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Open AccessArticle

Real-Time Model-Free Minimum-Seeking Autotuning Method for Unmanned Aerial Vehicle Controllers Based on Fibonacci-Search Algorithm

1
Institute of Control, Robotics and Information Engineering, Poznan University of Technology, Piotrowo 3a, 60-965 Poznan, Poland
2
Department of Cybernetics, Faculty of Electrical Engineering, Czech Technical University in Prague, Technicka 2, 166 27 Prague 6, Czech Republic
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(2), 312; https://doi.org/10.3390/s19020312
Received: 14 November 2018 / Revised: 30 December 2018 / Accepted: 9 January 2019 / Published: 14 January 2019
(This article belongs to the Section Intelligent Sensors)
The paper presents a novel autotuning approach for finding locally-best parameters of controllers on board of unmanned aerial vehicles (UAVs). The controller tuning is performed fully autonomously during flight on the basis of predefined ranges of controller parameters. Required controller properties may be simply interpreted by a cost function, which is involved in the optimization process. For example, the sum of absolute values of the tracking error samples or performance indices, including weighed functions of control signal samples, can be penalized to achieve very precise position control, if required. The proposed method relies on an optimization procedure using Fibonacci-search technique fitted into bootstrap sequences, enabling one to obtain a global minimizer for a unimodal cost function. The approach is characterized by low computational complexity and does not require any UAV dynamics model (just periodical measurements from basic onboard sensors) to obtain proper tuning of a controller. In addition to the theoretical background of the method, an experimental verification in real-world outdoor conditions is provided. The experiments have demonstrated a high robustness of the method to in-environment disturbances, such as wind, and its easy deployability. View Full-Text
Keywords: UAV; auto-tuning; extremum-seeking control; iterative learning; optimization UAV; auto-tuning; extremum-seeking control; iterative learning; optimization
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Giernacki, W.; Horla, D.; Báča, T.; Saska, M. Real-Time Model-Free Minimum-Seeking Autotuning Method for Unmanned Aerial Vehicle Controllers Based on Fibonacci-Search Algorithm. Sensors 2019, 19, 312.

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