Control system autonomy improvement: An attempt to introduce meta-heuristic algorithms into closed-loop UAV control systems

Abstract

This study proposes integrating Genetic Algorithms (GAs) into control systems to enhance autonomy, particularly for unmanned aerial vehicle (UAV) operations. Traditional control systems, which rely on expert knowledge and complex mathematical calculations, limit autonomy. In contrast, GAs offer robust global search capabilities, helping to avoid local optima and enhancing computational efficiency through parallel processing. Utilizing a modified Nonlinear Auto-Regressive eXogenous (NARX) model with feedback regulation ensures system stability and accurate tracking of target values, allowing the system to learn dynamic relationships essential for control in complex nonlinear conditions. We introduce a new GA-NARX based autonomous UAV control system designed for exploration in unfamiliar environments. Our enhanced system features a self-optimizing control mechanism that enables global optimization for peak performance. This advanced control system minimizes human-machine interaction by leveraging GAs' predictive abilities to anticipate future states while significantly improving the control precision. Overall, the design of this autonomous control system aims to optimize coordination and control strategies for UAV swarms, offering innovative solutions for efficient flight patterns.