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

@article{Zhu2020TrajectoryOF,
  title={Trajectory Optimization for Nonlinear Multi-Agent Systems using Decentralized Learning Model Predictive Control *},
  author={Edward L. Zhu and Yvonne R. St{\"u}rz and Ugo Rosolia and Francesco Borrelli},
  journal={2020 59th IEEE Conference on Decision and Control (CDC)},
  year={2020},
  pages={6198-6203}
}
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… 

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