What does this research mean for the field?

A Lyapunov-based backstepping controller optimized with Particle Swarm Optimization provides superior robustness, faster disturbance rejection, and improved trajectory tracking accuracy for quadrotor UAVs compared to PID and FOPID controllers. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

The aim is to develop a robust controller for quadrotor UAVs that addresses control challenges from their nonlinear dynamics and external disturbances.

April 25, 2026Open Access

Guidance Navigation and Control for Quadrotor UAV Using Lyapunov-Based Backstepping

Key Points

The aim is to develop a robust controller for quadrotor UAVs that addresses control challenges from their nonlinear dynamics and external disturbances.
Developed a Lyapunov-based backstepping (LYP) controller with inner- and outer-loop architecture for position and attitude control.
Compared the LYP controller's performance against PID and FOPID controllers under various scenarios.
Optimized controller gains using Particle Swarm Optimization (PSO).
The LYP controller achieved superior robustness and faster disturbance rejection under nominal and challenging conditions.
Demonstrated improved tracking accuracy with a lower root mean square error (RMSE) compared to PID and FOPID controllers.

Abstract

Quadrotor UAVs present a significant control challenge due to their underactuated nature; strong coupling effects; nonlinear dynamics; and high sensitivity to unknown effect parameters, external disturbances, and uncertainties. To address this issue, this study proposes a Lyapunov-based backstepping (LYP) controller that ensures robust stability and precise trajectory tracking. The controller employs an inner- and outer-loop architecture for coupled position and attitude control. Its performance is compared with Proportional–Integral–Derivative (PID) and Fractional-Order PID (FOPID) controllers under three scenarios: nominal conditions, external disturbances, and model parameter uncertainties. All controller gains are optimized using Particle Swarm Optimization (PSO). Simulation results, which are evaluated using time-domain metrics and root mean square error (RMSE), demonstrate that the proposed LYP controller achieves superior robustness, faster disturbance rejection, and improved tracking accuracy compared to both PID and FOPID controllers.

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Cite This Study

Sąsiadek et al. (Thu,) studied this question.

synapsesocial.com/papers/69ec5bd288ba6daa22dad1fb https://doi.org/https://doi.org/10.3390/s26092611

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