arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2001.08088math.OCcs.LGcs.SYeess.SYstat.ML

Training Neural Network Controllers Using Control Barrier Functions in the Presence of Disturbances

Arizona State University(亚利桑那州立大学);University of Colorado Boulder(科罗拉多大学博尔德分校);CIDSE(CIDSE(可译为“科罗拉多独立研究与教育中心”或保留原缩写))

Shakiba Yaghoubi, Georgios Fainekos, Sriram Sankaranarayanan

英文摘要:

Control Barrier Functions (CBF) have been recently utilized in the design of provably safe feedback control laws for nonlinear systems. These feedback control methods typically compute the next control input by solving an online Quadratic Program (QP). Solving QP in real-time can be a computationally expensive process for resource constraint systems. In this work, we propose to use imitation learning to learn Neural Network-based feedback controllers which will satisfy the CBF constraints. In the process, we also develop a new class of High Order CBF for systems under external disturbances. We demonstrate the framework on a unicycle model subject to external disturbances, e.g., wind or currents.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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