A Fast Learning Control Strategy for Unmanned Aerial Manipulators

Abstract

We present an artificial intelligence-based control approach, the fusion of artificial neural networks and type-2 fuzzy logic controllers, namely type-2 fuzzy-neural networks, for the outer adaptive position controller of unmanned aerial manipulators. The performance comparison of proportional-derivative (PD) controller working alone and the proposed intelligent control structures working in parallel with a PD controller is presented. The simulation and real-time results show that the proposed online adaptation laws eliminate the need for precise tuning of conventional controllers by learning system dynamics and disturbances online. The proposed approach is also computationally inexpensive due to the implementation of the fast sliding mode control theory-based learning algorithm which does not require matrix inversions or partial derivatives. Both simulation and experimental results have shown that the proposed artificial intelligence-based learning controller is capable of reducing the root-mean-square error by around 50% over conventional PD and PID controllers.