Brain emotional learning-based intelligent tracking control for Unmanned Aircraft Systems with uncertain system dynamics and disturbance
Mohammad Jafari, Ric Fehr, Luis Rodolfo García Carrillo, Hao Xu · 2017
In this paper, a novel neurobiologically inspired intelligent tracking controller is developed and implemented for Unmanned Aircraft Systems (UAS) in presence of uncertain system dynamics and disturbance. The methodology adopted, known as Brain Emotional Learning Based Intelligent Controller (BELBIC), is based on a novel computational model of emotional learning in mammals' brain limbic system. Compared with conventional stable control, BELBIC is more suitable for practical UAS since it can maintain the realtime UAS performance without known system dynamic and disturbance. Furthermore, the learning capability and low computational complexity of BELBIC make it very promising for implementation in complex real-time applications. To evaluate the practical performance of proposed design, BELBIC has been implemented into a benchmark UAS. Numerical and experimental results demonstrated the applicability and satisfactory performance of the proposed BELBIC-inspired design.