Research

The research in the lab focuses on development of control theoretic methods in the following areas:

• optimal and learning-based control for nonlinear systems,

• analysis of uncertain, dynamical systems using set-based methods,
• hierarchical, optimal and fault-tolerant control.

The research in the lab has a strong focus on controller development for the full cycle starting from system modelling to the end application. The particular topics also include advanced techniques of data analysis and classification along with model uncertainty assessment. Special attention is paid to complex and hierarchical systems, where consistency between control and subroutines for different system layers is of particular interest. Special attention is placed on

controlled environment agriculture, energy and fuel cell systems, and automatic train operation as application areas. Concrete control methods in turn include model predictive control, reinforcement learning, adaptive dynamic programming and other optimal control methods, with a special focus on learning aspects. Mathematical proofs and analysis of e.g. robustness properties of control methods in the presence of uncertainties is a part of virtually every new control method developed in the lab. We also try to bridge the gap between theory and computer implementation by explicitly dealing with computational uncertainty related to the imperfections of controller implementation in digital and analog devices. For this sake, the lab works on a variety of mathematical methods which explicitly incorporate the said uncertainty and subsequently aim at developing a basis of formally correct and automated controller extraction.

Selected projects can be found on our project overview.

Contact person: Dr.-Ing. Felix Krujatz.

A significant part of our research focuses on Controlled Environment Agriculture (CEA), circular food production, and bioprocesses for the bioeconomy.

Controlled Environment Agriculture (CEA) has emerged as a key pillar of modern agriculture, offering immense potential to revolutionize food production and address the challenges of sustainably feeding a growing global population. CEA involves the creation of optimal growth conditions for plants in closed, highly automated systems such as greenhouses, vertical farms, and indoor farming facilities. Within these environments, critical factors including temperature, light, humidity, and nutrient availability can be continuously monitored and precisely controlled.

The importance of CEA lies in its ability to overcome the inherent limitations of conventional agricultural practices, which are strongly influenced by unpredictable weather conditions and are highly susceptible to pests and diseases. By providing a controlled and optimized environment, CEA enables year-round cultivation, significantly reduces water consumption, eliminates the need for harmful pesticides, and maximizes crop yields. Furthermore, CEA supports regional and sustainable food production, reducing dependency on long transport routes and minimizing the carbon footprint associated with conventional agriculture. Beyond plant cultivation, we apply advanced control strategies to optimize the growth of larvae and fish, thereby enabling circular food production systems. Within the

CUBES Circle project, we investigate the use of plant cultivation byproducts—such as residues and organic waste—as feed for insect larvae. These larvae subsequently serve as a high-quality feed source for fish. The nutrient-rich wastewater generated in fish production is then reused as a nutrient solution for plant cultivation, effectively closing the loop. This closed-loop system minimizes waste, maximizes resource efficiency, and establishes a symbiotic relationship between the individual components of the production system.

To achieve optimal growth conditions for larvae and fish within such circular systems, feedback control strategies are implemented at each process stage. These systems enable continuous monitoring and regulation of key parameters, including water quality, temperature, oxygen concentration, and feeding regimes. Precise control of these variables ensures optimal conditions for growth, health, and nutrient uptake across plants, larvae, and fish. The integration of feedback control within circular food production systems thus enhances overall productivity, sustainability, and resource efficiency. The development and operation of such circular systems inherently require a deep understanding of the biological processes governing organism growth. Successful implementation and optimization depend on close collaboration among experts in plant sciences, aquaculture, biology, environmental sciences, and engineering. This interdisciplinary approach enables a comprehensive understanding of the complex physiological and ecological interactions within the system and supports the development of innovative solutions to enhance growth, optimize nutrient cycling, and maintain organism health and well-being. By fostering interdisciplinary collaboration, this research contributes to a holistic perspective on the interplay between biological processes, technological innovation, and sustainable practices—paving the way for transformative solutions in future food production. In our Lab we In close collaboration with biologists, our laboratory develops advanced

develop methodological approaches to address the challenges inherent in these experimental systems. Observer design techniques are employed to estimate unmeasured variables and to monitor organism growth and health. Machine learning algorithms are used to analyze large-scale system data, uncover hidden patterns, and support predictive modeling and data-driven decision-making. Through optimal control strategies, we enable efficient allocation of resources such as light, water, and nutrients, maximizing productivity while minimizing waste. In addition, hierarchical control concepts are implemented to manage complex interactions and dynamic dependencies between system components, ensuring coordinated and efficient operation.

Engaging engineers in the study of CEA provides a unique opportunity to actively contribute to sustainable agriculture. We invite students to explore innovative approaches for optimizing plant growth, developing efficient nutrient (re-)cycling strategies, and designing control systems that enhance the growth and well-being of plants, larvae, algae, and fish. This interdisciplinary research fosters a deep understanding of the interconnected nature of circular food production systems and equips students with the skills required to address the complex challenges of future food production.

Selected projects can be found on our project overview.

Contact person: Dr. Zühal Wagner.

Artificial intelligence (AI) has become a key technology for decision support in modern energy and automation systems. In industry and agricultural contexts, data from sensors, production, and the environment is becoming more complex. AI analyses this data to support forecasting, detect problems, and make automated decisions. AI-driven systems are able to adapt to changing energy demands and production conditions, resulting in higher efficiency, reduced energy consumption and greater resilience. Our research projects are focusing on how AI can enhance resource utilisation, system robustness, automated production processes and AI-driven decision support.

Across all projects, AI-driven decision support systems are regarded as a transformative technology and helps the development of next-generation energy and automation solutions that are sustainable, resilient and scalable.

Selected projects can be found on our project overview.

Contact person: Dr. Philipp Sauerteig.

Multiphysics systems bring together diverse physical processes – like heat, fluid flow, electricity, mechanics, chemistry, and biology – into a single, interconnected whole. Instead of treating each phenomenon in isolation, multi-physics thinking reveals how they influence one another, thus, enabling powerful symbiotic effects. From smart greenhouses that optimize climate and energy use to advanced renewable-energy grids and high-tech manufacturing, multi-physics systems form the invisible backbone of modern innovation. By understanding and modeling these interactions, we unlock solutions that are more efficient, more resilient, and far closer to how the real world actually works.
Controlling multiphysics systems is challenging because multiple physical processes interact in complex, nonlinear, and often unpredictable ways. Sensors, models, and controllers must handle fast-changing dynamics, cross-couplings between domains, and large amounts of data. Real-time coordination becomes difficult when thermal, mechanical, electrical, chemical, or biological effects influence each other simultaneously. Ensuring stability, efficiency, and robustness under these intertwined conditions is one of the core challenges in mastering multiphysics systems.

Selected projects can be found on our project overview.

Contact person: Michael Hauck.

In recent years, the ACSD lab has focused extensively on hydrogen and fuel cell research, aiming to address the challenges of efficient, sustainable, and reliable energy systems. But why is the control of fuel cell systems so important, and what role does our research play in advancing this field?
Fuel cells are a promising technology for clean energy conversion, with hydrogen as a key fuel. However, the efficiency, longevity, and safety of fuel cell systems depend heavily on precise control of their operating conditions. Without effective control strategies, issues such as fuel wastage, uneven temperature distribution, or system degradation can significantly impair performance. To overcome these challenges, our research combines advanced modeling, sensing, and control technologies to optimize efficiency and lifetime of fuel cell systems.

Modeling Dynamics of the Anode Loop

Our work begins with the modeling of the anode recirculation loop, with a particular focus on the dynamics of pressure and gas concentrations. By understanding how these values fluctuate under different operating conditions, we can predict and mitigate inefficiencies, ensuring stable operation even during transient loads or varying hydrogen supply conditions.

Development of Hydrogen Sensor and Soft Sensor Technologies

Real-time monitoring is crucial for maintaining fuel cell performance. Together with partners, we are developing innovative hydrogen sensor technologies to measure hydrogen concentration accurately and reliably. In parallel, we are working on hydrogen observers which are soft sensors, using mathematical models and real-time data to estimate key variables that are challenging to measure directly. These tools provide a deeper insight into system behavior, enabling more precise control and diagnostics. More information about the topic can be found in

this publication and in this publication.

Model Predictive Control for the Anode Purge Valve and Cooling Loop

Another major focus is the development of model predictive control (MPC) strategies for critical subsystems. For the anode purge valve, MPC ensures efficient removal of insert gases while minimizing hydrogen loss, improving overall system efficiency. Similarly, our MPC for the cooling loop maintains optimal operating temperatures and temperature gradient along the fuel cell stack, reducing thermal stresses and prolonging the system's lifespan. More information about the topic can be found in

this publication.

Optimal Energy Management for Efficiency and Longevity

To further enhance the performance of fuel cell systems, we are designing optimal energy management systems that balance energy flows across components. These systems maximize fuel cell efficiency and extend the overall service life of the system. By integrating predictive models with real-time control, we ensure that the system operates at its peak under all conditions.

Carbon Capturing & Utilization (CCU)

In addition to applications in fuel cells, control engineering methods are also applied to electrochemical processes in the field of Carbon Capture & Utilization (CCU). The objective is to capture carbon dioxide (CO₂) from ambient air or exhaust streams and convert it into a bound electrochemical form. In combination with hydrogen, a wide range of hydrogen-based derivatives and chemical value-added products can be synthesized for industrial, chemical, or biological applications. Exemplary application areas are listed here.

Selected projects can be found on our

project overview.

Contact person: Dr.-Ing. Patrick Schmidt.

A central area of application in which our professorship contributes its knowledge are various projects that pursue the goal of automated and efficient mobility in the railway sector. The associated projects are part of the Smart Rail Connectivity Campus (SRCC). Core components of the train projects are

• Modeling of train dynamics
• State estimation for predicting future track conditions
• Controller design for braking and acceleration
• Test planning and validation of models and controllers

Smart Rail Connectivity Campus (SRCC)

As part of the project, a globally visible research and development facility is to be established in Annaberg-Buchholz. The focus is not only on cooperation between Chemnitz University of Technology and the city of Annaberg-Buchholz, but also on expanding a network with partners in order to carry out research, development and innovation work. The aim of the project is digitized, networked, automated and sustainable, i.e. economically, ecologically and socially efficient mobility. Further information can be found on the website of the Smart Rail Connectivity Campus (SRCC).

Research train

The SRCC provides a research train (a converted VT 642 (Desiro) commuter train) for the implementation and validation of the algorithms designed by our team.