571 citations · 1.3k across the 123 of their papers we have counts for
19 papers · 1 filter
Inverse Optimal Control as an Errors-in-Variables Problem
Rahel Rickenbach, Anna Scampicchio, Melanie N. Zeilinger
Inverse optimal control (IOC) is about estimating an unknown objective of interest given its optimal control sequence. However, truly optimal demonstrations are often difficult to…
MHE under parametric uncertainty -- Robust state estimation without informative data
Simon Muntwiler, Johannes Köhler, Melanie N. Zeilinger
In this paper, we study joint state and parameter estimation for general nonlinear systems with uncertain parameters and persistent process and measurement noise. In particular, we…
Nonlinear Functional Estimation: Functional Detectability and Full Information Estimation
Simon Muntwiler, Johannes Köhler, Melanie N. Zeilinger
We consider the design of functional estimators, i.e., approaches to compute an estimate of a nonlinear function of the state of a general nonlinear dynamical system subject to pro…
Automatic nonlinear MPC approximation with closed-loop guarantees
Abdullah Tokmak, Christian Fiedler, Melanie N. Zeilinger +2
Safety guarantees are vital in many control applications, such as robotics. Model predictive control (MPC) provides a constructive framework for controlling safety-critical systems…
Efficient Zero-Order Robust Optimization for Real-Time Model Predictive Control with acados
Jonathan Frey, Yunfan Gao, Florian Messerer +3
Robust and stochastic optimal control problem (OCP) formulations allow a systematic treatment of uncertainty, but are typically associated with a high computational cost. The recen…
A Stiffness-Oriented Model Order Reduction Method for Low-Inertia Power Systems
Simon Muntwiler, Ognjen Stanojev, Andrea Zanelli +2
This paper presents a novel model order reduction technique tailored for power systems with a large share of inverter-based energy resources. Such systems exhibit an increased leve…
Robust Nonlinear Reduced-Order Model Predictive Control
John Irvin Alora, Luis A. Pabon, Johannes Köhler +5
Real-world systems are often characterized by high-dimensional nonlinear dynamics, making them challenging to control in real time. While reduced-order models (ROMs) are frequently…
Homothetic tube model predictive control with multi-step predictors
Danilo Saccani, Giancarlo Ferrari-Trecate, Melanie N. Zeilinger +1
We present a robust model predictive control (MPC) framework for linear systems facing bounded parametric uncertainty and bounded disturbances. Our approach deviates from standard…
Submodular Reinforcement Learning
Manish Prajapat, Mojmír Mutný, Melanie N. Zeilinger +1
In reinforcement learning (RL), rewards of states are typically considered additive, and following the Markov assumption, they are of states visited previous…
Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems
Truong X. Nghiem, Ján Drgoňa, Colin Jones +10
Physics-informed machine learning (PIML) is a set of methods and tools that systematically integrate machine learning (ML) algorithms with physical constraints and abstract mathema…
Time Dependent Inverse Optimal Control using Trigonometric Basis Functions
Rahel Rickenbach, Elena Arcari, Melanie N. Zeilinger
The choice of objective is critical for the performance of an optimal controller. When control requirements vary during operation, e.g. due to changes in the environment with which…
Model Predictive Control for Multi-Agent Systems under Limited Communication and Time-Varying Network Topology
Danilo Saccani, Lorenzo Fagiano, Melanie N. Zeilinger +1
In control system networks, reconfiguration of the controller when agents are leaving or joining the network is still an open challenge, in particular when operation constraints th…
Approximate non-linear model predictive control with safety-augmented neural networks
Henrik Hose, Johannes Köhler, Melanie N. Zeilinger +1
Model predictive control (MPC) achieves stability and constraint satisfaction for general nonlinear systems, but requires computationally expensive online optimization. This paper…
Robust Optimal Control for Nonlinear Systems with Parametric Uncertainties via System Level Synthesis
Antoine P. Leeman, Jerome Sieber, Samir Bennani +1
This paper addresses the problem of optimally controlling nonlinear systems with norm-bounded disturbances and parametric uncertainties while robustly satisfying constraints. The p…
On stochastic MPC formulations with closed-loop guarantees: Analysis and a unifying framework
Johannes Köhler, Ferdinand Geuss, Melanie N. Zeilinger
We investigate model predictive control (MPC) formulations for linear systems subject to i.i.d. stochastic disturbances with bounded support and chance constraints. Existing stocha…
Error analysis of regularized trigonometric linear regression with unbounded sampling: a statistical learning viewpoint
Anna Scampicchio, Elena Arcari, Melanie N. Zeilinger
The effectiveness of non-parametric, kernel-based methods for function estimation comes at the price of high computational complexity, which hinders their applicability in adaptive…
Multi-agent Distributed Model Predictive Control with Connectivity Constraint
Andrea Carron, Danilo Saccani, Lorenzo Fagiano +1
In cooperative multi-agent robotic systems, coordination is necessary in order to complete a given task. Important examples include search and rescue, operations in hazardous envir…
Active Learning-based Model Predictive Coverage Control
Rahel Rickenbach, Johannes Köhler, Anna Scampicchio +2
The problem of coverage control, i.e., of coordinating multiple agents to optimally cover an area, arises in various applications. However, coverage applications face two major cha…
Robust Nonlinear Optimal Control via System Level Synthesis
Antoine P. Leeman, Johannes Köhler, Andrea Zanelli +2
This paper addresses the problem of finite horizon constrained robust optimal control for nonlinear systems subject to norm-bounded disturbances. To this end, the underlying uncert…