Trajectory Control of Autonomous Airships

Trajectory control for autonomous airships encompasses the guidance, navigation and control strategies required to direct lighter-than-air vehicles along predefined routes with high accuracy and robustness. The inherent buoyancy, large aerodynamic damping and underactuated nature of airships pose challenges in countering wind disturbances, actuator saturation and structural flexibility. Modern approaches integrate stability augmentation systems, predictive control schemes and adaptive observers to estimate external perturbations and compensate for model uncertainties. Control architectures often decompose the problem into kinematic path-following and dynamic tracking loops, enabling smooth waypoint transitions, hover and near-hover manoeuvres, and long-endurance missions in the stratosphere or urban environments. Advances in sensor fusion, real-time optimisation and learning-based adaptation have begun to bridge the gap between high-fidelity simulation and field deployment, paving the way for environmental monitoring, communications relay and precision surveillance applications.

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Trajectory Control of Autonomous Airships publication trend

The graph below shows the total number of articles in trajectory control of autonomous airships across all publications each year (not limited to Nature Index journals).

Technical terms

Path-following control: Steering approach that directs a vehicle to remain on a predefined spatial route.

Model predictive control (MPC): Optimisation-based method that computes control inputs by predicting future system behaviour over a finite horizon.

Sliding mode control: Robust technique enforcing system trajectories to “slide” along a designed surface in state space, ensuring disturbance rejection.

Reinforcement learning: Data-driven paradigm in which control policies are acquired through trial-and-error interactions and reward feedback.

Incremental nonlinear dynamic inversion (INDI): Real-time control strategy that compensates for nonlinear dynamics incrementally, improving trajectory tracking under uncertainty.

References

1. Trajectory Tracking Control for a Stratospheric Airship Subject to Constraints and Unknown Disturbances. IEEE Access (2020).
2. Three‐Dimensional Path‐Following Control of a Robotic Airship with Reinforcement Learning. International Journal of Aerospace Engineering (2019).
3. Hexa-Propeller Airship for Environmental Surveillance and Monitoring in Amazon Rainforest. Aerospace (2024).