Neural Inverse Optimal Control Applied to Quadrotor UAV

This paper presents a neural nonlinear control scheme of a quadrotor unmanned aerial vehicle (UAV) for altitude and position trajectory tracking. The neural controller is based on a recurrent high order neural network identifier (RHONN) trained using an Extended Kalman Filter (EFK). The proposed nonlinear control strategy is based on inverse optimal control (IOC) technique for altitude and translation motion. In addition, the control law stabilize the orientation of the vehicle. The dynamical model is obtained by the Euler-Lagrange methodology. The effectiveness of this proposed control scheme is verified by simulation results.

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