Intelligent control for a drone by self-tunable Fuzzy Inference System
| Autor: | K. M. Zemalache, Hichem Maaref |
|---|---|
| Přispěvatelé: | LDEE, Université des sciences et de la Technologie d'Oran Mohamed Boudiaf [Oran] (USTO MB), Informatique, Biologie Intégrative et Systèmes Complexes (IBISC), Centre National de la Recherche Scientifique (CNRS)-Université d'Évry-Val-d'Essonne (UEVE), Université d'Évry-Val-d'Essonne (UEVE)-Centre National de la Recherche Scientifique (CNRS) |
| Rok vydání: | 2009 |
| Předmět: |
0209 industrial biotechnology
Engineering Adaptive neuro fuzzy inference system Adaptive control Tracking control Neuro-fuzzy business.industry Static feedback linearization controller 02 engineering and technology Fuzzy control system Fuzzy logic Drone [SPI.AUTO]Engineering Sciences [physics]/Automatic Self-tunable fuzzy inference system 020901 industrial engineering & automation [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing Control theory 0202 electrical engineering electronic engineering information engineering Fuzzy number Fuzzy set operations 020201 artificial intelligence & image processing business Intelligent control [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing |
| Zdroj: | Proc. of the 6th International Multi-Conference on Systems, Signals and Devices (SSD 2009) 6th International Multi-Conference on Systems, Signals and Devices (SSD 2009) 6th International Multi-Conference on Systems, Signals and Devices (SSD 2009), Mar 2009, Djerba, Tunisia. (elec. proc.), ⟨10.1109/SSD.2009.4956805⟩ |
| DOI: | 10.1109/ssd.2009.4956805 |
| Popis: | International audience; The work describes an automatically on-line Self-Tunable Fuzzy Inference System (STFIS) of a new configuration of mini-flying called XSF (X4 Stationnary Flyer) drone. A Fuzzy controller based on on-line optimization of a zero order Takagi-Sugeno fuzzy inference system (FIS) by a back propagation-like algorithm is successfully applied. It is used to minimize a cost function that is made up of a quadratic error term and a weight decay term that prevents an excessive growth of parameters. Thus, we carried out control for the continuation of simple trajectories such as the follow-up of straight lines, and complex (half circle, corner) by using the STFIS technique. This permits to prove the effectiveness of the proposed control law. We studied the robustness of the two controllers used in the presence of disturbances. We presented two types of disturbances, the case of a gust of wind and taking into account white noise disturbances. A comparison between the Self-Tunable Fuzzy Inference System (STFIS) and Adaptive Network based Fuzzy Inference System (ANFIS) is given. |
| Databáze: | OpenAIRE |
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