System identification and attitude control of a small scale unmanned helicopter

2009 ◽  
Author(s):  
C. Fan ◽  
Baoquan Song ◽  
Xuanping Cai ◽  
Yunhui Liu
Keyword(s):  
System Identification ◽  
Attitude Control ◽  
Small Scale ◽  
Unmanned Helicopter

Model-based optimal attitude and positioning control of small-scale unmanned helicopter

Robotica ◽  
2005 ◽  
Vol 23 (1) ◽  
pp. 51-63 ◽  
Author(s):  
Jinok Shin ◽  
Kenzo Nonami ◽  
Daigo Fujiwara ◽  
Kensaku Hazawa
Keyword(s):  
Network Theory ◽  
Attitude Control ◽  
Position Control ◽  
Small Scale ◽  
Unmanned Helicopter ◽  
Positioning Control ◽  
Model Based ◽  
Loop Dynamics ◽  
Neural Network Theory ◽  
Dynamics Simulations

In this paper, we propose a model-based control system design for autonomous flight and guidance control of a small-scale unmanned helicopter. Small-scale unmanned helicopters have been studied by way of fuzzy and neural network theory, but control that is not based on a model fails to yield good stabilization performance. For this reason, we design a mathematical model and a model-based controller for a small-scale unmanned helicopter system. In order to realize a fully autonomous small-scale unmanned helicopter, we have designed a MIMO attitude controller and a trajectory controller equipped with a Kalman filter-based LQI for a small-scale unmanned helicopter. The design of the trajectory controller takes into consideration the characteristics of attitude closed-loop dynamics. Simulations and experiments have shown that the proposed scheme for attitude control and position control is very useful.

scholarly journals Chaotic Artificial Bee Colony Algorithm for System Identification of a Small-Scale Unmanned Helicopter

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Li Ding ◽  
Hongtao Wu ◽  
Yu Yao
Keyword(s):  
System Identification ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
State Space Model ◽  
Global Optimum ◽  
Small Scale ◽  
Local Optimum ◽  
Unmanned Helicopter ◽  
Bee Colony ◽  
Chaotic Operator

The purpose of this paper is devoted to developing a chaotic artificial bee colony algorithm (CABC) for the system identification of a small-scale unmanned helicopter state-space model in hover condition. In order to avoid the premature of traditional artificial bee colony algorithm (ABC), which is stuck in local optimum and can not reach the global optimum, a novel chaotic operator with the characteristics of ergodicity and irregularity was introduced to enhance its performance. With input-output data collected from actual flight experiments, the identification results showed the superiority of CABC over the ABC and the genetic algorithm (GA). Simulations are presented to demonstrate the effectiveness of our proposed algorithm and the accuracy of the identified helicopter model.

Artificial bee colony optimised controller for small-scale unmanned helicopter

2017 ◽  
Vol 121 (1246) ◽  
pp. 1879-1896 ◽  
Author(s):  
R. Ma ◽  
H. Wu ◽  
L. Ding
Keyword(s):  
Attitude Control ◽  
Artificial Bee Colony ◽  
Control Method ◽  
Hierarchical Control ◽  
Small Scale ◽  
Unmanned Helicopter ◽  
Linear Quadratic ◽  
Linear Quadratic Gaussian ◽  
Bee Colony ◽  
Loop Transfer Recovery

ABSTRACTIn this paper, an efficient approach to design and optimize a flight controller of a small-scale unmanned helicopter is proposed. Given the identified helicopter model, the Linear Quadratic Gaussian/Loop Transfer Recovery (LQG/LTR) robust control method is applied for trajectory tracking and attitude control of the helicopter with a two-loop hierarchical control architecture. Since the performance of the controller extremely depends on its weighting matrices, the Artificial Bee Colony (ABC) algorithm is introduced to automatically select the parameters of the matrices. Comparative studies between optimal algorithms are also carried out. A series of flight experiments and simulations are conducted to investigate the effectiveness and robustness of the proposed optimised controller.

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