New Methodology for Optimal Flight Control Using Differential Evolution Algorithms Applied on the Cessna Citation X Business Aircraft – Part 1. Design and Optimization

Article English OPEN
Yamina BOUGHARI ; Georges GHAZI ; Ruxandra Mihaela BOTEZ ; Florian THEEL (2017)
  • Publisher: National Institute for Aerospace Research “Elie Carafoli” - INCAS
  • Journal: INCAS Bulletin (issn: 2066-8201, eissn: 2247-4528)
  • Related identifiers: doi: 10.13111/2066-8201.2017.9.2.3
  • Subject: Flight Control | Linear Quadratic Regulator | Optimal Control | Differential Evolution | Control Augmentation System | Stability Augmentation System | Proportional Integrator Derivative Tuning | Motor vehicles. Aeronautics. Astronautics | TL1-4050

Setting the appropriate controllers for aircraft stability and control augmentation systems are complicated and time consuming tasks. As in the Linear Quadratic Regulator method gains are found by selecting the appropriate weights or as in the Proportional Integrator Derivative control by tuning gains. A trial and error process is usually employed for the determination of weighting matrices, which is normally a time consuming procedure. Flight Control Law were optimized and designed by combining the Deferential Evolution algorithm, the Linear Quadratic Regulator method, and the Proportional Integral controller. The optimal controllers were used to reach satisfactory aircraft’s dynamic and safe flight operations with respect to the augmentation systems’ handling qualities, and design requirements for different flight conditions. Furthermore the design and the clearance of the controllers over the flight envelope were automated using a Graphical User Interface, which offers to the designer, the flexibility to change the design requirements. In the aim of reducing time, and costs of the Flight Control Law design, one fitness function has been used for both optimizations, and using design requirements as constraints. Consequently the Flight Control Law design process complexity was reduced by using the meta-heuristic algorithm.
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