Flight control clearance of the Cessna Citation X using evolutionary algorithms

Author(s):  
Yamina Boughari ◽  
Ruxandra Mihaela Botez ◽  
Georges Ghazi ◽  
Florian Theel

In this paper, an Aircraft Research Flight Simulator equipped with Flight Dynamics Level D (highest level) was used to collect flight test data and develop new controller methodologies. The changes in the aircraft’s mass and center of gravity position are affected by the fuel burn, leading to uncertainties in the aircraft dynamics. A robust controller was designed and optimized using the H∞ method and two different metaheuristic algorithms; in order to ensure acceptable flying qualities within the specified flight envelope despite the presence of uncertainties. The H∞ weighting functions were optimized by using both the genetic algorithm, and the differential evolution algorithm. The differential evolution algorithm revealed high efficiency and gave excellent results in a short time with respect to the genetic algorithm. Good dynamic characteristics for the longitudinal and lateral stability control augmentation systems with a good level of flying qualities were achieved. The optimal controller was used on the Cessna Citation X aircraft linear model for several flight conditions that covered the whole aircraft’s flight envelope. The novelty of the new objective function used in this research is that it combined both time-domain performance criteria and frequency-domain robustness criterion, which led to good level aircraft flying qualities specifications. The use of this new objective function helps to reduce considerably the calculation time of both algorithms, and avoided the use of other computationally more complicated methods. The same fitness function was used in both evolutionary algorithms (differential evolution and genetic algorithm), then their results for the validation of the linear model in the flight points were compared. Finally, robustness analysis was performed to the nonlinear model by varying mass and gravity center position. New tools were developed to validate the results obtained for both linear and nonlinear aircraft models. It was concluded that very good performance of the business Cessna Citation X aircraft was achieved in this research.

2013 ◽  
Vol 303-306 ◽  
pp. 2227-2230 ◽  
Author(s):  
Ling Juan Hou ◽  
Zhi Jiang Hou

Aiming at the stochastic vehicle routing problems with simultaneous pickups and deliveries, a novel discrete differential evolution algorithm is proposed for routes optimization. The algorithm can directly be used for the discrete domain by special design. Computational simulations and comparisons based on a medium-sized problem of SVRPSPD is provided. Results demonstrate that the proposed algorithm obtains better results than the basic differential evolution algorithm and the existing genetic algorithm.


Author(s):  
Anatoly Sukov

This paper examines the algorithm of differential evolution that has appeared rather recently. This algorithm ascribed by its developers to a class of evolutionary algorithms is a comparatively non-complicated technique o f solution search as applied to multiparameter optimisation tasks. Nevertheless, there are two essential factors preventing from wide application of the considered solution search technique. One of them lies in the principle of coding vectors (variables) that constitute a population the algorithm works with. The second problem is of pure technical character: in the process of search, stagnation occurs, or impossibility to find new solutions, when there is no optimal solution in the population and the vectors available are not heterogeneous. Besides studying search possibilities (limitations) of the differential evolution, some ways to cope with the problem of stagnation so-as to raise the performance of the algorithm are also suggested.


2013 ◽  
Vol 726-731 ◽  
pp. 3673-3679 ◽  
Author(s):  
Li Liao ◽  
Jian Zhong Zhou ◽  
Qiang Zou

In allusion to the problem of flood disaster classification, this paper proposes a chaotic differential evolution algorithm for to calculate the best projection direction for a projection pursuit model. By using polynomial function and penalty function, the objective function can be solved more perfectly. The new algorithm has been examined and tested with a real flood disaster.


2009 ◽  
Vol 12 (1) ◽  
pp. 66-82 ◽  
Author(s):  
C. R. Suribabu

Water distribution networks are considered as the most important entity in the urban infrastructure system and need huge investment for construction. The inherent problem associated with cost optimisation in the design of water distribution networks is due to the nonlinear relationship between flow and head loss and availability of the discrete nature of pipe sizes. In the last few decades, many researchers focused on several stochastic methods of optimisation algorithms. The present paper is focused on the Differential Evolution algorithm (henceforth referred to as DE) and utilises a similar concept as the genetic algorithm to achieve a goal of optimisation of the specified objective function. A simulation–optimisation model is developed in which the optimization is done by DE. Four well-known benchmark networks were taken for application of the DE algorithm to optimise pipe size and rehabilitation of the water distribution network. The findings of the present study reveal that DE is a good alternative to the genetic algorithm and other heuristic approaches for optimal sizing of water distribution pipes.


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