The fuzzy position control for the four rotor flying robot

Abstract

This study deals with a position controller of the four rotor flying robot using a fuzzy controller. In recent years, the flying robot has been developed to be used in various fields such as dangerous field, disaster field and so on. We also have already developed the flying robot with four rotors. However it was difficult to control the position when the wind is blowing. The reason is uncertainty of the model. In case of the traditional linear and nonlinear controller methods, these methods rely on an accurate model of the dynamics and do not address disturbance rejection explicitly. So the environment model changes constantly, and equation of motion can't be expressed. Thus we use a fuzzy controller method that can compensate for model uncertainty. We aim to confirm the effectiveness of fuzzy control, two controllers that are PD controller and fuzzy controller were designed and implemented to control a simulation model of the flying robot. Their simulation results prove the efficiency of the fuzzy controller for flying robot.

Synopsis of Soft Computing Techniques used in Quadrotor UAV Modelling and Control

“…A fuzzy controller is more suitable for path tracking in outdoor condition than a simple PD controller; still it is insufficient for the response and steady-state error control when a fair wind is blowing [19].…”

“…In [19] a PD like fuzzy controller is described for position control to compensate for nonlinear disturbance such as the wind. For the position controller of the robot, there are two inputs to the fuzzy logic controller.…”

“…Then the fuzzy controller outputs the force for moving to the target position. For the output of fuzzy controller in [19] a simplified fuzzy inference method is used with fixed MFs and an expert knowledge based fuzzy rule set is defined. The result in [19] is that the proposed fuzzy controller is more suitable for path tracking in outdoor condition than a simple PD controller.…”

“…For the output of fuzzy controller in [19] a simplified fuzzy inference method is used with fixed MFs and an expert knowledge based fuzzy rule set is defined. The result in [19] is that the proposed fuzzy controller is more suitable for path tracking in outdoor condition than a simple PD controller. But the proposed fuzzy controller still presented not so good results for the response and steady-state error when a fair wind was blowing.…”

Flying control of small-type helicopter by detecting its in-air natural features

“…However, they have several unresolved problems, such as requiring advanced technologies for operation, and an inability to fly outside the field of vision of the operator. To resolve such issues, extensive research have been carried out on autonomous flight control for small helicopters (Angeletti et al, 2008;Lee et al, 2013a,b;Misaki and Fujimoto, 2008;Cho et al, 2009;Censi et al, 2013a,b;Noda et al, 2011Noda et al, , 2012Passow et al, 2009;Schmid et al, 2013;Ruangwiset, 2009;Budiyono et al, 2011;Lim and Machida, 2010;Salado et al, 2010;Jeong and Jung, 2013;Gurdan et al, 2007;Rejon and Aranda-Bricaire, 2007;Bae et al, 2007;Shimada et al, 2009;Fang et al, 2010;Weng and Abidin, 2006;Teoh et al, 2012).…”