Reinforced Learning-Based Robust Control Design for Unmanned Aerial Vehicle

Innovation in UAV design technologies over the last decade and a half has resulted in capabilities that flourished the development of unique and complex multi-mission capable UAVs. These emerging new distinctive designs of UAVs necessitate development of intelligent and robust Control Laws which are independent of inherent plant variations besides being adaptive to environmental changes for achieving desired design objectives. Current research focuses on development of a control framework which aims to maximize the glide range for an experimental UAV employing reinforcement learning (RL)-based intelligent control architecture. A distinct model-free RL technique, abbreviated as ‘MRL’, is suggested which is capable of handling UAV control complications while keeping the computation cost low. At core, the basic RL DP algorithm has been sensibly modified to cater for the continuous state and control space domains associated with the current problem. Review of the performance characteristics through analysis of the results indicates the prowess of the presented algorithm to dynamically adapt to the changing environment, thereby making it suitable for complex designed UAV applications. Nonlinear simulations carried out under varying environmental conditions illustrated the effectiveness of the proposed methodology and its success over the conventional classical approaches.