Adaptive neural network-based super-twisting sliding mode control for UAV trajectory tracking under disturbances
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View abstract on PubMed
Summary
This summary is machine-generated.This study presents a novel control strategy for quadrotor systems, enhancing tracking control in disturbed environments. The method ensures accurate position estimation and robust disturbance rejection for improved performance.
Area Of Science
- Robotics
- Control Systems Engineering
- Artificial Intelligence
Background
- Quadrotor systems face challenges in maintaining stable flight and accurate trajectory tracking, especially in environments with external disturbances and uncertain states.
- Existing control methods often struggle with fast-tracking requirements and effective disturbance rejection simultaneously.
Purpose Of The Study
- To develop and validate a novel control strategy for fast-tracking control of quadrotor systems in perturbed environments with uncertain states.
- To enhance the robustness and accuracy of quadrotor tracking control through adaptive and intelligent control techniques.
Main Methods
- A two-phase control strategy integrating adaptive super-twisting nonsingular terminal sliding mode control (AST-NTSMC) with a multi-layer neural network (MLNN).
- Phase 1: MLNN for unknown quadrotor position estimation.
- Phase 2: AST-NTSMC for fast-tracking control and external disturbance rejection, with stability analysis via Lyapunov theory.
Main Results
- Accurate quadrotor position estimation using the MLNN.
- Rapid convergence of the AST-NTSMC surface, ensuring fast-tracking control.
- Effective rejection of external disturbances and chatter-free control performance.
- Adaptive estimation of uncertain disturbance bounds.
Conclusions
- The proposed integrated control strategy significantly enhances quadrotor tracking performance in challenging environments.
- The combination of MLNN and AST-NTSMC offers a robust and effective solution for fast-tracking control and disturbance rejection.
- Extensive simulations and hardware-in-the-loop tests validate the superiority and effectiveness of the proposed control technique.
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