Institute of Control Systems (IRS)

Cooperative Systems

Cooperative Control Systems


How will automation interact with humans in the future?

How to create synergies between humans and machines in the context of Industry 4.0?

The research group Cooperative Systems develops a framework for modeling and control of interactions between humans and machines. The individual strengths of human and machine are combined to achieve high performance systems, ready to meet future challenges of automatization. The fields of applications are e.g. Advanced Driver Assistance Systems, Robotics, Medical Technologies and Aerospace Engineering.

Cooperative Control Loop

Cooperative Control Loop

Modeling and Identification

The modeling of cooperative systems forms the basis of automation design for cooperative scenarios. In this context, uncertainties in perception and action need to be considered. Furthermore, semantics enable a strategic description of the interaction. Moreover, the identification of human behavior is essential in automation design.

Control Synthesis

Automation design in a cooperative scenario needs to be capable of a dynamic allocation of authority. Furthermore, it requires the ability to negotiate a common goal with the human. One approach to control cooperative systems is based on game theory and Model Predictive Control (MPC). In order to achieve real-time control, motion primitives are examined.



Motion Tracking is used in various scenarios to measure human motion and validate identification methods.

An Advanced Driving Simulator with haptic feedback human machine interfaces was developed at the IRS. It is used to validate cooperative control methods in the context of advanced driver assistance systems.

A newly developed Ball-on-Plate experiment will be used to apply cooperative identification and control methods in a highly dynamical scenario.


Jairo Inga Charaja

Head of Research Group

Research Interest:
Identification of Human Behavior in Cooperative Scenarios


Julian Ludwig

Research Associate

Research Interest:
Shifting Authority between Driver and Automation

Julian Schneider

Wissenschaftlicher Mitarbeiter


Florian Köpf

Research Associate

Research Interest:
Design of Cooperative Control Considering Uncertainties

Simon Rothfuß

Research Associate

Research Interest:
Modeling of Cooperative Goal Negotiation

Esther Bischoff

Research Associate

Research Interest:

Christian Braun

Research Associate

Research Interest:
Shared control of heterogenous robot swarms

Philipp Karg

Research Associate

Research Interest:
Modeling and Identification in Cooperative Human-Machine Scenarios

Balint Varga

Research Associate

Research Interest:
Cooperative control of mobile machinery

Student Assistants


Omar Abdulbaki

Entwicklung eines Simulators für autonome Fahrzeuge

Michael Meyling

Entwicklung eines Human-Machine-Interfaces für Multi-Roboter-Systeme

Recent job offers for student assistants can be found here.

Bachelor and Master Students

Leonie Damaske

Master Thesis

Entwicklung eines Planungsalgorithmus zur Berücksichtigung komplexer Aufgaben in Multi-Roboter-Systemen

Xin Ye

Master Thesis

Optimale dezentrale Belief-Space-Planung für die Mensch-Maschine-Interaktion in Multi-Roboter-Szenarien

Lars Erik Fischer

Bachelor Thesis

Aufbau und Untersuchung eines haptischen Force-Feedback-Interfaces mit Exoskeletten und einem Roboterarm

Maximilian Wörner

Master Thesis

Implementierung kooperativer Fahrerassistenz auf Basis der Spieltheorie

Simon Stoll

Master Thesis

Modellierung menschlicher Bewegungen mittels sensormotorischer Optimalregelung

Viola Findiku

Bachelor Zhesis

Automatisierte Parametrierung einer modellprädiktive Regelung für mobile Arbeitsmaschinen

Alejandro Léon

Bachelor Thesis

Umsetzung von Algorithmen zur Kartierung und Navigation mit einer autonomen Roboterplattform

Toshiaki Sebastian Tanaka

Master Thesis

Kooperative Methoden für das Autonome Fahren

Dominik Juric

Master Thesis

Reinforcement-Learning-basierte Identifikation in kooperativen Systemen

Ömer Ekin

Master Thesis

Identifikationsverfahren für nichtlineare, kooperative Systeme basierend auf Prinzipien des Reinforcement Learning

Jonas Teufel

Bachelor Thesis

Untersuchung bestehender Lösungsansätze zur Koordination heterogener Multi-Roboter-Teams

Elias Huber

Bachelor Thesis

Entwicklung und Umsetzung eines Anwendungsszenarios mit kooperierenden mobilen Roboterplattformen

Martin Hartmann

Bachelor Thesis

Aufbau und Analyse Virtueller Haptischer Human-Machine-Interfaces mit Exoskeletten und einem Roboterarm

Rinat Prezdnyakov

Master Thesis

Echtzeitfähige kooperative Regelung mit Intentionserkennung für die Mensch-Roboter-Interaktion

Cheng Guo

Master Thesis

Untersuchung der Mensch-Maschine-Kooperation zur Koordination heterogener Roboterteams