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20172026
most citedConstraint-adaptive MPC for large-scale systems: Satisfying state constraints without imposing them

6 citations · 27 across the 53 of their papers we have counts for

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17 papers · 1 filter

math.OC2026

Scaled Graph Bounding Techniques for Reset Systems

Timo de Groot, Maurice Heemels, Tom Oomen +1

Reset systems can overcome fundamental limitations of linear time-invariant control. The recently introduced notion of scaled (relative) graphs provides a promising framework for d…

math.OC2026

Scaled Relative Graphs and Dynamic Integral Quadratic Constraints: Connections and Computations for Nonlinear Systems

Timo de Groot, Tom Oomen, W. P. M. H. Heemels +1

Scaled relative graphs (SRGs) enable graphical analysis and design of nonlinear systems. In this paper, we present a systematic approach for computing both soft and hard SRGs of no…

math.OC2026

Safe Feedback Optimization through Control Barrier Functions

Giannis Delimpaltadakis, Pol Mestres, Jorge Cortés +1

Feedback optimization refers to a class of methods that steer a control system to a steady state that solves an optimization problem. Despite tremendous progress on the topic, an i…

math.OC2025

Data-Driven Stabilization Using Prior Knowledge on Stabilizability and Controllability

Amir Shakouri, Henk J. van Waarde, Tren M. J. T. Baltussen +1

In this work, we study data-driven stabilization of linear time-invariant systems using prior knowledge of system-theoretic properties, specifically stabilizability and controllabi…

math.OC2025

Verification and Synthesis of Discrete-Time Control Barrier Functions

Erfan Shakhesi, W. P. M. H. Heemels, Alexander Katriniok

Discrete-time Control Barrier Functions (DTCBFs) have recently attracted interest for guaranteeing safety and synthesizing safe controllers for discrete-time dynamical systems. Thi…

math.OC2025

Value Function Approximation for Nonlinear MPC: Learning a Terminal Cost Function with a Descent Property

T. M. J. T. Baltussen, C. A. Orrico, A. Katriniok +2

We present a novel method to synthesize a terminal cost function for a nonlinear model predictive controller (MPC) through value function approximation using supervised learning. E…