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General information |
| Author |
Pu, Huangzhong; Zhen, Ziyang; Jiang, Ju; Wang, Daobo |
| Published |
InTech Open Access Publisher, 2013
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| Abstract |
A novel intelligent control strategy based on a
brain emotional learning (BEL) algorithm is investigated in
the application of the attitude control of a small unmanned
aerial vehicle (UAV) in this study. The BEL model imitates
the emotional learning process in the amygdalaorbitofrontal
(A‐O) system of mammalian brains. Here it is
used to develop the flight control system of the UAV. The
control laws of elevator, aileron and rudder manipulators
adopt the forms of traditional flight control laws, and three
BEL models are used in above three control loops, to online
regulate the control gains of each controller.
Obviously, a BEL intelligent control system is self‐learning
and self‐adaptive, which is important for UAVs when
flight conditions change, while traditional flight control
systems remain unchanged after design. In simulation, the
UAV is on a flat flight and suddenly a wind disturbs it
making it depart from the equilibrium state. In order to
make the UAV recover to the original equilibrium state, the
BEL intelligent control system is adopted. The simulation
results illustrate that the BEL‐based intelligent flight
control system has characteristics of better adaptability and
stronger robustness, when compared with the traditional
flight control system. |
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International Journal of Advanced Robotic Systems
Author: Ottaviano, Erika; Ceccarelli, Marco; Husty, Manfred; Yu, Sung-Hoon; Kim, Yong-Tae; Park, Chang-Woo; Hyun, Chang-Ho; Chen, Xiulong; Feng, Weiming; Sun, Xianyang; Gao, Qing; Grigorescu, Sorin M.; Pozna, Claudiu; Liu, Wanli; Zhankui, Wang; Guo, Meng; Fu, Guoyu; Zhang, Jin; Chen, Wenyuan; Peng, Fengchao; Yang, Pei; Chen, Chunlin; Ding, Rui; Yu, Junzhi; Yang, Qinghai; Tan, Min; Polden, Joseph; Pan, [...]
Published: 2004
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