Robust Decentralised Control for Modular Aerial Parcel Delivery Using Persistently Excited Physics-Informed Neural Networks

This paper presents a robust decentralised control approach for modular aerial parcel delivery using persistently excited physics-informed neural networks (PE-PINNs). The proposed method enables each propeller module to independently generate control efforts based solely on its local state information and that of its 1-hop neighbors, without requiring global system knowledge. The PE-PINN is trained to approximate the optimal centralized control policy by incorporating the nominal system dynamics and accounting for modeling uncertainties. Key innovations include estimating the Lipschitz constant to ensure persistent excitation during training, and a decentralised control formulation that minimizes the difference between the learned and optimal control efforts. Experimental results on a modular aerial testbed demonstrate the PE-PINN's ability to achieve high-accuracy fixed-point hover and trajectory tracking performance, outperforming a prior decentralised control approach by 8.57% and 24.17% respectively. The proposed framework enables scalable and robust control of modular aerial systems for parcel delivery applications.