter (KF), with the actuator dynamics as process model and the algebraic relationships between moments and commands as measurement model. The effectiveness of the proposed CA strategy has been shown through a numerical analysis. The numerical simulations showed that the control commands computed by Control Allocation algorithms guarantee moments that match the ones requested by the attitude control laws. This has been verified in nominal conditions (i.e. no actuator faults) but also in faulty ones, where one or more actuators are subject to malfunctioning and, furthermore, in simulations scenarios in which aggressive maneuvers led to the saturation of one or more control surfaces.
© 2019 Gianfranco Morani, Francesco Nebula, Federico Corraro and Marco Ariola. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.