Evolutionary Algorithms for Robust Cessna Citation X Flight Control

Cessna Citation X Business Aircraft Eigenvalue Stability – Part2: Flight Envelope Analysis

“…These figures obtained using the Matlab command summarize the information about the region; in fact the method selected, the candidate Lyapunov function, the approach, the order of discretization, and the bounds of the normalized uncertainties used in the LFR model are indicated. The results represent the Number of Optimizations denoted by NOPs that have been solved (they correspond to the number of tiles attempted to be cleared, that is expressed by the sum of the number of the "green" plus the number of "red" tiles) [6], therefore to the time taken for the region analysis. These results are presented graphically in Figures 5 and 7, where they indicate the sub-regions where the analysis has been cleared; the stable sub-regions were in "green", the unstable sub-regions were in "red", and the unknown sub-regions were in "white"; when the sub-regions (tiles) are unknown so denoted in "white", the aircraft cannot be trimmed for its corresponding altitudes h and TAS; the results were expressed in percentages (%) of the area of the analyzed region in Figures 5 and 7.…”

Optimization of Engine Model Parameters Gain and Time Constant for the Cessna Citation X Business Aircraft Engine

“…The flight cases are explained in detail in these references. Various optimal control methodologies have been studied for the Cessna Citation X in [3,4,5].…”

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

“…In [21], [22], and [23] the authors used a PID controller based on genetic tuning. In [24][25][26][27][28][29] the Differential Evolution and Genetic Algorithm were used to optimize robust flight controllers.…”