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

2 Apr 2020Edward L. ZhuYvonne R. StürzUgo RosoliaFrancesco Borrelli

We present a decentralized trajectory optimization scheme based on learning model predictive control for multi-agent systems with nonlinear decoupled dynamics under separable cost 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... (read more)

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