M
Melanie N. Zeilinger
Researcher at ETH Zurich
Publications - 161
Citations - 5584
Melanie N. Zeilinger is an academic researcher from ETH Zurich. The author has contributed to research in topics: Model predictive control & Computer science. The author has an hindex of 32, co-authored 130 publications receiving 3578 citations. Previous affiliations of Melanie N. Zeilinger include University of California, Berkeley & École Polytechnique Fédérale de Lausanne.
Papers
More filters
Journal ArticleDOI
Learning-Based Model Predictive Control: Toward Safe Learning in Control
TL;DR: This research presents a meta-modelling framework that automates the very labor-intensive and therefore time-heavy and expensive process of manually cataloging and cataloging individual neurons to provide real-time information about their levels of activity.
Journal ArticleDOI
Cautious Model Predictive Control Using Gaussian Process Regression
TL;DR: This work describes a principled way of formulating the chance-constrained MPC problem, which takes into account residual uncertainties provided by the GP model to enable cautious control and presents a model predictive control approach that integrates a nominal system with an additive nonlinear part of the dynamics modeled as a GP.
Journal ArticleDOI
A General Safety Framework for Learning-Based Control in Uncertain Robotic Systems
Jaime F. Fisac,Anayo K. Akametalu,Melanie N. Zeilinger,Shahab Kaynama,Jeremy H. Gillula,Claire J. Tomlin +5 more
TL;DR: A general safety framework based on Hamilton–Jacobi reachability methods that can work in conjunction with an arbitrary learning algorithm is proposed, which proves theoretical safety guarantees combining probabilistic and worst-case analysis and demonstrates the proposed framework experimentally on a quadrotor vehicle.
Proceedings ArticleDOI
Efficient interior point methods for multistage problems arising in receding horizon control
TL;DR: This work presents efficient interior point methods tailored to convex multistage problems, a problem class which most relevant MPC problems with linear dynamics can be cast in, and specifies important algorithmic details required for a high speed implementation with superior numerical stability.
Proceedings ArticleDOI
Reachability-based safe learning with Gaussian processes
Anayo K. Akametalu,Shahab Kaynama,Jaime F. Fisac,Melanie N. Zeilinger,Jeremy H. Gillula,Claire J. Tomlin +5 more
TL;DR: This work proposes a novel method that uses a principled approach to learn the system's unknown dynamics based on a Gaussian process model and iteratively approximates the maximal safe set and further incorporates safety into the reinforcement learning performance metric, allowing a better integration of safety and learning.