Title: Improving trajectory tracking for quadrotors under wind disturbances by a neural network-based control strategy

Authors: Jinxing Zhao; Yuhao Fan; Haohao Liu; Zinuo Zeng; Haolan Zheng

Addresses: School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China ' School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China ' School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China ' School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China ' School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China

Abstract: Accurate trajectory tracking for a quadrotor is challenging in windy environments. This study proposes a novel control framework that combines the model predictive control (MPC) and a neural network state space model (NNSSM) to improve trajectory tracking drift under strong wind disturbances. The aerodynamic effects are explicitly modelled with a small multi layer perceptron (MLP) neural network by introducing the influences of the aerodynamic disturbances on the control input and state. Then the NNSSM could be achieved by combing the quadrotor's simple dynamic model and the MLP model, and incorporated into the MPC framework as the predictive model. In this way, a neural network-based MPC (NNMPC) capable of compensating the wind disturbances is achieved. A simulation experiment has been performed to evaluate the trajectory tracking performance, and shows that the NNMPC greatly reduces trajectory tracking errors compared with the MPC neglecting the aerodynamic disturbances in a windy environment.

Keywords: quadrotors; trajectory tracking; wind disturbances; neural network control; MPC; model predictive control.

DOI: 10.1504/IJMMS.2025.150094

International Journal of Mechatronics and Manufacturing Systems, 2025 Vol.18 No.2, pp.182 - 199

Received: 06 May 2025
Accepted: 01 Oct 2025
Published online: 28 Nov 2025 *

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