review · Engineering Reports
Unmanned aerial vehicles, particularly quadcopters, are increasingly used across surveillance, precision agriculture, and transport. However, their nonlinear dynamics, underactuated systems, and sensitivity to environmental disturbances create persistent obstacles to reliable autonomous control. An evaluation of advancements across the past five years investigates primary modelling frameworks, including Newton-Euler, Newton-Quaternion, and geometry-based stochastic models. It also assesses multiple control strategies, spanning observer-based, sliding mode, H-infinity, model predictive, and neural network-based approaches. Comparing these options highlights critical shortcomings in handling uncertainties, scaling multi-vehicle systems, and managing energy constraints. Hybrid control systems combining adaptive mechanisms, learning-based algorithms, and quaternion-based modelling emerge as the most viable route to achieve robust, scalable, and computationally efficient autonomy in dynamic operating environments.
Quadcopters are critical tools for sectors such as farming, transport, and surveillance, but unpredictable conditions and battery limitations restrict their reliability. Identifying effective control and modelling architectures enables developers to build smarter aerial systems. This knowledge helps direct engineering efforts towards hybrid methods that improve flight stability, energy efficiency, and operational safety in complex environments.
This work provides early-stage conceptual guidance for developers, drone manufacturers, and practitioners working in precision agriculture, surveillance, and transport. By mapping the strengths of hybrid adaptive and learning-based controllers against current energy and scalability constraints, it helps teams select suitable architectures for next-generation autonomous flight systems. The insights represent foundational analytical guidance rather than a market-ready deployment.
AI-generated from the published abstract. Always read the original work before citing.
ABSTRACT Unmanned Aerial Vehicles (UAVs), particularly quadcopters, have found growing applications across diverse sectors such as surveillance, precision agriculture, and transport. However, their nonlinear dynamics, underactuated systems, and sensitivity to disturbances present persistent challenges in achieving robust and autonomous control. This review systematically examines advancements in UAV modeling and control techniques over the past five years. The study evaluates key modeling frameworks, Newton–Euler, Newton–Quaternion, and Geometry‐Based Stochastic Models (GBSM), and analyzes a spectrum of control strategies, including observer‐based, sliding mode, H‐infinity, model predictive, and neural network‐based controllers. Through a comparative assessment of their robustness, computational efficiency, and adaptability, the manuscript identifies critical limitations in handling uncertainties, scalability in UAV systems, and energy constraints. The findings highlight that hybrid control strategies incorporating adaptive mechanisms, learning‐based algorithms, and quaternion‐based modeling offer significant potential for enhancing autonomy and control. Therefore, this review provides a foundational roadmap for researchers and practitioners aiming to develop intelligent, efficient, and scalable UAV control systems capable of thriving in dynamic operational environments.
This page summarises published work. The authoritative version sits with the publisher.
DOI: 10.1002/eng2.70215
Is something wrong with this record? Report it or request removal.
Discussion
Have you built on this work, tried to replicate it, or seen it applied in practice? Share what you know. Verified researchers and MARATTO™ domain experts can open a discussion, and any member can reply. Contributions are reviewed before they appear.
No discussion yet. Open the first thread.
New to MARATTO™? Create a free account.
