2020/04/02 by Edward L. Zhu, Yvonne R. Stürz, Zhu, Edward L. +5
Engineering · #Advanced Control Systems Design #Advanced Control Systems Optimization #FOS: Computer and information sciences #FOS: Electrical engineering #FOS: Mathematics #Fault Detection and Control Systems #Machine Learning (cs.LG) #Optimization and Control (math.OC) #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2004.01298
openalex publication_date 2020/04/02 · openalex created_date 2022/07/26 · openalex updated_date 2026/07/28
We present a decentralized minimum-time trajectory optimization scheme based\non learning model predictive control for multi-agent systems with nonlinear\ndecoupled dynamics and coupled state constraints. By performing the same task\niteratively, data from previous task executions is used to construct and\nimprove local time-varying safe sets and an approximate value function. These\nare used in a decoupled MPC problem as terminal sets and terminal cost\nfunctions. Our framework results in a decentralized controller, which requires\nno communication between agents over each iteration of task execution, and\nguarantees persistent feasibility, finite-time closed-loop convergence, and\nnon-decreasing performance of the global system over task iterations. Numerical\nexperiments of a multi-vehicle collision avoidance scenario demonstrate the\neffectiveness of the proposed scheme.\n