Model Predictive Control for Autonomous UAV Landings: A Comprehensive Review of Strategies, Applications and Challenges
Kesavan Panjavarnam, Zool Hilmi Ismail, Tang Howe Hing, Kazuma Sekiguchi, Gianmarco Goycochea Casas
Abstract
ABSTRACT This survey presents a structured review of model predictive control (MPC) strategies for autonomous UAV landings, covering both theory and practice. As UAVs are increasingly used in critical missions, robust and precise landing control is essential. The paper introduces the MPC framework—linear and nonlinear models, constraints, and optimization—and analyses 48 articles from 2017 to early April 2025, identified via Scopus. These studies are organized into six thematic areas: maritime platform landings, landings on moving ground vehicles, fixed‐wing UAVs in constrained settings, robust and fault‐tolerant control, sensor fusion and vision‐based methods, and cooperative multi‐agent strategies. Advanced topics include neural optimization, economic and distributed MPC, and learning‐based approaches using Gaussian Processes and deep neural networks to enhance real‐time adaptability under uncertainty. Bibliometric analysis reveals trends in publication frequency, geographic distribution, and research clustering. The review also highlights key challenges such as real‐time computation, robust perception, and sensor integration in GPS‐denied or turbulent environments. This work offers a comprehensive reference for the development of intelligent and resilient MPC‐based control systems for autonomous UAV landings.