H
Hanwen Zhang
Researcher at University of Pennsylvania
Publications - 7
Citations - 155
Hanwen Zhang is an academic researcher from University of Pennsylvania. The author has contributed to research in topics: Computer science & Optimization problem. The author has an hindex of 3, co-authored 4 publications receiving 50 citations.
Papers
More filters
Posted Content
Learning Control Barrier Functions from Expert Demonstrations
Alexander Robey,Haimin Hu,Lars Lindemann,Hanwen Zhang,Dimos V. Dimarogonas,Stephen Tu,Nikolai Matni +6 more
TL;DR: These are the first results that learn provably safe control barrier functions from data, agnostic to the parameterization used to represent the CBF, assuming only that the Lipschitz constant of such functions can be efficiently bounded.
Proceedings ArticleDOI
Learning Control Barrier Functions from Expert Demonstrations
Alexander Robey,Haimin Hu,Lars Lindemann,Hanwen Zhang,Dimos V. Dimarogonas,Stephen Tu,Nikolai Matni +6 more
TL;DR: In this article, a learning-based approach to safe controller synthesis based on control barrier functions (CBFs) is proposed, which is agnostic to the parameterization used to represent the CBF.
Posted Content
Learning Hybrid Control Barrier Functions from Data
Lars Lindemann,Haimin Hu,Alexander Robey,Hanwen Zhang,Dimos V. Dimarogonas,Stephen Tu,Nikolai Matni +6 more
TL;DR: An optimization-based framework for learning certifiably safe control laws from data is presented and sufficient conditions on the data are identified such that feasibility of the optimization problem ensures correctness of the learned hybrid control barrier functions, and hence the safety of the system.
Learning Hybrid Control Barrier Functions from Data
Lars Lindemann,Haimin Hu,Alexander Robey,Hanwen Zhang,Dimos V. Dimarogonas,Stephen Tu,Nikolai Matni +6 more
TL;DR: In this article, an optimization-based framework for learning certifiably safe control laws from data is proposed, in which the system dynamics are known and data exhibiting safe system behavior is available.
Journal ArticleDOI
State-Following-Kernel-Based Online Reinforcement Learning Guidance Law Against Maneuvering Target
TL;DR: In this paper , a state-following-kernel-based reinforcement learning method with an extended disturbance observer is proposed, whose application to a missile-target interception system is considered, and the target maneuver is then estimated by an observer in real time, which leads to an infinite-horizon optimal regulation problem.