Research

Research Overview:

Research Goal:

Our goal is to improve the flexibility, safety, and efficiency of complex, dynamic, and connected systems. We focus our research in three core areas:

1. 1. Complex, dynamic systems
   2. Control and automation
   3. Artificial intelligence

 

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Complex, Dynamic Systems

A system is a group of individual components that drive global behavior through interactions and decision making. The system’s behavior is driven by these individual components, with changes in individual objectives and interactions leading to varying outcomes. Systems theory account for various different systems found in both nature and in engineered systems.

In our lab, the application area is primarily in industrial manufacturing and production systems. The goal of this area of research is to model, analyze, and control complex, dynamic manufacturing systems. To achieve this objective, we need to develop methods to gather data from various sources in the manufacturing environment (e.g., shop floor, product design process, customer feedback); provide context to this data by integrating physics-based models, artificial intelligence techniques, subject matter expertise, etc.; analyze this data; and build/test algorithms to control and coordinate the different components in the manufacturing system.

To learn more about some of this research, please take a look at some of the following representative publications:

1. 1. Kovalenko, I., Tilbury, D., and Barton, K., “The Model-Based Product Agent: A Control Oriented Architecture for Intelligent Products in Multi-Agent Manufacturing Systems,” Control Engineering Practice. 10.1016/j.conengprac.2019.03.009
   2. Moyne, J., Qamsane, Y., Balta, E., Kovalenko, I., Faris, J., Tilbury, D., and Barton, K., “A Requirements Driven Digital Twin Framework: Specification and Opportunities,” IEEE Access. 10.1109/ACCESS.2020.3000437
   3. Ocker, F., Kovalenko, I., Tilbury, D., Barton, K., and Vogel-Heuser, B., “A Framework for Automatic Initialization of Multi-Agent Production Systems Using Semantic Web Technologies,” IEEE Robotics and Automation Letters. 10.1109/LRA.2019.2931825
   4. Kovalenko, I., Saez, M., Barton, K., and Tilbury, D., “SMART: A System-level Manufacturing and Automation Research Testbed,” Smart and Sustainable Manufacturing Systems. 2017.10.1520/SSMS20170006
   5. Qamsane, Y., Moyne, J., Toothman, M., Kovalenko, I., Balta, E., Faris, J., Tilbury, D., and Barton, K., “A Methodology to Develop and Implement Digital Twin Solutions for Manufacturing Systems.” IEEE Access. 10.1109/ACCESS.2021.3065971

 

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Control and Automation

Control theory is often used to help ensure the safety, reliability, and efficiency of various types of systems. Control systems are used everywhere you look, from the thermostat in the buildings to the busses that we take to work!

In our lab, we focus on developing control theory for large scale systems, with a number of intelligent components (or sub-systems). This high-level view leads us to use and develop models that leverage both continuous dynamics and discrete dynamics. Using this information, we look to design and test various optimization and control algorithms that would allow our systems to perform based on our specifications.

To learn more about some of this research, please take a look at some of the following representative publications:

1. 1. Balta, E.*, Kovalenko, I.*, Spiegel, I.*, Tilbury, D., and Barton, K., “Model Predictive Control of Priced Timed Automata Encoded With First-Order Logic,” IEEE Transactions on Control Systems Technology, Vol. 30, No. 1, pp. 352-359, Jan. 2022, doi: 10.1109/TCST.2021.3054800. *The authors have equal contributions to this work.
   2. Kovalenko, I., Balta, E., Tilbury, D., and Barton, K., “Cooperative Product Agents to Improve Manufacturing System Flexibility: A Model-Based Decision Framework,” TechRxiv. Preprint. 10.36227/techrxiv.17136680.v1 
   3. Kovalenko, I., Tilbury, D., and Barton, K., “The Model-Based Product Agent: A Control Oriented Architecture for Intelligent Products in Multi-Agent Manufacturing Systems,” Control Engineering Practice. 10.1016/j.conengprac.2019.03.009
   4. Kovalenko, I., Tilbury, D., and Barton, K., “Priced timed automata models for control of intelligent product agents in manufacturing systems,” IFAC-PapersOnLine. 10.1016/j.ifacol.2021.04.051
   5. Spiegel, I., Kovalenko, I., Hoelzle, D., Sammons, P., and Barton, K., “Hybrid modeling and identification of jetting dynamics in electrohydrodynamic jet printing,” in 2017 IEEE Conference on Control Technology and Applications (CCTA). 10.1109/CCTA.2017.8062543

 

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Artificial Intelligence

The field of artificial intelligence encompasses a number of subfields, from machine learning to robotics to multi-agent systems. Recently, there has been a lot of important advances and applications of artificial intelligence in various fields.

In our lab, the research in the area of artificial intelligence is primarily focused on developing distributed intelligence methods for cyber-physical systems. Specifically, we focus on developing control and coordination techniques that will allow a group of intelligent software components (agents) to obtain and analyze information from a dynamic environment; coordinate with other agents through standardized communication channels; and make decisions based on information obtained from individual sensing capabilities and information from other agents. These techniques are tested in different cyber-physical systems where robots, people, and other machines physically interact to drive system behavior.

To learn more about some of this research, please take a look at some of the following representative publications:

1. Kovalenko, I., Tilbury, D., and Barton, K., “The Model-Based Product Agent: A Control Oriented Architecture for Intelligent Products in Multi-Agent Manufacturing Systems,” Control Engineering Practice. 10.1016/j.conengprac.2019.03.009
2. Kovalenko, I., Balta, E., Tilbury, D., and Barton, K., “Cooperative Product Agents to Improve Manufacturing System Flexibility: A Model-Based Decision Framework,” TechRxiv. Preprint. 10.36227/techrxiv.17136680.v1 
3. Kovalenko, I., Ryashentseva, D., Vogel-Heuser, B., Tilbury, D., and Barton, K., “Dynamic Resource Task Negotiation to Enable Product Agent Exploration in Multi-Agent Manufacturing Systems,” IEEE Robotics and Automation Letters. 10.1109/LRA.2019.2921947
4. Ocker, F., Kovalenko, I., Tilbury, D., Barton, K., and Vogel-Heuser, B., “A Framework for Automatic Initialization of Multi-Agent Production Systems Using Semantic Web Technologies,” IEEE Robotics and Automation Letters.  10.1109/LRA.2019.2931825
5. Zheng, G., Kovalenko, I., Barton, K., and Tilbury, D., “A table-top demonstration of intelligent products and human operators for agent-based manufacturing systems,” Procedia Manufacturing. 10.1016/j.promfg.2018.10.053