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Adaptive Discrete-Time Flight Control Using Disturbance Observer and Neural Networks.
IEEE Transactions on Neural Networks and Learning Systems
|February 15, 2019
Summary
This study presents an adaptive neural control (ANC) strategy for unmanned aerial vehicles, effectively managing uncertainties and disturbances. The proposed method ensures stable flight performance despite system complexities and input limitations.
Area of Science:
- Robotics
- Control Systems Engineering
- Artificial Intelligence
Background:
- Unmanned aerial vehicles (UAVs) face challenges in precise trajectory tracking due to system uncertainties, external disturbances, and actuator limitations.
- Existing control strategies often struggle to simultaneously address these complex, real-world operational constraints in discrete-time nonlinear systems.
- Developing robust control solutions is crucial for enhancing the reliability and performance of autonomous aerial systems.
Purpose of the Study:
- To develop an adaptive neural control (ANC) strategy for discrete-time nonlinear UAV dynamics.
- To address system uncertainties, bounded time-varying disturbances, and input saturation.
- To ensure robust and stable trajectory tracking performance for UAVs.
Main Methods:
- Utilized adaptive neural networks for approximating system uncertainties.
- Designed a discrete-time disturbance observer (DTDO) to mitigate bounded time-varying disturbances.
- Employed a backstepping technique combined with an auxiliary system and discrete-time tracking differentiator for control strategy.
- Applied discrete-time Lyapunov analysis to prove system stability.
Main Results:
- Successfully tackled system uncertainties using neural network approximation.
- Effectively restrained the impact of bounded disturbances via the designed DTDO.
- Demonstrated the boundedness of all signals within the closed-loop system.
- Validated the proposed ANC technique's feasibility through numerical simulations.
Conclusions:
- The proposed adaptive neural control strategy effectively handles uncertainties, disturbances, and input saturation in discrete-time nonlinear UAV systems.
- The integration of a discrete-time disturbance observer and backstepping-based ANC ensures robust trajectory tracking.
- Numerical simulations confirm the stability and practical applicability of the developed control approach for UAVs.
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