Adaptive Neural-Network-Based Nonlinear Model Predictive Control for Quadrotor Formation Trajectory Tracking

This paper presents a new method for the accurate formation trajectory tracking of quadrotor unmanned aerial vehicles (UAVs) in the presence of unknown external disturbances. By integrating adaptive neural networks (ANN) with the nonlinear model predictive control (NMPC), this paper proposes a robus...

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Bibliographic Details
Published in:Chinese Control Conference pp. 4487 - 4492
Main Authors: Hu, Jinghe, Xian, Bin, Shao, Peng
Format: Conference Proceeding
Language:English
Published: Technical Committee on Control Theory, Chinese Association of Automation 28.07.2025
Subjects:
Adaptive Neural Network
Adaptive systems
Autonomous aerial vehicles
Formation control
Formation Trajectory Tracking
Heuristic algorithms
Nonlinear Model Predictive Control
Prediction algorithms
Predictive control
Quadrotors
Simulation
Trajectory tracking
Vehicle dynamics
ISSN:1934-1768
Online Access:Get full text
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Summary:This paper presents a new method for the accurate formation trajectory tracking of quadrotor unmanned aerial vehicles (UAVs) in the presence of unknown external disturbances. By integrating adaptive neural networks (ANN) with the nonlinear model predictive control (NMPC), this paper proposes a robust formation control strategy. For this control design, a radial basis function neural network (RBFNN) can learn the dynamic properties of the UAV system on line and updates its weights based on predetermined optimization vectors and the reference inputs for the UAVs. Meanwhile, the predictive and optimal advantages of the nonlinear model predictive algorithm are utilized to forecast the future states of the UAVs and further optimize control inputs, ensuring that the UAVs can effectively maintain the desired formation and follow the reference trajectory in the presence of unknown external disturbances. Simulation results demonstrate that the proposed UAV formation trajectory tracking control algorithm exhibits effective tracking performance under disturbances, and validate the efficacy of the algorithm presented in this paper.
ISSN:1934-1768
DOI:10.23919/CCC64809.2025.11179762