Reference : Using the Cross-Entropy method for control optimization: A case study of see-and-avoi...
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Aerospace & aeronautics engineering
http://hdl.handle.net/10993/19120
Using the Cross-Entropy method for control optimization: A case study of see-and-avoid on unmanned aerial vehicles
English
Olivares Mendez, Miguel Angel mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) >]
Fu, Changhong [Universidad Politecnica de Madrid]
Kannan, Somasundar mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) >]
Voos, Holger mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit > ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)]
Campoy, Pascual [Universidad Politecnica de Madrid]
Jun-2014
Control and Automation (MED), 2014 22nd Mediterranean Conference of
Yes
No
International
Control and Automation (MED), 2014 22nd Mediterranean Conference of
June 2014
[en] Comuter vision ; Fuzzy control ; Optimization ; Cross-Entropy
[en] This paper presents an adaptation of the Cross-Entropy (CE) method to optimize fuzzy logic controllers. The CE is a recently developed optimization method based on a general Monte-Carlo approach to combinatorial and continuous multi-extremal optimization and importance sampling. This work shows the application of this optimization method to optimize the inputs gains, the location and size of the different membership functions' sets of each variable, as well as the weight of each rule from the rule's base of a fuzzy logic controller (FLC). The control system approach presented in this work was designed to command the orientation of an unmanned aerial vehicle (UAV) to modify its trajectory for avoiding collisions. An onboard looking forward camera was used to sense the environment of the UAV. The information extracted by the image processing algorithm is the only input of the fuzzy control approach to avoid the collision with a predefined object. Real tests with a quadrotor have been done to corroborate the improved behavior of the optimized controllers at different stages of the optimization process.
http://hdl.handle.net/10993/19120
10.1109/MED.2014.6961536

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