Neural Dynamics for Adaptive Attitude Tracking Control of a Flapping Wing Micro Aerial Vehicle

2021 ◽  
Author(s):  
Mei Liu ◽  
Dexiu Ma ◽  
Shuai Li
Author(s):  
Mohammad A. Ayoubi ◽  
Chokri Sendi

In this paper, we use the Newton-Euler formulation to derive the equations of motion of a quadrotor unmanned aerial vehicle. We use the Modified Rodrigues Parameters to describe the attitude motion of a quadrotor for large attitude angles. Then, a globally stable feedback law for the problem of attitude tracking control of the vehicle was derived based on the Lyapunov’s direct method. Simulation results confirm that the proposed controller can track a reference attitude signal in the presence of parameter uncertainty, time delay, and slow time-varying external moments.


2012 ◽  
Vol 443-444 ◽  
pp. 177-182
Author(s):  
Sheng Bin Hu ◽  
Wen Hua Lu ◽  
Min Xun Lu

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.


Author(s):  
Dinesh D Dhadekar ◽  
S E Talole

In this article, position and attitude tracking control of the quadrotor subject to complex nonlinearities, input couplings, aerodynamic uncertainties, and external disturbances coupled with faults in multiple motors is investigated. A robustified nonlinear dynamic inversion (NDI)-based fault-tolerant control (FTC) scheme is proposed for the purpose. The proposed scheme is not only robust against aforementioned nonlinearities, disturbances, and uncertainties but also tolerant to unexpected occurrence of faults in multiple motors. The proposed scheme employs uncertainty and disturbance estimator (UDE) technique to robustify the NDI-based controller by providing estimate of the lumped disturbance, thereby enabling rejection of the same. In addition, the UDE also plays the role of fault detection and identification module. The effectiveness and benefits of the proposed design are confirmed through 6-DOF simulations and experimentation on a 3-DOF Hover platform.


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