Enhancing Situational Awareness by means of Hybrid Adaptive Neural Control of Vertical Flight in Unmanned Helicopter

This paper focuses on a critical component of the situational awareness, the neural control of autonomous vertical flight for an unmanned aerial vehicle. Autonomous vertical flight is a challenging but important task for tactical unmanned aerial vehicles to achieve high level of autonomy under adverse conditions. The fundamental requirement for vertical flight is the knowledge of the height above the ground, and a properly designed controller to govern the process. With the situational awareness strategy, we proposed a two stage flight control procedure using two adaptive neural networks to address the dynamics variation and performance requirement difference in initial and final stages of flight trajectory for a nontrivial small-scale helicopter model comprising five states, two inputs and two outputs. This control strategy for chosen helicopter model has been verified by simulation of descending and landing manoeuvres using software package Simulink and demonstrated good performance for fast situational awareness in real-time search-and-rescue operations.