@article{Xiong2026, 
author = {Diwen Xiong and Liuyi Wen and Mingsheng Shang},
title = {A Neural-Dynamics-Based Control Scheme for Double-Arm Mobile Robots with Dynamic Neural Network},
year = {2026},
journal = {Tsinghua Science and Technology},
volume = {31},
number = {3},
pages = {1635-1651},
keywords = {double-arm mobile robot (DMR), kinematic control, physically constrained velocity collaborative control (PCVCC), dynamic neural network (DNN)},
url = {https://www.sciopen.com/article/10.26599/TST.2024.9010194},
doi = {10.26599/TST.2024.9010194},
abstract = {A double-arm mobile robot (DMR) is an advanced robotic system that integrates a mobile platform and double robotic arms. To address the control issue of the DMR, this paper provides its mathematical model and deduces kinematic equations as preliminaries. On this basis, a physically constrained velocity collaborative control (PCVCC) scheme is proposed for kinematic control of the DMR, which adjusts the mobile platform and robotic arms cooperatively using a designed optimization criterion. To explore the optimal solution of the PCVCC scheme, a gradient descent method assisted by velocity compensation is exploited to design a dynamic neural network (DNN) solver. Subsequently, theoretical analyses confirm the global convergence of the DNN solver. Finally, simulations, experiments, and comparisons demonstrate the feasibility and superiority of the proposed method.}
}