Trajectory Optimization for Nonlinear Multi-Agent Systems using Decentralized Learning Model Predictive Control
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.
Additional Information
© 2020 IEEE. This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 846421. This work was partially funded by the grant ONR-N00014-18-1-2833.Attached Files
Accepted Version - 2004.01298.pdf
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Additional details
- Eprint ID
- 107642
- Resolver ID
- CaltechAUTHORS:20210121-152557974
- 846421
- Marie Curie Fellowship
- N00014-18-1-2833
- Office of Naval Research (ONR)
- Created
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2021-01-22Created from EPrint's datestamp field
- Updated
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2021-11-16Created from EPrint's last_modified field