A Comparison of Adaptive Supervisory Switching Control Schemes for High Maneuverability Aircrafts

  • Andrei-Sorin Neamtu
  • Adrian-Mihail Stoica
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 187)

Abstract

Adaptive Supervisory Switching Control schemes work by introducing in the control scheme a supervisory unit which chooses, from a set of candidate controllers the one most suited for the current plant. There are two main classes of methods in this categoary. The first called Unfalsified Control Adaptive Supervisory Switching Control (UASSC) works by calculating for each candidate controller at discrete moments of time using the input/output data recorded up to that point a performance index and discarding from the candidate controller set those controllers which surpass a given threshold of this index. This process is called falsification. The second called Multi-Model Adaptive Switching Supervisory (MASSC), works by associating a dynamic nominal model with every candidate controller and comparing norms of sequences of estimation errors based on the various nominal models, as the candidate controller associated to the nominal model yielding the prediction norm of minimum magnitude is believed to be the most suitable one. Recently a new categories called Multi Model Unfalsifeid Adaptive Supervisory Swithing Control (MMUASSC) was introduced. The schemes belonging to this category combine the advantages of both Unfalsifeid and Multi-Model Control techniques. In this paper we review the theory behind the control techniques belonging to the first (UASSC) and third (MMUASSC) of the categories above and adapt it to the case of controlling a fighter aircraft. We also provide a case study, where we compare these control schemes on a simulated fighter aircraft.

Keywords

Adaptive Control Nominal Model Robust Controller Flight Condition Fighter Aircraft 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Andrei-Sorin Neamtu
    • 1
    • 2
  • Adrian-Mihail Stoica
    • 1
  1. 1.Faculty of Aerospace EngineeringPolitehnica University of BucharestBucharestRomania
  2. 2.National Aerospace Research Institute (Institutul National de Cercetari Aerospatiale)“Elie Carafoli” BucharestBucharestRomania

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