Jafari, Mohammad (University of Nevada, Reno), Fehr, Ric (University of Nevada, Reno), GARCIA CARRILLO, Luis Rodolfo (Texas A&M University - Corpus Christi), Xu, Hao (University of Nevada, Reno)

Brain Emotional Learning-Based Intelligent Tracking Control for Unmanned Aircraft Systems with Uncertain System Dynamics and Disturbance

Scheduled for presentation during the "Bio Inspired UASs, Autonomy, and Training" (FrB1), Friday, June 16, 2017, 13:45−14:05, Salon E

2017 International Conference on Unmanned Aircraft Systems, June 13-16, 2017, Miami Marriott Biscayne Bay, Miami, FL,

This information is tentative and subject to change. Compiled on April 19, 2024

Abstract

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 real-time 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.