On Position and Attitude Control of Flapping Wing Micro-aerial Vehicle

2020 ◽  
pp. 207-216
Author(s):  
Dexiu Ma ◽  
Long Jin ◽  
Dongyang Fu ◽  
Xiuchun Xiao ◽  
Mei Liu
Keyword(s):  
Attitude Control ◽  
Flapping Wing ◽  
Micro Aerial Vehicle ◽  
Aerial Vehicle

Fuzzy Sliding Mode Control for Attitude of Flapping Wing Micro Aerial Vehicle Based on Variable Rate Reaching Law

2012 ◽  
Vol 178-181 ◽  
pp. 2801-2804
Author(s):  
Sheng Bin Hu ◽  
Wen Hua Lu ◽  
Zhi Yi Chen ◽  
Lei Lei ◽  
Yi Xuan Zhang
Keyword(s):  
Attitude Control ◽  
Sliding Mode ◽  
Flapping Wing ◽  
Variable Rate ◽  
Micro Aerial Vehicle ◽  
Fuzzy Sliding Mode Control ◽  
Reaching Law ◽  
Control Scheme ◽  
Aerial Vehicle ◽  
Fuzzy Sliding Mode

A fuzzy sliding mode control scheme based on variable rate reaching law for attitude control of flapping wing micro aerial vehicle is proposed in this paper. Based on the feedback linearization technique, a sliding mode controller is designed. To faster response speed, a fuzzy controller is designed to adaptively tune the slope of sliding mode surface. To reduce the chattering, the variable rate reaching law is proposed. The variable rate reaching law is composed of the distance from current point to sliding mode surface in phase plane. The simulation studies for attitude control of a flapping wing micro aerial vehicle have been carried out. Simulation results show that the proposed control scheme is effective.

Flapping Wing Micro Aerial Vehicle Attitude Control with Fuzzy Sliding Mode Controller Based on RBF Neural Network

2012 ◽  
Vol 443-444 ◽  
pp. 177-182
Author(s):  
Sheng Bin Hu ◽  
Wen Hua Lu ◽  
Min Xun Lu
Keyword(s):  
Neural Network ◽  
Sliding Mode Control ◽  
Attitude Control ◽  
Sliding Mode ◽  
Flapping Wing ◽  
Micro Aerial Vehicle ◽  
Mode Control ◽  
Attitude Tracking ◽  
Aerial Vehicle ◽  
Fuzzy Sliding Mode

A adaptive fuzzy Sliding Mode Control (SMC) scheme based on Radial Basis Function Neural Network (RBFNN) for attitude tracking control of Flapping Wing Micro Aerial Vehicle (FWMAV) is proposed in this paper. A RBFNN is used to compute the equivalent control of sliding mode control, An adaptive algorithm is used for weight adaptation of the RBFNN and A Lyapunov function is selected for the design of the SMC. The simulation results of FWMAV demonstrate that the control scheme is effective.

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