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arXiv · 2004.01298

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

Edward L. Zhu·Yvonne R. Stürz·Ugo Rosolia·Francesco Borrelli
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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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Edward L. Zhu, Yvonne R. Stürz, Ugo Rosolia, Francesco Borrelli. 2020-04-02. Trajectory Optimization for Nonlinear Multi-Agent Systems using Decentralized Learning Model Predictive Control. https://arxiv.org/abs/2004.01298

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