Flight Time Improvement Using Adaptive Model Predictive Control for Unmanned Aerial Vehicles

TL;DR

This paper introduces an adaptive Model Predictive Control method for UAVs that significantly reduces flight time, outperforming traditional MPC, and paving the way for integrating intelligent algorithms into UAV control systems.

Contribution

The paper presents a novel MPC approach specifically designed to minimize UAV flight time, addressing limitations of classical MPC controllers and enhancing control efficiency.

Findings

Proposed MPC outperforms standard MPC in flight time reduction.

The approach demonstrates potential for integrating intelligent algorithms into UAV controllers.

Results show improved efficiency in UAV flight control.

Abstract

Intelligent aerial platforms such as Unmanned Aerial Vehicles (UAVs) are expected to revolutionize various fields, including transportation, traffic management, field monitoring, industrial production, and agricultural management. Among these, precise control is a critical task that determines the performance and capabilities of UAV systems. However, current research primarily focuses on trajectory tracking and minimizing flight errors, with limited attention to improving flight time. In this paper, we propose a Model Predictive Control (MPC) approach aimed at minimizing flight time while addressing the limitations of the commonly used classical MPC controllers. Furthermore, the MPC method and its application for UAV control are presented in detail. Finally, the results demonstrate that the proposed controller outperforms the standard MPC in terms of efficiency. Moreover, this approach shows potential to become a foundation for integrating intelligent algorithms into basic controllers.