Industry & Application Tutorial Sessions

Tutorial sessions address state-of-the-art control theory and industrial applications. While session formats vary, tutorial sessions often start with a longer 40- or 60-minute talk on the underlying theory or application area. After the lead presentation, there are usually several 20-minute talks highlighting particular aspects or applications of the topic area in further detail.

We are pleased to offer 8 tutorial sessions this year.

WeB01: Battery Health Management System for Automotive Applications

Presenters: Anirudh Allam, Stefano Marelli, Simona Onori and Carlo Taborelli (Clemson University), Tae-Kyung Lee (Ford Motor Company), Ying Shi, Kandler Smith, Eric Wood, Ahmad Pesaran and Shriram Santhanagopalan (National Renewable Energy Lab), Hosam Fathy (Penn State University)
Time: Wednesday, July 1, 1:30pm - 3:30pm
Location: Wabash Room

Advances in lithium-ion battery technology have created new opportunities for this energy storage system to penetrate much deeper into the transportation sector, especially through automotive hybridization. Liion cells, offering higher energy densities, long cycle life, and lighter weight than legacy battery chemistries such as nickel cadmium, nickel metal hydride and lead acid, are becoming the chemistry of preference to meet automotive industry performance targets. On the other hand, automotive applications are quite demanding as they place unprecedented cost, reliability, power/energy density, and safety requirements on electrochemical batteries. When managing and optimizing the safety and reliability of batteries, battery management system implement tasks such as monitoring and evaluating battery health status, charge control, and cell balancing. In battery management system, state of charge, state of health, and state of life are the three critical parameters to be monitored for both users and manufacturers. In this tutorial session we address advances and new challenges in the battery health management system (BHMS) design for automotive applications. The leading paper of this tutorial session, “Battery Health Management Systems for Automotive Applications” by the two organizers, introduces main definitions, terminology and concept of the battery domain and presents the challenges within this electrochemical system device that represent the main barrier to the deep market penetration of EVs/PHEVs. It then focuses on two main aspects of the BHMS design: 1) parameter identifiability of electrochemical models for diagnostics applications, and 2) propagation of aging from cell-to-cell within a battery pack and stateof- health monitoring of battery pack based on the knowledge of its components (cells) and their aging interaction. Three invited contributions, all supported by their respective papers, follow describing advances in the modeling, control and implementation of BHMS.

ThA01: Model-Based Powertrain and Aftertreatment System Control Design and Implementation

Presenters: Guoming Zhu (Michigan State University), Junmin Wang (The Ohio State University), Zongxuan Sun (University of Minnesota), Xiang Chen (University of Windsor), Mrdjan Jankovic (Ford Motor Company), Yue-Yun Wang (General Motors Company), Hussein Dourra (Chrysler Group LLC)
Time: Thursday, July 2, 10:00am - 12:00pm
Location: Wabash Room

This tutorial session introduces the need for applying model-based control techniques to vehicle powertrain and aftertreatment systems. The model-based control development process for powertrain and aftertreatment systems will be discussed and compared with the traditional approaches. The controloriented powertrain and aftertreatment system modeling techniques will be addressed along with its realtime simulation requirement for HIL (hardware-in-the-loop) simulations. The application examples of the model-based control of internal combustion engine, transmission, and aftertreatment systems will be discussed in detail. The representatives from leading industry practitioners such as Chrysler, Ford, and GM will present their experience in model-based transmission control, engine aftertreatment control, and model based powertrain calibration.

ThA13: Robust, Adaptive, and Output Feedback-Based Aircraft Control Systems

Presenters: Kevin Wise, Eugene Lavretsky, Ross Gadient, Jasmine Minteer-Levine (The Boeing Company), Petros Ioannou (University of Southern California), Benjamin Dickinson, Adam Hart, Brendan Bialy, and Sharon Stockbridge (US Air Force Research Lab), Scott Nivison (University of Florida), Chiung Hung (Bevilacqua Research Corp), James R. Cloutier (Engility Corp), Anuradha Annaswamy (MIT), Irene Gregory and Paul Rothhaar (NASA Langley), Kasey Ackerman and Steven Snyder (University of Illinois Urbana-Champaign)
Time: Thursday, July 2, 10:00am - 12:00pm
Location: Salon 5

This tutorial session presents a general process involved in designing reliable flight control laws in practice. Specifically, we introduce robust, adaptive and output feedback-based flight control architectures applied to various aerial platforms. The tutorial covers aircraft modeling that encompasses flight dynamics including aerodynamics, propulsion, structures and subsystems; models for control design, subsystem models including actuators, air data system and sensors; atmospheric disturbances and system uncertainty. Control design and analysis requirements for manned, unmanned and dual-mode aircraft are presented. Control architectures and design methods are briefly covered from the perspective of advantages and challenges they present specifically for flight control design. In addition, we present a discussion of the vision and future challenges associated with the design approaches.

ThA15: Optimization and Control of Automated Transportation Networks

Presenters: Rick Zhang and Marco Pavone (Stanford University), Kevin Spieser and Emilio Frazzoli (MIT), Patrick Boesch and Francesco Ciari (ETH Zurich), Daniel Fagnant (University of Utah), Paolo Santi (Istituto di Informatica e Telematica)
Time: Thursday, July 2, 10:00am - 12:00pm
Location: Salon 7

The progress made in the field of autonomous driving in the past decade might soon enable automated transportation networks, whereby a fleet of robotic cars provide mobility to the world urban population. Among the several engineering, legal, and economic aspects, the fleet optimization and control problem are subject of debate: How to optimally coordinate such a large-scale, dynamic, and stochastic system? Would optimally-coordinated robotic vehicles decrease congestion? What type of service should the vehicles provide (last-mile, shared minivans, etc.)? The objective of this tutorial is to bring together researchers from the control, operations research, and transportation community to discuss and formulate a research roadmap to address this new research area at the interface of control theory and transportation science. Topics that will be covered include dynamic vehicle routing of robotic vehicles, dynamic traffic assignment, microscopic / mesoscopic / macroscopic models for automated transportation networks, distributed algorithms for robotic coordination, and spatial game theory.

ThC11: Neurocontrol: Methods, Models and Technologies for Manipulating Dynamics in the Brain

Presenters: Jason Ritt (Boston University), ShiNung Ching (Washington University in St. Louis), Steven Schiff (Penn State University), Philip Sabes, Maria Dadarlat, and Joseph O'Doherty (University of California, San Francisco), Zhe Chen (New York University)
Time: Thursday, July 2, 4:00pm - 6:00pm
Location: Salon 3

Neuroscience and neurology are experiencing rapid development of new tools and technology for modifying neural activity. These tools hold extraordinary promise as probes with which to elucidate basic mechanisms in both the normal and pathological brain, and potentially as new therapeutic options in neurological disease. Many outstanding problems remain, however, particularly in the development of methods to robustly model, measure and alter the behavior of highly complex and nonlinear neuronal circuits. Systems and control theory will be a critical ingredient in the successful execution and realization of this promise. This tutorial session will offer a survey of emerging technologies and research problems at the boundary between systems engineering, neuroscience and neural medicine. Emphasis will be placed on both recent theoretical developments – for example, system identification and controllability in neuronal networks – and real-­‐world constraints in applications such as clinical deep brain stimulation, or optical stimulation in experimental neuroscience. Discussion will extend to how these constrains may necessitate the development of entirely new paradigms in systems and control theory that respond to the unprecedented challenges facing neural scientists and engineers.

FrA01: Simulation-Guided Approaches for Verification of Automotive Control Systems

Presenters: James Kapinski, Jyotirmoy Deshmukh, Xiaoqing Jin, Hisahiro Ito, and Ken Butts (Toyota Motor Company), Georgios Fainekos (Arizona State University), Alexandre Donze and Sanjit Seshia (University of California, Berkeley), Sriram Sankaranarayanan and Aditya Zutshi (University of Colorado, Boulder)
Time: Friday, July 3, 10:00am - 12:00pm
Location: Wabash Room

Automotive embedded control systems are a vital aspect of modern automotive development, but the considerable complexity of these systems has made quality checking a challenging endeavor. Simulationbased approaches are attractive, as they often scale well with the complexity of the system design. This tutorial session covers a range of simulation-guided techniques that can be used to increase the confidence in the quality of an automotive control system design. The session begins with an overview of simulationbased approaches and how they relate to the broader areas of verification and automotive control design. The subsequent three presentations cover new software tools that use simulation-guided approaches to address some aspect of automotive control design verification. The first tool automatically identifies design bugs given a control model and a machine-checkable requirement using simulation. The second tool uses simulations to automatically infer machine-checkable requirements from a control design model. The last tool automatically identifies concrete examples of undesirable system behaviors by splicing together sequences of abbreviated simulation traces. The tool presentations will include live demos using automotive control design examples.

FrA09: Breakthroughs in Controls for Power Electronics

Presenters: Sudip Mazumder (University of Illinois, Chicago), Tobias Geyer (ABB Corp.)
Time: Friday, July 3, 10:00am - 12:00pm
Location: Salon 1

This tutorial provides a radically different perspective to control of switching power electronic systems. It is based on controlling the time evolution of the switching states (i.e., switching sequences) as well as controlling the switching transition of the power semiconductor device of the solid state electronic system. The former – i.e., switching sequence based control (SBC) yields rapid response under transient condition, optimal equilibrium response, and yields seamless transition between the two states of dynamics. By enabling integration of modulation and control, SBC precludes the need for ad-hoc offline modulation synthesis. In other words, an optimal switching sequence for the power converter is generated dynamically without the need for prior determination of a modulation scheme (which generates a predetermined switching sequence) in typical conventional approaches. One of the distinctions between SBC and conventional model predictive control (MPC) is that SBC ensure optimal determination of the switching sequence of the power converter under stability bound. The tutorial will provide the mechanism to carry out SBC and MPC control syntheses and demonstrate the differences between SBC and MPC. Several device, converter, and network level implementations (e.g., motor drive, multilevel converter, microgrid, parallel inverters, aircraft power system) of the SBC will be provided. The second part of the tutorial will focus on switching transition control (STC). The primary objective of STC is to demonstrate how key power electronic system parameters including 𝑑𝑑𝑣𝑣/𝑑𝑑𝑡𝑡 and 𝑑𝑑𝑖𝑖/𝑑𝑑𝑡𝑡 stress, switching loss, electromagnetic noise emission can be controlled dynamically by modulating the dynamics of the power semiconductor devices. Both electrical and newly developed optical control mechanisms to achieve STC will be demonstrated. In the context of the latter, mechanisms for monolithic integration of switching sequence control as well as switching transition control will be outlined and the revolutionary impact of such a novel integration on system performance will be demonstrated with practical applications.

FrB10: Autonomy and Machine Intelligence in Complex Systems

Presenters: Kyriakos Vamvoudakis and Joao Hespanha (University of California, Santa Barbara), Panos Antsaklis (University of Notre Dame), Warren Dixon (University of Florida), Frank Lewis, Hamidreza Modares, and Bahare Kiumarsi (University of Texas at Arlington)
Time: Friday, July 3, 1:30pm - 3:30pm
Location: Salon 2

This tutorial will discuss the development of novel state-of-the-art control approaches and theory for complex systems based on machine intelligence in order to enable full autonomy. Given the presence of modeling uncertainties, the unavailability of the model, the possibility of cooperative/non-cooperative goals and malicious attacks compromising the security of teams of complex systems, there is a need for approaches that respond to situations not programmed or anticipated in design. Unfortunately, existing schemes for complex systems do not take into account recent advances of machine intelligence. We shall discuss on how to be inspired by the human brain and combine interdisciplinary ideas from different fields, i.e. computational intelligence, game theory, control theory, and information theory to develop new self-configuring algorithms for decision and control given the unavailability of model, the presence of enemy components and the possibility of network attacks. Due to the adaptive nature of the algorithms, the complex systems will be capable of breaking or splitting into parts that are themselves autonomous and resilient. The algorithms discussed will be characterized by strong abilities of learning and adaptivity. As a result, the complex systems will be fully autonomous, and tolerant to communication failures.