CPS Events
Automatic Control in the Era of Artificial Intelligence
Abstract
In an era when Artificial Intelligence is seen as a universal solution, this presentation critically examines its role in automatic control, focusing on optimal control techniques. It traces the evolution of optimal control from traditional, model-based approaches to modern, data-driven methods powered by AI. The talk highlights how theoretical foundations have been linked with computational capabilities, a relationship that has evolved over time. It discusses scenarios where AI-driven approaches can outperform classical methods and examines cases where the AI hype overshadows practical benefits. The presentation further explores applications in self-driving cars, advanced robotics, and energy-efficient systems. Looking forward, it identifies future directions including the design of learning control architectures that integrate predictive capabilities at every level, enabling systems to autonomously refine performance through learning and interaction with their environment.
Bio
Francesco Borrelli received his ‘Laurea' degree from the University of Naples Federico II, Italy, and his PhD from the Automatic Control Laboratory at ETH Zurich in 2002. He is currently a Professor at the Dept. of Mechanical Engr. at the University of California, Berkeley, USA, where he conducts research in the field of predictive control. Prof. Borrelli has authored over 200 publications in the field of predictive control and is the author of the book Predictive Control. He has received several awards for his contributions to the predictive control field, including the 2009 NSF CAREER Award and the 2012 IEEE Control System Technology Award, and was elected IEEE Fellow in 2016. In 2017, he was awarded the Industrial Achievement Award by the International Federation of Automatic Control (IFAC) Council. Prof. Borrelli has been a consultant to major corporations since 2004 and pioneers predictive control in self-driving vehicles, solar plants, and energy-efficient buildings. He founded BrightBox, co-directed Hyundai’s Berkeley center, and launched WideSense.
Applying Digital Twin Technology to Rotating Mechanical Systems and Space Applications
Abstract
Digital twin (DT) technology is transforming industries by creating digital replicas of physical entities, yet its full potential is hindered by the complexity of modeling and integrating diverse data types. The research focuses on enhancing DT applications through the integration of verification and validation (V&V) with Weighted Flow Matching (WFM). It examines how V&V improves data reliability in rotating mechanical systems and assesses WFM’s role in refining data-driven generative modeling. A meta-learning-based reweighting algorithm (LRW) is introduced to dynamically assign weights to training samples based on their gradient directions, thereby optimizing model robustness against noisy or biased data. Experiments involving rotating machinery and space applications demonstrate improved predictive accuracy, reliable fault diagnosis, and effective system analysis. This integrated approach strengthens DT technology by ensuring more precise system analysis, enhanced model predictive control, and robustness in complex scenarios.
Bio
Yasar Yanik is a Postdoctoral Researcher in the Dept. of Applied Mathematics at UCSC, where he leads research on Digital Twin Enabled Autonomous Control for On Orbit Spacecraft Servicing (SURI). His research spans rotating machinery, photovoltaic systems, and spacecraft subsystems, integrating data-driven modeling with physics-based simulations to enhance performance and reliability. He has collaborated with institutions such as Pantex, Sandia National Laboratories, and the Air Force Research Laboratory to advance cutting-edge engineering solutions. Yanik holds a Ph.D. in Mechanical Engineering from Texas Tech University, where he developed high-fidelity digital twin frameworks for predictive diagnostics and health management of energy and mechanical systems.
Design of Resilient, Engineered, Autonomous, and Multifunctional (DREAM) Structures
Abstract
This talk will discuss strategies for the design of a new generation of intelligent structures equipped with sensors and control devices that can react in real time during multiple hazards. We create these adaptive structures with human-like capabilities by using agent-based modeling, vibration control, and evolutionary game theory. We modified a patented neural dynamic model for seismic design optimization of diagrid buildings, rocking-steel braced frames, and related structural systems. We also investigate integrative design methods to simultaneously design the structure and control devices, optimal placement of sensors, and distribution of tasks during failure mechanisms. We study control methodologies for novel testing technologies such as real-time hybrid simulation that combines numerical and experimental substructures. This unique intersection allows for design studies looking at aerodynamic mitigation through the origami-inspired morphing building envelope, and biomimicry design applying aquatic life adaptations and patterns into urban planning and structural design.
Bio
Mariantonieta Gutierrez Soto, Ph.D., is an assistant professor of Engineering Design at Penn State. She also holds an affiliation in the Dept. of Architectural Engr. and the Dept. of Civil and Environmental Engr. at Penn State. She is the Director of the DREAM Structures Lab. Dr. Gutierrez Soto holds a B.S. in Civil Engr. from Lamar University, Beaumont, Texas, and a M.S. and Ph.D. degree in Civil Engr. with a focus on structures from the Ohio State University under the mentorship of Prof. Hojjat Adeli. She was the recipient of the 2023-2024 Faculty Engagement Award by Penn State’s Teaching and Learning with Technology Center. She also received the “Teacher Who Made a Difference” award in 2020 and the “Faculty Research Mentor of the Week” award in 2019 from the University of Kentucky. She received the Presidential Fellowship in 2016.
From Coordination to Collaboration in Multi-Robot Systems: Lessons from Ecology
Abstract
A standard approach to multi-robot systems is to divide the team-level tasks into suitable building blocks and have the robots solve their respective subtasks in a coordinated manner. However, by bringing together robots of different types, it should be possible to arrive at completely new capabilities and skill-sets. In other words, the whole could become greater than the sum of its parts. Inspired by the ecological concept of a mutualism, i.e. the interaction between two or more species that benefit everyone involved, this idea is formalized through the composition of barrier functions for encoding collaborative arrangements in terms of expansions and contractions of relevant sets. Contextualized in a long-duration setting for robots deployed over long time scales, where optimality has to take a backseat to “survivability”, example scenarios include robotic environmental monitoring, safe learning, and remote access in the Robotarium, which is a multi-robot lab that has been in (almost) continuous operation for over five years.
Bio
Dr. Magnus Egerstedt is the Dean of Engineering and a Professor in the Department of Electrical Engineering and Computer Science at the University of California, Irvine. Prior to joining UCI, Egerstedt was on the faculty at the Georgia Institute of Technology, serving as the Chair in the School of Electrical and Computer Engineering and the Director for Georgia Tech's Institute for Robotics and Intelligent Machines. He received the M.S. degree in Engineering Physics and the Ph.D. degree in Applied Mathematics from the Royal Institute of Technology, Stockholm, Sweden, the B.A. degree in Philosophy from Stockholm University, and was a Postdoctoral Scholar at Harvard University. Dr. Egerstedt conducts research in the areas of control theory and robotics, with particular focus on control and coordination of multi-robot systems. Magnus Egerstedt is a Fellow of IEEE and IFAC, a member of the Royal Swedish Academy of Engineering Science, and served as President of the IEEE Control Systems Society. He has received a number of teaching and research awards, including the Ragazzini Award, the O. Hugo Schuck Best Paper Award, and the Alumni of the Year Award from the Royal Institute of Technology.
Embodied Intelligence in Bionic Limbs
Abstract
Recent developments in the design of bionic limbs increasingly emphasize the importance of human-centered principles in creating intelligent systems that can operate seamlessly within complex, dynamic environments and interact naturally with users. This talk explores the challenges of achieving embodied intelligence in lower-limb prostheses, and presents results in key methodologies, including compliant actuator design and AI-based control architectures. These innovations not only have the potential to enhance the functionality of the prosthesis but also to significantly improve the intuitiveness of the user experience and, ultimately, to improve their quality of life.
Bio
Raffaella Carloni is an Associate Professor in the Department of Artificial Intelligence, Faculty of Science and Engineering, University of Groningen, The Netherlands, where she directs the Robotics Laboratory and serves as the chair of the Human-centered Robotics group. She received the B.Sc./M.Sc. degrees in Electronic Engineering from the University of Bologna, Italy, in 2002, and the Ph.D. degree from the Department of Electronics, Computer Science and Systems, University of Bologna in 2007. She was Assistant/Associate Professor at the University of Twente, Enschede, The Netherlands, from 2008 to 2017.
