April 22, 2017

Tutorial Sessions

The tutorial sessions at 2018 American Control Conference (ACC) address the development and/or application of state-of-the-art control approaches & theory to real-world engineering applications.

We are pleased to offer the following 6 tutorial sessions at ACC 2018.


WeA22 Optimization and Machine Learning: Theory, Algorithms, and Applications

Time: 10:00 – 12:00, Wednesday, June 27, 2018

Organizer: Somayeh Sojoudi, Assistant Professor, Electrical Engineering & Computer Sciences, Mechanical Engineering, University of California – Berkeley

Co-organizer: Javad Lavaei, Assistant Professor, Industrial Engineering and Operations Research, University of California – Berkeley

Optimization theory plays a vital role in the design, analysis, control and operation of real-world systems. The development of efficient optimization techniques and numerical algorithms has been an active area of research for many decades. The goal is to design a robust and scalable method that is able to find a provably optimal or near-optimal solution in a reasonable amount of time. The area of convex optimization was developed many years ago to identify a small but amenable class of optimization problems. Convex optimization has found a wide range of applications across engineering and economics. Several areas in control theory, such as optimal control, distributed controller, system identification, robust control, state estimation, model predictive control and dynamic programming, have significantly benefited from optimization theory and algorithms. With the recent advances in computing and sensor technologies, optimization theory has also become the backbone of the emerging areas of data science and machine learning, where it supplies us the techniques to extract useful information from data.


Somayeh Sojoudi: Conic Optimization and Machine Learning

Javad Lavaei: Low-rank Optimization and Energy Systems

Cedric Josz: Convexification Techniques and Hierarchies

Richard Y. Zhang: Low-complexity Numerical Algorithms

Sam Coogan and Murat Arcak: Optimizing Signalized Traffic Flow Networks


WeB22 Modeling and Control of Social Human-Robot Interaction (sHRI) Systems: Problems and Challenges

Time: 13:30 – 15:30, Wednesday, June 27, 2018

Organizer: Yue Wang, Assistant Professor, Mechanical Engineering Department, Clemson University

Co-organizer: Fumin Zhang, Professor, School of Electrical and Computer Engineering, Georgia Institute of Technology

The goal for this invited tutorial session is to present an introduction to the modeling and control approaches for social human-robot interaction (sHRI) systems, the problems of interest, and challenges therein. HRI represents an important research area with rapidly growing interest among control practitioners. There are many emerging civilian and military applications where robots work together with humans in order to achieve improved performance and balanced human experience. The attention on new intelligent control methods for HRI is motivated by the benefit of synergizing human intelligence with cooperating robots to improve the joint human-robot team performance and reduce manpower and workload. In this tutorial session, we are in particular interested in the research of modeling and control approaches in social human robot interaction (sHRI), which is a recently emerged branch in HRI that has received relatively less attention in the control community than physical human robot interaction (pHRI). Potential applications of sHRI systems include surveillance, reconnaissance, and combat tasks, collaborative manufacturing, intelligent transportation, smart health and so on. A fundamental issue that arises in the devise of sHRI systems is designing quantitative models to capture social interactions and control algorithms that improve performance while maintaining human social experience. Such models and algorithms must account for different features of the robotic systems as well as human factors to foster a new framework for quantitative analysis, which is an interesting however challenging problem.


Yue Wang and Fumin Zhang: Integrating Social Aspects into the Intelligent Control of Human-Robot Interaction Systems

Tina Setter: Trust-Based Interactions in Human-Robot Teams

Changliu Liu and Masayoshi Tomizuka: Designing Robot Behavior Towards Safe and Efficient Human Robot Interactions

Mike Goodrich: Toward Human Interaction with Bio-Inspired Robot Swarms

Weihua Sheng: Human-Vehicle Collaborative Driving for Improved Transportation Safety


WeC22 Real-Time Optimization and Model Predictive Control for Aerospace and Automotive Applications

Time: 16:00 – 18:00, Wednesday, June 27, 2018

Organizer: Stefano Di Cairano, Mitsubishi Electric Research Laboratory

Co-organizer: Ilya Kolmanovsky, Professor, Department of Aerospace Engineering, University of Michigan

Real-time optimization and model predictive control have been receiving increased attention in automotive and aerospace domains for the past fifteen years. They have been successfully demonstrated in a number of experimental applications, and they have stimulated the developments in the field of model predictive control to address the needs of systems with faster dynamics and limited computing power. The tutorial session provides an overview of the current status and the ongoing research and development activities in the field of real-time optimization and model predictive control for automotive and aerospace systems. Contributors from both industry and academia will address the benefits of real-time optimization and model predictive control, discuss formulations and computational strategies that are effective in these applications, and provide an outlook on future development and research challenges that remain.


Stefano Di Cairano and Ilya Kolmanovsky: Real-Time Optimization and Model Predictive Control for Aerospace and Automotive Applications

Behcet Acikmese: A Tutorial on Real-time Convex Optimization Based Guidance and Control for Aerospace Applications

Rolf Findeisen: Model Predictive Control for Aircraft Load Alleviation: Opportunities and Challenges

Mike Huang: Toward Real-Time Automotive Model Predictive Control: A Perspective from a Diesel Air Path Control Development

Francesco Borrelli: Model Predictive Control in Automated Driving


ThA22 NSF EPCN CAREER Awardees: Emerging Research in Control Systems

Time: 10:00 – 12:00, Thursday, June 28, 2018

Chair: Radhakisan Baheti, National Science Foundation

Co-Chair: Anthony Kuh, National Science Foundation

Organizer: Radhakisan Baheti, U. S. National Science Foundation

The purpose of the session is to review recent accomplishments and emerging opportunities in control systems from the perspective of U.S. National Science Foundation (NSF) CAREER award recipients.  The CAREER program is the most prestigious award in support of early-career faculty who have potential to serve as academic role models in research and education and to lead advances in the mission of their department or organization.  The CAREER awardees from NSF Energy, Power, Control and Networks (EPCN) program will discuss the latest breakthrough research in their respective field and key research opportunities in systems and control. The session will include the following presentations:


Amir Ali Ahmadi: Structured Sum of Squares Polynomials in Optimization and Control

Philip N. Brown and Jason R. Marden: Studies on Mechanisms for Robust Social Influence

Ricardo G. Sanfelice: Hybrid Feedback Control: Modeling, Design, and Open Problems

Ketan Savla: Scalable Microscopic and Macroscopic Control of Traffic Systems

Sridevi Sarma: Fragility in Epileptic Networks

Radhakisan Baheti: NSF Programs in Control, Robotics, Smart-Grid, and Cyber-Physical Systems


ThB22 NSF EPCN CAREER Awardees: Emerging Research in Smart Grid

Time: 13:30 – 15:30, Thursday, June 28, 2018

Chair: Radhakisan Baheti, National Science Foundation

Co-Chair: Anthony Kuh, National Science Foundation

Organizer: Radhakisan Baheti, U. S. National Science Foundation

The CAREER awardees in this session will discuss their latest research results and their potential to impact the next generation of power grid. The session will include the following presentations followed by panel discussion:


Javad Lavaei: Efficient Computational Techniques for Power Systems

Eilyan Bitar: Decentralized Coordination of Distributed Energy Resources at Scale

Sairaj Dhople: Modeling, Analysis, and Control of Low-inertia Power Networks

Aranya Chakrabortty: Infusing Autonomy in Networked Microgrids through Optimization and Control

All the NSF EPCN CAREER awardees from sessions ThB22 and ThB22: Panel Discussion


FrA22 Closing the Loop in IoT-Enabled Manufacturing Systems: Challenges and Opportunities

Time: 10:00 – 12:00, Friday, June 29, 2018

Organizer: Kira Barton, Department of Mechanical Engineering, University of Michigan

Co-organizers: Felipe Lopez, Department of Mechanical Engineering, University of Michigan, Francisco Maturana, Rockwell Automation, Dawn Tilbury, Department of Mechanical Engineering, University of Michigan

As we move into an era of more connected, smarter manufacturing systems, a number of opportunities and challenges arise in the area of control. The adoption of the Internet of Things (IoT) – a network infrastructure where physical and virtual entities are part of the same information network – has transformed manufacturing systems by integrating the supply chain with sensors and actuators on the factory floor. Thus far, most of the work on IoT-enabled manufacturing systems has focused on the integration of the large volumes of data gathered with IoT-devices and their transformation into useful information. To become smarter, manufacturing systems need to close the loop and transform IoT data into manufacturing knowledge and useful actions. Closing the loop will allow manufacturing systems to become more responsive to market changes and customer desires, and will improve quality, asset utilization, and profitability. To realize these goals, the future of IoT-enabled manufacturing requires closer collaboration between experts in control, manufacturing, and information systems.

The control of IoT-enabled manufacturing plants will have substantial differences with respect to earlier technologies. Sensors in the plant will connect through algorithms to additional sources of information distributed throughout the enterprise and will make decisions communicated to actuators via the Internet. Ensuring overall effectiveness of this feedback loop requires a deep understanding of dynamics and control.

This tutorial will provide an overview of control challenges in IoT-enabled manufacturing systems, and identify research opportunities for control engineers in academia and industry to contribute to a rapidly-evolving field.


Kira Barton: Closing the loop in IoT-enabled Manufacturing Systems: Challenges and Opportunities

Francisco Maturana: The Connected Enterprise

Yan Lu: Multi-Loop Feedback Control in Smart Manufacturing Systems Enabled by Standards

Keith Wiegand: Smart Manufacturing



For more information on Tutorial Sessions please contact:

Vice Chair for Industry & Applications
Ken Butts
Toyota Motor North America-R& D