Automatic Landing Control Systems for Unmanned Aerial Vehicles
Summary
Automatic landing control systems enable unmanned aerial vehicles (UAVs) to execute precise, reliable touchdowns without human intervention. These systems integrate guidance, navigation and control functions to manage the final approach, flare and touchdown phases under varying environmental conditions, including gusty winds, deck motion on shipborne platforms and uneven terrain. Modern architectures draw on diverse sensor suites—such as inertial measurement units, GNSS, vision systems and laser rangefinders—to deliver real-time state estimation. Control algorithms range from classical proportional–integral–derivative schemes to advanced robust and adaptive methods, each designed to accommodate model uncertainties, sensor noise and external disturbances. Fault-tolerant designs ensure safe landings in the event of actuator or sensor failures, while optimisation-based strategies enforce input and state constraints. The global significance of automatic landing spans civilian applications (package delivery, precision agriculture and infrastructure inspection), military operations (shipborne recovery and contested environments) and emergency response (medical supply drops in disaster zones). Continued research seeks to improve touchdown accuracy, energy efficiency and resilience to evolving mission requirements, fostering fully autonomous aerial platforms.
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Automatic Landing Control Systems for Unmanned Aerial Vehicles publication trend
The graph below shows the total number of articles in automatic landing control systems for unmanned aerial vehicles across all publications each year (not limited to Nature Index journals).
Technical terms
Adaptive sliding mode control: A robust technique that combines sliding mode surfaces with adaptive laws to handle model uncertainties and component faults.
Extended state observer (ESO): An observer that concurrently estimates system states and external disturbances for real-time compensation.
Model predictive control (MPC): An optimisation-based method that solves a finite-horizon control problem at each step, respecting system constraints and predicting future behaviour.
H∞ loop shaping: A frequency-domain design approach that tailors open-loop gain to ensure robustness against disturbances and model uncertainties.
References
- Elman Neural Network‐Based Direct Lift Automatic Carrier Landing Nonsingular Terminal Sliding Mode Fault‐Tolerant Control System Design. Computational Intelligence and Neuroscience (2023).
- Active Disturbance Rejection Attitude Control for a Bird-Like Flapping Wing Micro Air Vehicle During Automatic Landing. IEEE Access (2020).
- Autonomous Landing of an UAV Using H∞ Based Model Predictive Control. Drones (2022).
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