Trajectory tracking control of a flapping wing micro aerial vehicle via neural networks
This conference paper figures out the tracking control issue of a flapping wing micro aerial vehicle (FWMAV) subject to uncertain system parameters based on the model in Lagrangian form. As we know, there are many uncertainties in the complex nonlinear system. To deal with this problem, radial basis function neural networks (RBFNNs) are considered to substitute uncertainties of FWMAV model. Through Lyapunov stability analysis, all the control performances of the FWMAV system are guaranteed via selecting the appropriate control variables. The boundedness of system states is achieved. Finally, a series of simulations are realized with MATLAB and show good tracking results via our control methods.
2016 International Conference on Advanced Robotics and Mechatronics (ICARM)