Session 1402: Advanced Battery Management Systems: modeling and numerical simulation for control

  • Date: Wednesday June 1, 9:30-11:00
  • Room #: Virtual

Abstract: The growing market of lithium-ion batteries in consumer electronics, automobiles, unmanned aerial vehicles, and power grid sector has stressed the need and relevance for a properly designed advanced Battery Management System (BMS) that can ensure the battery system’s reliability and performance.  The main objectives of this tutorial session are to 1) provide an exposure of the state-of-the-art in battery management system design, 2) discuss challenges and opportunities in numerical methods for multi-scale electrochemical models, 3) provide a comprehensive summary of modeling tools used for estimation in battery systems 4) present novel and robust methods for parameter identification in Lithium-ion and Lithium-metal batteries. Through this tutorial session, the audience will gain an understanding of the utility of these methods, and how they are used for BMS design in an integrated platform while delivering time and cost-savings in a risk-free virtual environment.

  1. “Advanced BMS modeling and numerical simulation for control: Introduction, Motivation, Challenges and Perspectives”, Simona Onori and Venkat Subramanian
  2. “Recent Progress on State and Parameter Estimation for Lithium-Sulfur Batteries”, Hosam Fathy
  3. “Nondestructive methods for estimating parameters of physics-based lithium-ion cell Models”, Gregory Plett
  4. “Multi-scale models for Lithium-Ion Batteries”, Taylor R. Garrick
  5. “FPGA-accelerated BMS Hardware-in-the-loop (HIL) Simulation Platform for Next Generation EVs”, Igor Alvarado

Session 0251: A Tutorial on Nonlinear Model Predictive Control: What Advances Are On the Horizon?

  • Date: Wednesday June 8, 10:00-11:30
  • Session #: WeA10
  • Room #: International C

Abstract: Model predictive control (MPC) is an optimization-based control strategy that has become popular due to its ability to handle complex systems that involve highly interactive and multivariable dynamics, nonlinearities, and constraints. Furthermore, due to its versatility in its ability to provide robustness guarantees (by design) as well as direct consideration of economically-oriented control objectives, MPC is increasingly being proposed/developed as a key enabling technology in emerging engineering applications such as biomedical systems, autonomous vehicles, energy systems and advanced manufacturing, to name a few. The past decade has witnessed significant advancements in the field of model predictive control (MPC) for nonlinear and uncertain systems. A variety of issues, such as closed-loop stability and performance, computational tractability, and feasibility/constraint satisfaction, have been tackled from both theoretical and practical perspectives. Nonetheless, MPC still has some important limitations that need to be overcome for its practical use in these emerging applications, which necessitates a paradigm shift from traditional approaches for MPC design.  The goal of this session is to provide a tutorial-like overview of the recent advances in MPC, with particular emphasis on key developments that have been made within the past five years, including 1. machine learning and 2. nonlinear and mixed integer optimization.

  1. “Advances in the fusion of learning-based methods and MPC”, Ali Mesbah, Kim P. Wabersich, Angela P. Schoellig, Melanie N. Zeilinger, Sergio Lucia, Thomas Badgwell, Joel A. Paulson
  2. “Distributed MPC with ALADIN”—A Tutorial – Boris Houska
  3. “Advances in mixed-integer MPC”, Robert D. McAllister and James B. Rawlings

Session 0528: Artificial Intelligence (AI) Technologies for Process Control, Monitoring, and Optimization

  • Date: Wednesday June 8, 14:00-15:30
  • Session #: WeB10
  • Room #: International C

Abstract: Artificial intelligence (AI), namely the study of intelligent agents for perceiving the environment and making decisions, is widely recognized as a field that can deeply reshape modern engineering technologies. In the context of automatic control, AI entails the utilization of data science and machine learning methods for a broad set of tasks: deriving surrogate models or data based system descriptions to complement or replace first-principles models in control algorithms, supervisory control, process and control performance monitoring, etc. AI and datascience- based methods have a long history in process automation and control, especially in system identification, adaptive control, and monitoring. More recently, the potential of using data to improve the performance and expand the applicability of established control technologies such as model predictive control has motivated considerable research activity. Industrial applications of AI are also rapidly expanding with a firm belief that AI will be a major enabler in the fourth industrial revolution currently underway. This tutorial session will provide tutorial reviews of methods and technologies involving AI in the field of process automation and control, and perspectives on the application of such technologies in industry.

  1. “Data-Driven Control: Overview and Perspectives”, Prodromos Daoutidis, Wentao Tang,
  2. “Artificial Intelligence in Fault Diagnosis, Supervisory Control, and Process Safety Analysis: Challenges and Opportunities”, Venkat Venkatasubramanian
  3. “Machine Learning in Industrial Advanced Process Control”, Heiko Claussen, Sven Serneels
  4. “Advancing industrial analytics using AI and mathematical optimization”, Sreekanth Rajagopalan

Session 1403: Sustainability and Industry 4.0

  • Date: Thursday June 9, 10:00-11:30
  • Session #: ThA10
  • Room #: International C

Abstract: The recent advancements within the framework of Industry 4.0 provide manufacturing an ever-increasing amount of data. Development in sensor technology, implementation of more advanced monitoring technologies and progression of the digital thread provides a much more detailed yet broader view of production processes, their supply chains, and life cycles. These innovations are without a doubt of great economic value to operations. But they also provide worthy insights for sustainability, which especially recently has rivaled digitalization as top priority for many companies. In this tutorial session, speakers present various approaches on how the growing information collected via Industry 4.0 initiatives can benefit sustainability strategies and be used to maximize both, economic and environmental benefits for manufacturing.

  1. “Sustainability and Industry 4.0: Obstacles and Opportunities”, Bhavik Bakshi and Joel Paulson
  2. “Science-Based Data Analytics for Molecular-to-Systems Engineering”, Alex Dowling
  3. “Systematic dimensionality reduction for optimization and control with many sustainability objectives”, Andrew Allman
  4. “Configurable Graph-based Modeling and Optimization Framework for Energy Systems”, Matthew Ellis

Session 0612: Managerial Decision Making as an Application for Control Science and Engineering

  • Date: Thursday June 9, 14:30-16:00
  • Session #: ThB10
  • Room #: International C

Abstract: The focus of this tutorial session for the 2022 American Control Conference is on the topic of control applications for managerial decision making. The intent is to highlight a challenging, fascinating, and important avenue for control scientists and engineers to exploit their expertise. The session will consist of a main paper and five short presentations. Time will be allocated for Q&A and discussion with the audience. This proposal discusses the motivation for the session, related work in different fields, and the composition of the session.

  1. “Managerial Decision Making as an Application for Control Science and Engineering,” T. Samad, D. Abramovitch, M. Lees, I. Mareels, R. Rhinehart, F. Cuzzola, O. Gusikhin, E. Juuso
  2. “Network Control System Applications for Manager Decision-Making,” M. Lees
  3. “Feedback Entropy—A Conceptual Framework for Management,” I. Mareels
  4. “Business Performance Management and Control Systems,” F. Cuzzola
  5. “Prescriptive Analytics and Control Towers: A New Dimension of Managerial Decision Making in the Age of Reinforcement and Machine Learning,” S. Pickl
  6. “Using Feedback Control Principles as Guiding Metaphors for Business Processes,” D. Abramovitch
  7. Discussion with the audience

Session 0385: Hybrid Modelling: Challenges and Opportunities

  • Date: Friday June 10, 10:00-11:30
  • Session #: FrA10
  • Room #: International C

Abstract: We have witnessed major theoretical and technological breakthroughs in image and video

processing and voice recognition in the last six years. These breakthroughs were enabled by petabytes of heterogeneous data (Big Data), enormous computing power, and major algorithmic advances in machine learning. Machine learning algorithms such as Deep Learning and Reinforcement Learning have been central to these breakthroughs. These advances have created an air of optimism among researchers and practitioners that similar successes can be replicated in the process industry. In this tutorial, we will introduce audience to several recent approaches to hybrid modelling using machine learning tools.

  1. “Building Hybrid AI Models in Process Systems Engineering: Challenges and Opportunities”, Venkat Venkarasubramanian
  2. “Universal hybrid modeling of aerobic carotenoid production using Saccharomyces Cerevisiae”, Joseph Kwon
  3. “Learning Sparse Nonlinear Models for Control”, Steven L. Brunton
  4. “Robust Unit Commitment Optimization under Volatile Wind Power Outputs Assisted by Clustering-based Data-Driven Techniques”, Ning Zhao and Fengqi You