Optimization of a neural network based direct inverse control for controlling a quadrotor unmanned aerial vehicle

Abstract

Abstract. UAVs are mostly used for surveillance, inspection and data acquisition. We have developed a Quadrotor UAV that is constructed based on a four motors with a lift-generating propeller at each motors. In this paper, we discuss the development of a quadrotor and its neural networks direct inverse control model using the actual flight data. To obtain a better performance of the control system of the UAV, we proposed an Optimized Direct Inverse controller based on re-training the neural networks with the new data generated from optimal maneuvers of the quadrotor. Through simulation of the quadrotor using the developed DIC and Optimized DIC model, results show that both models have the ability to stabilize the quadrotor with a good tracking performance. The optimized DIC model, however, has shown a better performance, especially in the settling time parameter.

Attitude and Altitude Control of Quadcopter Maneuvers using Neural Network–Based Direct Inverse Control

“…Our research is based on a cross configuration of a quadcopter constructed in the Computational Intelligence and Intelligent Systems Laboratory, Universitas Indonesia. We previously also developed an NN-DIC scheme as a control system and optimized it to easily control the hovering state of the quadcopter (Heryanto et al, 2015). Our results showed that the optimized NN-DIC system improved the controller's performance, as indicated by a faster settling time compared with that of a non-optimized NN-DIC method.…”