Trajectory Optimization for Nonlinear Multi-Agent Systems using Decentralized Learning Model Predictive Control

• URL: http://arxiv.org/abs/2004.01298v4
• Date: Fri, 18 Dec 2020 05:00:29 GMT
• Title: Trajectory Optimization for Nonlinear Multi-Agent Systems using Decentralized Learning Model Predictive Control
• Authors: Edward L. Zhu, Yvonne R. St\"urz, Ugo Rosolia, Francesco Borrelli
• Abstract summary: 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. 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.
• Score: 5.2647625557619815
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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