2017/03/19 by Tam W. Nguyen, Nguyen, Tam, Takeshi Hatanaka +7
Computer Science · Engineering · Physics and Astronomy · #Advanced Memory and Neural Computing #Distributed #Distributed Control Multi-Agent Systems #FOS: Computer and information sciences #FOS: Electrical engineering #Micro and Nano Robotics #Multiagent Systems (cs.MA) #Parallel #Robotics (cs.RO) #Systems and Control (eess.SY) #and Cluster Computing (cs.DC) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1703.06416
openalex publication_date 2017/03/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
This paper focuses on a passivity-based distributed reference governor (RG) applied to a pre-stabilized mobile robotic network. The novelty of this paper lies in the method used to solve the RG problem, where a passivity-based distributed optimization scheme is proposed. In particular, the gradient descent method minimizes the global objective function while the dual ascent method maximizes the Hamiltonian. To make the agents converge to the agreed optimal solution, a proportional-integral consensus estimator is used. This paper proves the convergence of the state estimates of the RG to the optimal solution through passivity arguments, considering the physical system static. Then, the effectiveness of the scheme considering the dynamics of the physical system is demonstrated through simulations and experiments.