all AI news
Automatic nonlinear MPC approximation with closed-loop guarantees
April 12, 2024, 4:43 a.m. | Abdullah Tokmak, Christian Fiedler, Melanie N. Zeilinger, Sebastian Trimpe, Johannes K\"ohler
cs.LG updates on arXiv.org arxiv.org
Abstract: Safety guarantees are vital in many control applications, such as robotics. Model predictive control (MPC) provides a constructive framework for controlling safety-critical systems, but is limited by its computational complexity. We address this problem by presenting a novel algorithm that automatically computes an explicit approximation to nonlinear MPC schemes while retaining closed-loop guarantees. Specifically, the problem can be reduced to a function approximation problem, which we then tackle by proposing ALKIA-X, the Adaptive and Localized …
abstract algorithm applications approximation arxiv complexity computational control cs.lg cs.sy eess.sy framework loop math.oc mpc novel predictive presenting robotics safety safety-critical systems type vital
More from arxiv.org / cs.LG updates on arXiv.org
Jobs in AI, ML, Big Data
Data Architect
@ University of Texas at Austin | Austin, TX
Data ETL Engineer
@ University of Texas at Austin | Austin, TX
Lead GNSS Data Scientist
@ Lurra Systems | Melbourne
Senior Machine Learning Engineer (MLOps)
@ Promaton | Remote, Europe
Data Engineer - AWS
@ 3Pillar Global | Costa Rica
Cost Controller/ Data Analyst - India
@ John Cockerill | Mumbai, India, India, India