Robust Variant-Parameter Double Integral Multi- Layer Neural Dynamics for Tracking Tasks of Quadrotors in Unbounded Noisy Environments
Lin Xiao, Hao Liu, Qiuyue Zuo, Linju Li · IEEE Transactions on Intelligent Transportation Systems · 2025
In real-world scenarios, quadrotors face significant noise challenges from both internal and external sources, necessitating more robust controllers. While most existing models assume bounded noise, unbounded noise poses greater practical challenges. To address this, we propose a novel controller design method based on variant-parameter double integral multi-layer neural dynamics (VP-DIMND) for quadrotors. First, the design process of the quadrotor’s position and attitude controller based on the VP-DIMND method is presented. Second, theoretical analysis demonstrates that the VP-DIMND controller ensures rapid convergence and robust performance against various noise conditions, including bounded and unbounded noises. This is achieved through the use of variant-parameter multi-layer zeroing neural dynamics and double integral design mode. Finally, simulation experiments show that the VP-DIMND controller effectively enables the quadrotor track the given time-varying tasks in various noisy environments. Moreover, compared with similar methods, the proposed VP-DIMND method improves the convergence speed by about 40%, and the tracking results under the VP-DIMND controller outperforms the existing controllers, including convergence and robustness. These results also highlight the potential of the VP-DIMND controller for enhancing the stability and reliability of quadrotor in noisy environments.