2020/01/18 by Shakiba Yaghoubi, Yaghoubi, Shakiba, Georgios Fainekos +3
Engineering · #Advanced Control Systems Optimization #FOS: Computer and information sciences #FOS: Electrical engineering #FOS: Mathematics #Fault Detection and Control Systems #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Optimization and Control (math.OC) #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2001.08088
openalex publication_date 2020/01/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Control Barrier Functions (CBF) have been recently utilized in the design of\nprovably safe feedback control laws for nonlinear systems. These feedback\ncontrol methods typically compute the next control input by solving an online\nQuadratic Program (QP). Solving QP in real-time can be a computationally\nexpensive process for resource constraint systems. In this work, we propose to\nuse imitation learning to learn Neural Network-based feedback controllers which\nwill satisfy the CBF constraints. In the process, we also develop a new class\nof High Order CBF for systems under external disturbances. We demonstrate the\nframework on a unicycle model subject to external disturbances, e.g., wind or\ncurrents.\n