Adaptive Control Strategies for Unmanned Aerial Systems
Summary
Adaptive control strategies for unmanned aerial systems (UAS) have emerged as a critical enabler for reliable operation in uncertain environments. These methods endow aerial platforms with the capability to modify control laws in real time, responding to variations in aerodynamic parameters, payload shifts and external disturbances such as wind gusts. Central to many approaches is an online estimation of unmodelled dynamics, often achieved through disturbance observers or neural network-based estimators, which feeds corrective signals into the primary controller. Hybrid schemes integrate robust control frameworks, such as sliding mode algorithms, with adaptive elements to mitigate chattering while guaranteeing stability. Model-based predictive techniques have also been extended with adaptive layers to cope with time-varying uncertainties, balancing performance with computational tractability. Recent advances have further emphasised fault-tolerant designs, enabling UAS to detect and compensate for actuator or sensor failures, thereby maintaining mission continuity. Overall, the field is converging towards modular architectures that combine estimation, adaptation and robustness to ensure safe and efficient flight across a broad spectrum of operational conditions.
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Adaptive Control Strategies for Unmanned Aerial Systems publication trend
The graph below shows the total number of articles in adaptive control strategies for unmanned aerial systems across all publications each year (not limited to Nature Index journals).
Technical terms
Adaptive control: A methodology that adjusts controller parameters in real time to compensate for system uncertainties and changing operating conditions.
Sliding mode control: A robust control strategy that drives the system trajectory onto a predefined sliding surface and maintains motion along it, ensuring insensitivity to certain disturbances.
Disturbance observer: An estimator that reconstructs the effect of external disturbances on a system, enabling the controller to counteract them effectively.
Active Disturbance Rejection Control (ADRC): A control framework combining state estimation and feedback compensation to suppress both internal uncertainties and external disturbances.
Prescribed performance functions: Designer-defined bounds on transient and steady-state behaviour that the control system enforces regardless of model inaccuracies.
References
- Fixed Time Disturbance Observer Based Sliding Mode Control for a Miniature Unmanned Helicopter Hover Operations in Presence of External Disturbances. IEEE Access (2020).
- Robust Trajectory Tracking Control for Uncertain 3-DOF Helicopters With Prescribed Performance. IEEE/ASME Transactions on Mechatronics (2022).
- Neural Network-Based Active Fault-Tolerant Control Design for Unmanned Helicopter with Additive Faults. Remote Sensing (2021).
- Trajectory Tracking Active Disturbance Rejection Control of the Unmanned Helicopter and Its Parameters Tuning. IEEE Access (2021).
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