ArticleSensors (Basel, Switzerland)2024
The Application and Optimisation of a Neural Network PID Controller for Trajectory Tracking Using UAVs.
Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- PPO-Based Reinforcement Learning Control of a Flapping-Wing Robot with a Bio-Inspired Sensing and Actuation Feather Unit.Sensors (Basel, Switzerland) · 2026Article
- Heterogeneous agricultural robots: a review of collaborative sensing and control, challenges and opportunities.Frontiers in plant science · 2026Review
- LSTM-Enhanced TD3 and Behavior Cloning for UAV Trajectory Tracking Control.Biomimetics (Basel, Switzerland) · 2025Article
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Authors and funding
3 authors.
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Abstract
This paper considers the problem of flying a UAV along a given trajectory at speeds close to the speed of sound and above. A novel pitch channel control system is presented using the example of a trajectory with rapid and large changes in flight height. The control system uses a proportional-integral-differential (PID) controller, whose gains were first determined using the Ziegler-Nichols II method. The determined gains were then optimised to minimise height error using a recurrent back-propagation neural network (PIDNN), with which new controller gains were determined, which is also a novelty of this study. Simulations were carried out for flights at subsonic speeds close to the speed of sound and supersonic speeds, at low and high altitudes. The simulations showed that determining controller gains using a recurrent neural network significantly minimises height errors and increases the flexibility of the PID controller.
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Registered trials
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