About

Research in the Control and Automation for Intelligent Systems Lab focuses on developing, analyzing, and applying control theory and artificial intelligence to improve the safety, flexibility, and performance of complex, dynamic systems, such as manufacturing systems.

Research topics

• Computer Science
• Engineering
• Software engineering
• Artificial Intelligence
• Manufacturing engineering
• Systems engineering
• Database
• World Wide Web

Selected publications

• A Digital Twin Framework for Computer Hardware Design and Assembly: A Risk-Prioritized Approach

  2025-06-23

  articleSenior author

  Abstract The rapid growth of artificial intelligence applications has intensified the demand for high-performance computing (HPC) servers, driving the need for more efficient and economical manufacturing processes. However, modern HPC servers present significant challenges: complex architectures vulnerable to assembly damage, compressed development cycles, strict cost constraints, and supply chain vulnerabilities across numerous subcomponents including printed circuit board assemblies (PCBAs) and processor modules. To address these challenges, we propose a Risk-Prioritized Digital Twin (RPDT) framework that enables concurrent optimization of design and assembly processes. The framework integrates three core components: failure modes and effects analysis (FMEA), visual kinematic simulation (VKS), and tolerance analysis. FMEA identifies high-risk components and processes. The combination of VKS and tolerance analysis enables early-stage risk detection through the superposition of static design tolerances and kinematic process parameters. We validate the RPDT framework through an industry case study of the system hardware development for a high-density HPC server. The results show that the RPDT framework has the potential to reduce physical build iterations, minimize material waste, and prevent costly soft-tooling and prototyping cycles.

  PublisherDOI

• Energy-Aware Model Predictive Control for Batch Manufacturing System Scheduling Under Different Electricity Pricing Strategies

  ArXiv.org · 2025-06-28

  preprintOpen accessSenior author

  Manufacturing industries are among the highest energy-consuming sectors, facing increasing pressure to reduce energy costs. This paper presents an energy-aware Model Predictive Control (MPC) framework to dynamically schedule manufacturing processes in response to time-varying electricity prices without compromising production goals or violating production constraints. A network-based manufacturing system model is developed to capture complex material flows, batch processing, and capacities of buffers and machines. The scheduling problem is formulated as a Mixed-Integer Quadratic Program (MIQP) that balances energy costs, buffer levels, and production requirements. A case study evaluates the proposed MPC framework under four industrial electricity pricing schemes. Numerical results demonstrate that the approach reduces energy usage expenses while satisfying production goals and adhering to production constraints. The findings highlight the importance of considering the detailed electricity cost structure in manufacturing scheduling decisions and provide practical insights for manufacturers when selecting among different electricity pricing strategies.

  PublisherOA PDFDOI

• A Model Predictive Control Framework to Enhance Safety and Quality in Mobile Additive Manufacturing Systems

  ArXiv.org · 2025-06-29

  preprintOpen accessSenior author

  In recent years, the demand for customized, on-demand production has grown in the manufacturing sector. Additive Manufacturing (AM) has emerged as a promising technology to enhance customization capabilities, enabling greater flexibility, reduced lead times, and more efficient material usage. However, traditional AM systems remain constrained by static setups and human worker dependencies, resulting in long lead times and limited scalability. Mobile robots can improve the flexibility of production systems by transporting products to designated locations in a dynamic environment. By integrating AM systems with mobile robots, manufacturers can optimize travel time for preparatory tasks and distributed printing operations. Mobile AM robots have been deployed for on-site production of large-scale structures, but often neglect critical print quality metrics like surface roughness. Additionally, these systems do not have the precision necessary for producing small, intricate components. We propose a model predictive control framework for a mobile AM platform that ensures safe navigation on the plant floor while maintaining high print quality in a dynamic environment. Three case studies are used to test the feasibility and reliability of the proposed systems.

  PublisherOA PDFDOI

• Guest Editorial: Engineering and Operating Digital Twins for Automated Production or Construction Systems

  IEEE Transactions on Automation Science and Engineering · 2025-01-01

  editorial
  PublisherDOI

• Bi-level Model Predictive Control for Energy-aware Integrated Product Pricing and Production Scheduling

  IFAC-PapersOnLine · 2025-01-01

  articleOpen accessSenior authorCorresponding

  The manufacturing industry is under growing pressure to enhance sustainability while preserving economic competitiveness. As a result, manufacturers have been trying to determine how to integrate onsite renewable energy and real-time electricity pricing into manufacturing schedules without compromising profitability. To address this challenge, we propose a bi-level model predictive control framework that jointly optimizes product prices and production scheduling with explicit consideration of renewable energy integration. The higher level determines the product price to maximize revenue and renewable energy usage. The lower level controls production scheduling in runtime to minimize operational costs and respond to the product demand. Market response is incorporated through price elasticity, enabling strategic pricing to align the product demand with the availability of renewable energy. Results from a lithium-ion battery pack manufacturing system case study demonstrate that our approach enables manufacturers to reduce grid energy costs while increasing profit.

  PublisherDOI

• A Model Predictive Control Framework to Enhance Safety and Quality in Mobile Additive Manufacturing Systems

  2025-08-17

  articleSenior author

  In recent years, the demand for customized, on-demand production has grown in the manufacturing sector. Additive Manufacturing (AM) has emerged as a promising technology to enhance customization capabilities, enabling greater flexibility, reduced lead times, and more efficient material usage. However, traditional AM systems remain constrained by static setups and human worker dependencies, resulting in long lead times and limited scalability. Mobile robots can improve the flexibility of production systems by transporting products to designated locations in a dynamic environment. By integrating AM systems with mobile robots, manufacturers can optimize travel time for preparatory tasks and distributed printing operations. Mobile AM robots have been deployed for on-site production of large-scale structures, but often neglect critical print quality metrics like surface roughness. Additionally, these systems do not have the precision necessary for producing small, intricate components. We propose a model predictive control framework for a mobile AM platform that ensures safe navigation on the plant floor while maintaining high print quality in a dynamic environment. Three case studies are used to test the feasibility and reliability of the proposed systems.

  PublisherDOI

• A Large Language Model-Enabled Control Architecture for Dynamic Resource Capability Exploration in Multi-Agent Manufacturing Systems

  2025-08-17 · 4 citations

  articleSenior author

  Manufacturing environments are becoming more complex and unpredictable due to factors such as demand variations and shorter product lifespans. This complexity requires real-time decision-making and adaptation to disruptions. Traditional control approaches highlight the need for advanced control strategies capable of overcoming unforeseen challenges, as they demonstrate limitations in responsiveness within dynamic industrial settings. Multi-agent systems address these challenges through decentralization of decision-making, enabling systems to respond dynamically to operational changes. However, current multi-agent systems encounter challenges related to real-time adaptation, context-aware decision-making, and the dynamic exploration of resource capabilities. Large language models provide the possibility to overcome these limitations through context-aware decision-making capabilities. This paper introduces a large language model-enabled control architecture for multi-agent manufacturing systems to dynamically explore resource capabilities in response to real-time disruptions. A simulation-based case study demonstrates that the proposed architecture improves system resilience and flexibility. The case study findings show improved throughput and efficient resource utilization compared to existing approaches.

  PublisherDOI

• A Large Language Model-Enabled Control Architecture for Dynamic Resource Capability Exploration in Multi-Agent Manufacturing Systems

  ArXiv.org · 2025-05-28

  preprintOpen accessSenior author

  Manufacturing environments are becoming more complex and unpredictable due to factors such as demand variations and shorter product lifespans. This complexity requires real-time decision-making and adaptation to disruptions. Traditional control approaches highlight the need for advanced control strategies capable of overcoming unforeseen challenges, as they demonstrate limitations in responsiveness within dynamic industrial settings. Multi-agent systems address these challenges through decentralization of decision-making, enabling systems to respond dynamically to operational changes. However, current multi-agent systems encounter challenges related to real-time adaptation, context-aware decision-making, and the dynamic exploration of resource capabilities. Large language models provide the possibility to overcome these limitations through context-aware decision-making capabilities. This paper introduces a large language model-enabled control architecture for multi-agent manufacturing systems to dynamically explore resource capabilities in response to real-time disruptions. A simulation-based case study demonstrates that the proposed architecture improves system resilience and flexibility. The case study findings show improved throughput and efficient resource utilization compared to existing approaches.

  PublisherOA PDFDOI

• Dynamic Task Adaptation for Multi-Robot Manufacturing Systems with Large Language Models

  ArXiv.org · 2025-05-28

  preprintOpen accessSenior author

  Recent manufacturing systems are increasingly adopting multi-robot collaboration to handle complex and dynamic environments. While multi-agent architectures support decentralized coordination among robot agents, they often face challenges in enabling real-time adaptability for unexpected disruptions without predefined rules. Recent advances in large language models offer new opportunities for context-aware decision-making to enable adaptive responses to unexpected changes. This paper presents an initial exploratory implementation of a large language model-enabled control framework for dynamic task reassignment in multi-robot manufacturing systems. A central controller agent leverages the large language model's ability to interpret structured robot configuration data and generate valid reassignments in response to robot failures. Experiments in a real-world setup demonstrate high task success rates in recovering from failures, highlighting the potential of this approach to improve adaptability in multi-robot manufacturing systems.

  PublisherOA PDFDOI

• Bi-level Model Predictive Control for Energy-aware Integrated Product Pricing and Production Scheduling

  ArXiv.org · 2025-07-18

  articleOpen accessSenior author

  The manufacturing industry is under growing pressure to enhance sustainability while preserving economic competitiveness. As a result, manufacturers have been trying to determine how to integrate onsite renewable energy and real-time electricity pricing into manufacturing schedules without compromising profitability. To address this challenge, we propose a bi-level model predictive control framework that jointly optimizes product prices and production scheduling with explicit consideration of renewable energy availability. The higher level determines the product price to maximize revenue and renewable energy usage. The lower level controls production scheduling in runtime to minimize operational costs and respond to the product demand. Price elasticity is incorporated to model market response, allowing the system to increase demand by lowering the product price during high renewable energy generation. Results from a lithium-ion battery pack manufacturing system case study demonstrate that our approach enables manufacturers to reduce grid energy costs while increasing profit.

  PublisherOA PDF

Frequent coauthors

• Kira Barton

  University of Michigan–Ann Arbor

  26 shared

• Dawn M. Tilbury

  University of Michigan–Ann Arbor

  23 shared

• Efe C. Balta

  14 shared

• Birgit Vogel‐Heuser

  Technical University of Munich

  10 shared

• Jong-Han Lim

  9 shared

• Jun Ueda

  5 shared

• Felix Ocker

  Honda (Germany)

  5 shared

• Mostafa Tavakkoli Anbarani

  Pennsylvania State University

  5 shared

Labs

• Control and Automation for Intelligent Systems LabPI

  Research in the Control and Automation for Intelligent Systems Lab focuses on developing, analyzing, and applying control theory and artificial intelligence to improve the safety, flexibility, and performance of complex, dynamic systems, such as manufacturing systems.

Education

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• M.S., Not provided in the HTML snippet

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• B.S., Not provided in the HTML snippet

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