activity
20242026
collaborators

8 papers

math.OC2026

Anytime Plug-and-Play Control with Contract-Based Distributed MPC

Sabrina Bodmer, Danilo Saccani, Melanie N. Zeilinger +1

A central challenge in many mobile multi-robot applications is that communication topologies are inherently time-varying. Agents may enter or exit the network and such changes cann…

cs.RO2026

Robust Adaptive Predictive Control for Hook-Based Aerial Transportation Between Moving Platforms

Péter Antal, Andrea Carron, Melanie Zeilinger +2

This paper presents a novel model predictive control (MPC) approach for autonomous pick-and-place between moving platforms with a hook-equipped aerial manipulator. First, for accur…

eess.SY2026

Bridging RL and MPC for mixed-integer optimal control with application to Formula 1 race strategies

Joschua Wüthrich, Romir Damle, Giona Fieni +3

We propose a hybrid reinforcement learning (RL) and model predictive control (MPC) framework for mixed-integer optimal control, where discrete variables enter the cost and dynamics…

eess.SY2026

Distributed Predictive Control Barrier Functions: Towards Scalable Safety Certification in Modular Multi-Agent Systems

Jonas Ohnemus, Alexandre Didier, Ahmed Aboudonia +2

We consider safety-critical multi-agent systems with distributed control architectures and potentially varying network topologies. While learning-based distributed control enables…

cs.RO2026

An MPC framework for efficient navigation of mobile robots in cluttered environments

Johannes Köhler, Daniel Zhang, Raffaele Soloperto +2

We present a model predictive control (MPC) framework for efficient navigation of mobile robots in cluttered environments. The proposed approach integrates a finite-segment shortes…

eess.SY2026

Real-Time Online Learning for Model Predictive Control using a Spatio-Temporal Gaussian Process Approximation

Lars Bartels, Amon Lahr, Andrea Carron +1

Learning-based model predictive control (MPC) can enhance control performance by correcting for model inaccuracies, enabling more precise state trajectory predictions than traditio…