Trajectory Optimization for Nonlinear Multi-Agent Systems using Decentralized Learning Model Predictive Control
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Publication:6337961
arXiv2004.01298MaRDI QIDQ6337961
Author name not available (Why is that?)
Publication date: 2 April 2020
Abstract: We present a decentralized minimum-time trajectory optimization scheme based on learning model predictive control for multi-agent systems with nonlinear decoupled dynamics and coupled state constraints. By performing the same task iteratively, data from previous task executions is used to construct and improve local time-varying safe sets and an approximate value function. These are used in a decoupled MPC problem as terminal sets and terminal cost functions. Our framework results in a decentralized controller, which requires no communication between agents over each iteration of task execution, and guarantees persistent feasibility, finite-time closed-loop convergence, and non-decreasing performance of the global system over task iterations. Numerical experiments of a multi-vehicle collision avoidance scenario demonstrate the effectiveness of the proposed scheme.
Has companion code repository: https://github.com/zhu-edward/multi-agent-LMPC
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