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[Technical Meeting]

Principal Time Series for High-dimensional Dynamic Data Mining
Speaker(s): Professor Si-Zhao Joe Qin, University of Southern California , USA
June 15, 2017


Host: IEEE Joint CS/RA/SMC Chapter in Vancouver Section

Time: 14:00:00 - 15:00:00

Place: MacLeod 418, Main Mall 2356, UBC,

Speaker(s): Professor Si-Zhao Joe Qin, University of Southern California , USA

Speaker Bio: Dr. S. Joe Qin obtained his B.S. and M.S. degrees in Automatic Control from Tsinghua University in Beijing, China, in 1984 and 1987, respectively, and his Ph.D. degree in Chemical Engineering from University of Maryland at College Park in 1992. He is the Professor at the Viterbi School of Engineering of the University of Southern California and Presidential Chair at the Chinese University of Hong Kong, Shenzhen.

Dr. Qin is a Fellow of IEEE and Fellow of the International Federation of Automatic Control (IFAC). He is a recipient of the National Science Foundation CAREER Award, the 2011 Northrop Grumman Best Teaching award at Viterbi School of Engineering, the DuPont Young Professor Award, Halliburton/Brown & Root Young Faculty Excellence Award, NSF-China Outstanding Young Investigator Award, Chang Jiang Professor of Tsinghua University, National ``Thousand Talent'' Professor of China, and recipient of the IFAC Best Paper Prize for a model predictive control survey paper published in Control Engineering Practice. He is currently a Subject Editor for Journal of Process Control and a Member of the Editorial Board for Journal of Chemometrics. He has published over 130 papers in SCI journals or book chapters, with over 8,000 ISI Web of Science citations and the associated h-index of 46. He has given over 40 invited plenary or keynote speeches and over 100 invited technical seminars worldwide. Dr. Qin's research interests include process data analytics, machine learning, process monitoring and fault diagnosis, model predictive control, system identification, building energy optimization, multi-step batch process control, and control performance monitoring.

Details: Industrial processes and equipment are designed with a specific purpose under normal operations. However, for operation under abnormal situations, data become indispensable to diagnose root causes. In this talk we offer a perspective on the essence of process data analytics, how data have been effectively used in process operation and control, and new perspectives on how process systems operations evolve to a paradigm of data-centric operations and control.

The talk covers what we proposed as principal time series extraction from high-dimensional dynamic data for prediction and fault diagnosis. A dynamic inner PCA (DiPCA) algorithm is presented where dynamic latent variables are first extracted to capture the dynamic relations in the data. After the dynamic relations are extracted, static PCA can be applied to model the static relations left in the data. The models provided by DiPCA generate a subspace of principal time series that are most predictable from the past data. Fault detection indices are developed based on the proposed model for the purpose of process monitoring.

Slides: Download

[Distinguished Lecture]

Networked Control Systems with Industrial Applications
Speaker(s): Professor Huijun Gao ,
Director, Research Institute of Intelligent Control and Systems
Dean, School of Sciences
Harbin Institute of Technology , China
February 03, 2017


Host: IEEE Joint CS/RA/SMC Chapter in Vancouver Section,
IEEE Circuits and Systems Society joint Chapter of the Vancouver/Victoria Sections,
IEEE Systems, Man, and Cybernetics Socity Distinguished Lecturer Program

Time: 13:30:00 - 15:00:00

Place: ASB 10901 (Big Data Hub Board Room), Simon Fraser University, Burnaby, BC, Canada

Speaker(s): Professor Huijun Gao ,
Director, Research Institute of Intelligent Control and Systems
Dean, School of Sciences
Harbin Institute of Technology , China

Speaker Bio: Huijun Gao received his Ph.D. degree in control science and engineering from Harbin Institute of Technology, China, in 2005. He was a Research Associate with the Department of Mechanical Engineering, The University of Hong Kong, from November 2003 to August 2004. From October 2005 to October 2007, he carried out his postdoctoral research with the Department of Electrical and Computer Engineering, University of Alberta, Canada. Since November 2004, he has been with Harbin Institute of Technology, where he is currently a Professor and director of the Research Institute of Intelligent Control and Systems. Prof. Gao's research interests include network-based control, robust control/filtering theory and their engineering applications. He is an IEEE Fellow and received the IES David Irwin Early Career Award. He is Co-Editor-in-Chief of IEEE Transactions on Industrial Electronics and Associate Editor of Automatica, IEEE Transactions on Control Systems Technology, IEEE Transactions on Cybernetics, IEEE/ASME Transactions on Mechatronics etc. Prof. Gao is an IEEE Industrial Electronics Society (IES) Administration Committee (AdCom) member. He is a Thomson Reuters Highly Cited Researcher and was listed among the top 17 scholars in ``The World's Most Influential Scientific Mind'' by Thomson Reuters, 2014.

Details: In recent years, the analysis and synthesis of networked control systems (NCSs) have received increasing attention from both scientific and industrial communities. Compared with traditional point-to-point control systems, the main advantages of NCSs come from their low cost, their flexibility and easy re-configurability, their natural reliability and robustness to failure, and their adaptation capability. Consequently, NCSs have been finding applications in a broad range of areas such as power grids, water distribution networks, transportation networks, haptics collaboration over the Internet, mobile sensor networks, and so on. However, the introduction of communication channels in the control loop also brings some network-induced critical issues or constraints such as variable transmission delays, data packet dropouts, packet disorder, quantization errors, etc., which would significantly degrade the system performance or even destabilize the system in certain conditions. This talk will first introduce some elegant approaches to network-based control and estimation problems. Then, a novel two-layer network-based architecture for operational control of industrial processes will be discussed. It will be shown that under the proposed framework, the overall optimal operational control of networked industrial processes can be achieved.

Light refreshments will be served. The event is open to public. We would greatly appreciate if you could register at https://events.vtools.ieee.org/meeting_registration/register/43191 so that we may more accurately estimate the room size and refreshments.

Slides: Download

[Seminar]

Learning Control: Ideas and Problems in Adaptive Fuzzy Control
Speaker(s): Professor Shun-Feng Su, Department of Electrical Engineering, National Taiwan University of Science and Technology, Taiwan
July 26, 2016


Host: IEEE Circuits and Systems Society joint Chapter of the Vancouver/Victoria Section & IEEE Joint CS/RA/SMC Chapter in Vancouver Section

Time: 14:30:00 - 15:30:00

Place: IRMACS Centre, ASB 10901 (Board Room), Simon Fraser University, Burnaby, BC, Canada

Speaker(s): Professor Shun-Feng Su, Department of Electrical Engineering, National Taiwan University of Science and Technology, Taiwan

Speaker Bio: Shun-Feng Su received the B.S. degree in electrical engineering, in 1983, from National Taiwan University, Taiwan, R.O.C., and the M.S. and Ph.D. degrees in electrical engineering, in 1989 and 1991, respectively, from Purdue University, West Lafayette, IN. He is now a Chair Professor of the Department of Electrical Engineering, National Taiwan University of Science and Technology, Taiwan, R.O.C. He is an IEEE Fellow and CACS fellow. He has published more than 200 refereed journal and conference papers in the areas of robotics, intelligent control, fuzzy systems, neural networks, and non-derivative optimization. His current research interests include computational intelligence, machine learning, virtual reality simulation, intelligent transportation systems, smart home, robotics, and intelligent control. Dr. Su is very active in various international/domestic professional societies. He is now the president of the International Fuzzy Systems Association. He now is also in the Boards of Governors of the IEEE SMC society, Chinese Automatic Control Society, the Taiwan Society of Robotics, and the Taiwan Fuzzy System Association. Dr. Su also acted as Program Chair, Program Co-Chair, or PC members for various international and domestic conferences. Dr. Su currently serves as Associate editors of IEEE Transactions on Cybernetics, IEEE Transactions on Fuzzy Systems, and IEEE Access, a subject editor of Journal of Chinese Institute of Engineers, and the Editor-in-Chief of International Journal of Fuzzy Systems.

Details: Light refreshments will be served. The event is open to public. We would greatly appreciate if you would please register at https://meetings.vtools.ieee.org/meeting_registration/post_registration/40539 so that we may more accurately estimate the room size and refreshments. Maps: IRMACS, SFU (http://www.irmacs.sfu.ca/about/visitors/getting-to-sfu)

Intelligent control is a promising way of control design in recent decades. Intelligent control design usually needs some knowledge of the system considered. However, such knowledge usually may not be available. Learning becomes a important mechanism for acquiring such knowledge. Learning control seems a good idea for control design for unknown or uncertain systems. To learn controllers is always a good idea, but somehow like a dream. It is because learning is to learn from something. But when there is no good controller, where to learn from? Nevertheless, there still exist approaches, such as adaptive fuzzy control, that can facilitate such an idea. It is called performance based learning (reinforcement learning and Lyapunov stability). This talk is to discuss fundamental ideas and problems in one learning controller -- adaptive fuzzy control. Some deficits of such an approach are discussed. The idea is simple and can be extended to various learning mechanisms. In fact, such an idea can also be employed in various learning control schemes. If you want to use such kind of approaches, those issues must be considered in your study.

[Technical Meeting]

Nonlinear Adaptive Robust Control -- Theory and Applications
Speaker(s): Professor Bin Yao, Purdue University (USA) & Zhejiang University (China),
July 18, 2016


Host: IEEE Joint CS/RA/SMC Chapter

Time: 11:00:00 - 12:00:00

Place: MacLeod 418, Main Mall 2356, UBC

Speaker(s): Professor Bin Yao, Purdue University (USA) & Zhejiang University (China),

Speaker Bio: Dr. Yao received his PhD degree in Mechanical Engineering from the University of California at Berkeley in 1996 after obtaining B.Eng. in Applied Mechanics from Beijing University of Aeronautics and Astronautics of China in 1987 and M.Eng. degree in Electrical Engineering from Nanyang Technological University of Singapore in 1992. He has been with the School of Mechanical Engineering at Purdue University since 1996 and was promoted to the rank of Associate Professor in 2002 and Professor in 2007. He was also honored as a Kuang-piu Professor in 2005, a Changjiang Chair Professor at Zhejiang University by the Ministry of Education of China in 2010, and an awardee of the National Recruitment of Global Experts of China in 2011.

Dr. Yao was awarded a Faculty Early Career Development (CAREER) Award from the National Science Foundation (NSF) in 1998 and a Joint Research Fund for Outstanding Overseas Chinese Young Scholars from the National Natural Science Foundation of China (NSFC) in 2005. His research interests include the design and control of intelligent high performance coordinated control of electro-mechanical/hydraulic systems, optimal adaptive and robust control, nonlinear observer design and neural networks for virtual sensing, modeling, fault detection, diagnostics, and adaptive fault-tolerant control, and data fusion. He has published significantly on the subjects with well over 270 technical papers while enjoying the application of the theory through industrial consulting. He is the recipient of the O. Hugo Schuck Best Paper (Theory) Award from the American Automatic Control Council in 2004, the Outstanding Young Investigator Award of ASME Dynamic Systems and Control Division (DSCD) in 2007, and the Best Conference Paper Awards on Mechatronics of ASME DSCD in 2012.

He is a Fellow of ASME and a senior member of IEEE and has chaired numerous sessions and served in a number of International Program Committee of various IEEE, ASME, and IFAC conferences including the General Chair of the 2010 IEEE/ASME International Conference on Advanced Intelligent Mechatronics and the International Program Committee Chair of the 6th IFAC Symposium on Mechatronic Systems in 2013. From 2000 to 2002, he was the Chair of the Adaptive and Optimal Control Panel and, from 2001 to 2003, the Chair of the Fluid Control Panel of the ASME Dynamic Systems and Control Division (DSCD). He was the founding member to the ASME DSCD Mechatronics Technical Committee in 2005 and served in various roles including TC Chair. He was a Technical Editor of the IEEE/ASME Transactions on Mechatronics from 2001 to 2005 and Associate Editor of the ASME Journal of Dynamic Systems, Measurement, and Control from 2006 to 2009. Details can be found at https://engineering.purdue.edu/~byao

Details: Control of nonlinear systems with uncertainties has been one of the mainstream areas of focus in control community during the past twenty years. Two approaches have been popular: robust adaptive control and deterministic robust control (DRC). This talk will present a theoretically solid nonlinear adaptive robust control (ARC) approach that well reflects what a human brain normally does -- seamless integration of the fast reaction to immediate feedback information and the slow learning utilizing large amount of stored past information that is available in the computer based control systems -- to synthesize performance oriented controllers with built-in intelligences under practical constraints.

The first half of the seminar focuses on the basic ideas of ARC strategy and touches some specific design issues. The issues include: (i) means to achieve fast robust feedback; (ii) learning techniques (e.g., parameter adaptation) to reduce model uncertainties for an improved performance; (iii) desired compensation structure to alleviate the effect of measurement noises, (iv) direct and indirect ARC designs, (v) integrated direct/indirect ARC design, and (vi) constrained optimization based model compensation ARC. The constructed ARC controllers range from the full-state feedback ARC for MIMO nonlinear systems in semi-strict feedback forms and the nonlinear observer based ARC for a class of nonlinear systems with partial state feedback to the output feedback ARC for uncertain linear systems with bounded disturbances.

The second half the seminar focuses on the applications of the proposed ARC approach to the intelligent and precision control of several electro-mechanical/hydraulic systems. The applications include the precision motion control of linear motor driven high-speed/high-acceleration electromechanical devices (e.g., machine tools) for precision manufacturing, the ultra precision motion control of piezo-actuator driven devices for nano-positioning, the motion and pressure control of electro-hydraulic systems (e.g., industrial hydraulic excavators), the energy-saving control of electro-hydraulic systems via novel programmable valves, and the coordinated motion and force tracking control of robot manipulators in contact with various contacting surfaces, exoskeletons, redundant drive systems, mobile robots, and a hummingbird-size flapping-wing micro aerial vehicle (MAV). Various experimental results will be shown to illustrate the high performance and versatility nature of the proposed ARC approac

[Technical Event]

Workshop & Site Tour at Zaber Technologies: Simplifying Designs Requiring Multi-axis Motion
Speaker(s): Applications Engineers: Mike McDonald, Duncan Davidson, Zaber Technologies Inc., Vancouver, Canada
May 26, 2016


Host: Technical Event

Time: 15:00:00 - 18:00:00

Place: #2 -- 605 West Kent Ave. N, Vancouver, BC V6P 6T7

Speaker(s): Applications Engineers: Mike McDonald, Duncan Davidson, Zaber Technologies Inc., Vancouver, Canada

Speaker Bio: About Zaber Technologies Inc, adapted from http://www.zaber.com/:

What We Do
At Zaber, we design and manufacture precision positioning devices that are affordable, integrated, and easy to use. Our devices are used in many different applications and markets, such as photonics and optics, life sciences, microscopy, and industrial automation.

How We Started
Zaber was founded in 1997 by a group of friends. Back then, precision linear actuators all used DC motors with gearbox and encoders, and they required bulky controllers and a number of other accessories and supplies. Precision motion control was expensive and difficult to set up and use. Recognizing the need for affordable and integrated solutions for motion control, Zaber released the world's first precision linear actuator with a built-in controller, the T-LA28 Series linear actuator. Based on a stepper motor instead of a combination of DC motor, gearbox, and encoder, the T-LA28 was the first device to feature all control and drive electronics in one compact package. The integration of all control and drive electronics in the same package became the foundation of Zaber's T-, A- and, later X-Series product lines.

Where We Are Today
Since introducing our first series, our product offering has grown to include over 100 motion control products, which are distributed worldwide. We continue to advance our design and manufacturing capabilities, allowing us to build, test, and ship most of our products within 1-5 business days.

Details: Does your application move in two or more directions? Have you encountered challenges when working with multi-axis system?

Zaber designs and manufactures motorized devices and systems that can be used in precision automation and multi-axis configurations. Join our applications engineers for a discussion on multi-axis motion, methods to simplify application design, and solutions to integrate motion control quickly and easily.

This free workshop and site tour will give participants a chance to learn about Zaber, our R&D capabilities, company history, and culture. We will have live product demos, a Q&A with Zaber's engineers, and a site tour of our R&D and production facilities.

Registration is required for this event as seats are limited. Deadline for registration closes on Monday, May 23, 2016, 5:00pm PST.

To register, please email contact@zaber.com and provide your Name, Company, and Telephone number. If you have any food allergies, please also include a note in your email, as light refreshments will be served.

Agenda 2:50pm - 3:00pm: Arrive* and sign-in at Zaber Technologies (1st Floor)
3:00pm - 4:45pm: Introductions and presentation on Zaber
4:45pm - 5:30pm: Site tour
5:30pm - 6:00pm: Product demos and Q&A
*Free parking available.

[Technical Meeting]

State Estimation using Moving Horizon Estimation and Particle Filtering
Speaker(s): Professor James B. Rawlings, University of Wisconsin, USA
March 21, 2016


Host: IEEE Joint CS/RA/SMC Chapter in Vancouver Section & UBC Chemical and Biological Department

Time: 15:00:00 - 16:00:00

Place: Institute of Applied Mathematics, Room LSK 460, UBC

Speaker(s): Professor James B. Rawlings, University of Wisconsin, USA

Speaker Bio: James B. Rawlings received the B.S. from the University of Texas in and the Ph.D. from the University of Wisconsin, both in Chemical Engineering. He spent one year at the University of Stuttgart as a NATO postdoctoral fellow and then joined the faculty at the University of Texas. He moved to the University of Wisconsin in 1995 and is currently the Paul A. Elfers Professor and W. Harmon Ray Professor of Chemical and Biological Engineering and the co-director of the Texas-Wisconsin-California Control Consortium (TWCCC). His research interests are in the areas of chemical process modeling, molecular-scale chemical reaction engineering, monitoring and control, nonlinear model predictive control and moving horizon state estimation. Professor Rawlings has written numerous research articles and coauthored three textbooks: "Modeling and Analysis Principles for Chemical and Biological Engineers" (2013) with Mike Graham, "Model Predictive Control: Theory and Design" (2009), with David Mayne, and "Chemical Reactor Analysis and Design Fundamentals" (2004), with John Ekerdt. He is a Fellow of AIChE and IEEE.

Details: This seminar provides an overview of currently available methods for state estimation of linear, constrained and nonlinear dynamic systems. The seminar begins with a brief overview of the Kalman filter, which is the optimal estimator for a linear dynamic system subject to independent, normally distributed disturbances. Next, alternatives for treating nonlinear and constrained dynamic systems are discussed. Two complementary methods are presented in some detail: moving horizon estimation, which is based on optimization, and particle filtering, which is based on sampling. The advantages and disadvantages of these two approaches are presented. Topics for new research are suggested that address combining the best features of moving horizon estimators and particle filters.

[Technical Meeting]

Optimal Dynamic Operation of Chemical Processes: Assessment of the Last 20 Years and Current Research Opportunities
Speaker(s): Professor James B. Rawlings, University of Wisconsin, USA
March 21, 2016


Host: IEEE Joint CS/RA/SMC Chapter in Vancouver Section & UBC Chemical and Biological Department

Time: 10:00:00 - 11:00:00

Place: CHBE (Chemical and Biological Engineering) Room 202, UBC

Speaker(s): Professor James B. Rawlings, University of Wisconsin, USA

Speaker Bio: James B. Rawlings received the B.S. from the University of Texas in and the Ph.D. from the University of Wisconsin, both in Chemical Engineering. He spent one year at the University of Stuttgart as a NATO postdoctoral fellow and then joined the faculty at the University of Texas. He moved to the University of Wisconsin in 1995 and is currently the Paul A. Elfers Professor and W. Harmon Ray Professor of Chemical and Biological Engineering and the co-director of the Texas-Wisconsin-California Control Consortium (TWCCC). His research interests are in the areas of chemical process modeling, molecular-scale chemical reaction engineering, monitoring and control, nonlinear model predictive control and moving horizon state estimation. Professor Rawlings has written numerous research articles and coauthored three textbooks: "Modeling and Analysis Principles for Chemical and Biological Engineers" (2013) with Mike Graham, "Model Predictive Control: Theory and Design" (2009), with David Mayne, and "Chemical Reactor Analysis and Design Fundamentals" (2004), with John Ekerdt. He is a Fellow of AIChE and IEEE.

Details: This talk, intended for the general chemical engineering audience, provides a critical assessment of the research progress in the fields of dynamic operation of chemical processes and process control. The following points are discussed:
(i) What new intellectual ideas, concepts, and tools have emerged from this research field during the last 20 years.
(ii) How successfully have the research innovations in problem conceptualization, formulation, and solution been reduced to industrial practice.
(iii) What application areas have benefited from this research.

Next we present a selection of open problems and research challenges. These research challenges are formulated by enumerating the current industrial needs in different application areas, and identifying common themes that can be addressed by developing new tools in systems theory and engineering. We focus on two topics of interest to our research group:
(i) How do we distribute tasks in a large-scale application to a collection of agents/controllers so that the overall system achieves near optimal operation.
(ii) How do we use systems and control tools to address the larger goal of optimizing process economic performance rather than traditional lower level tasks such as set point tracking and disturbance rejection.

[Technical Meeting]

Consensus and MPC-based Formation Control for a Multi-UAV System
Speaker(s): Professor Toru Namerikawa, Keio University, Japan
February 16, 2016


Host: IEEE Joint CS/RA/SMC Chapter in Vancouver Section

Time: 11:00:00 - 12:00:00

Place: MacLeod 418, Main Mall 2332, UBC

Speaker(s): Professor Toru Namerikawa, Keio University, Japan

Speaker Bio: Toru Namerikawa received the B.E., M.E and Ph. D of Engineering degrees in Electrical and Computer Engineering from Kanazawa University, Japan, in 1991, 1993 and 1997, respectively. He is currently a Professor at Department of System Design Engineering, Keio University, Yokohama, Japan. He held visiting positions at Swiss Federal Institute of Technology in Zurich in 1998, University of California, Santa Barbara in 2001, University of Stuttgart in 2008 and Lund University in 2010. His main research interests are robust control, distributed and cooperative control and their application to power network and mechatronic systems.

Details: In this talk, a cooperative formation control strategy with collision-avoidance capability for a multi-unmanned aerial vehicle (UAV) system using decentralized model predictive control (MPC) and consensus-based control is dealt with. Consensus-based control algorithms are applied for formation flying in three-dimensional space. However, UAVs where these formation control algorithms are applied have not the ability to avoid collisions. Decentralized model predictive control (MPC) is applied to generate control inputs for formation flying with collision-avoidance capability. Using decentralized MPC, each UAV plans only its own action to track the trajectory specified by the formation control algorithm within the feasible regions satisfying collision-avoidance. We show how the optimization problems with coupled constraints such as collision-avoidance can be solved by each decoupled UAV in parallel with the other UAVs so that the decisions independently taken by each UAV can ensure consistency in coupled constraints of collision-avoidance. The computation time is also taken into account because it is a crucial factor to apply MPC to actual UAVs. Finally, the proposed approach is validated by some simulations.

Slides: Download

[Technical Meeting]

Optimal Input Design in System Identification for MPC
Speaker(s): Professor Bo Wahlberg, KTH Royal Institute of Technology, Stockholm, Sweden
May 23, 2015


Host: IEEE Joint CS/RA/SMC Chapter in Vancouver Section

Time: 13:30:00 - 14:30:00

Place: MacLeod 418, 2356 Main Mall, Vancouver, UBC

Speaker(s): Professor Bo Wahlberg, KTH Royal Institute of Technology, Stockholm, Sweden

Speaker Bio: Bo Wahlberg received the M.Sc. degree in Electrical Engineering 1983 and the Ph.D. degree in 1987 from Linkoping University, Sweden. In December 1991, he became Professor of the Chair of Automatic Control at KTH Royal Institute of Technology, Stockholm, Sweden. He was a visiting professor at the Department of Electrical Engineering, Stanford University, USA, August 1997 - July 1998 and August 2009 - June 2010, and vice president of KTH 1999 - 2001. He is a Fellow of the IEEE for his contributions to system identification using orthonormal basis functions. He is a co-founder of Centre of Autonomous Systems and the Linnaeus Center ACCESS on networked systems at KTH. He is the KTH founding director and PI for the Wallenberg Autonomous Systems Program, that recently was granted 200 million USD over ten years for research into autonomous systems and software development. His research interests include system identification, modeling and control of industrial processes, and statistical signal processing with applications in autonomous systems. Bo Wahlberg is currently visiting The University of British Columbia for two months.

Details: System identification concerns how to construct mathematical models of dynamic systems based on experimental data. The quality of an estimated model should be related to the specifications of the intended application, in our case Model Predictive Control (MPC). In model-based control applications it is often possible to externally excite the system during the data collection experiment. The properties of the exciting input signal directly influence the quality of the identified model, and well-designed excitation signals can reduce both the experimental time and efforts. The objective of this presentation is to give an overview of Application Oriented Optimal Input Design for MPC. This first step concerns how to measure control performance degradation due to model mismatch (robustness). This is quite a difficult problem for MPC and several approximations will be discussed. The idea is then to minimize experimental costs (e.g. experimental time or the energy of the excitation signal), while guarantying that the estimated model with a given probability satisfies the specifications of the application. In this setting we will study constraints on both input and output signals connected to MPC. This will result in an optimization problem, where the optimal solution should reveal system properties important for the application while hiding irrelevant dynamics. We will discuss several ways to solve this optimal input problem including how handle signal constraint. The optimal input design methods discussed in this presentation are implemented in the open Matlab toolbox MOOSE.
This is joint work with Hakan Hjalmarsson, Cristian Rojas, Mariette Annergren, Christian Larsson, Per Hagg, Afrooz Ebadat and Patricio Valenzuela Pacheco.

[Technical Meeting]

Field Robotics and Assistive Robotic Systems in Industrial Applications
Speaker(s): Professor Dikai Liu, University of Technology Sydney (UTS), Australia
May 25, 2015


Host: IEEE Joint CS/RA/SMC Chapter in Vancouver Section

Time: 11:00:00 - 12:00:00

Place: Kaiser 2020, UBC, Main Mall 2332, Vancouver

Speaker(s): Professor Dikai Liu, University of Technology Sydney (UTS), Australia

Speaker Bio: Professor Dikai Liu is Co-Director of the Centre for Autonomous Systems (www.cas.uts.edu.au) at the University of Technology Sydney (UTS), Australia. His main research interest is robotics including navigation, exploration, robot teams and physical human-robot interaction. He has developed many robotic systems for practical applications, including autonomous robots for steel structure maintenance, bio-inspired autonomous climbing robots for complex structure inspection, and assistive robots for augmenting human strength in industrial applications. Since 2005, his research has received three best paper awards (ISARC'2007, ISARC'2006, ISSNIP'2011-Biomedical Sensing and Sensors Symposium) and one best paper award nomination from international conferences; won five national and university awards (two EEAS'2013, 2012 UTS VC's Award for Research Excellence, 2006 Carrick Australia Citation for teaching, 2005 UTS Teaching Award); and been in finalists of four international and national awards (2013 IEEE/IFR IERA Award, 2013 AEEA, 2013 Australian Museum Eureka Prize, and 2005 AAEE). He is the recipient of three Australian patents. Dikai Liu received his PhD in 1997 from the Wuhan University of Technology, China.

Details: Current applications of robotics is distinguished from more traditional automation by the focus on robots that operate in relatively unstructured, dynamic, difficult and often hazardous environments. Over the past decade, a number of robotic systems have been deployed in highly challenging application areas including infrastructure maintenance, mining, cargo handling and healthcare. The first part of this presentation will focus on a range of autonomous robotic systems developed at the Centre for Autonomous Systems at the University of Technology Sydney, Australia. Key elements of these systems ranging from perception, mapping, control to learning will be described.

Advances in computing, sensing, actuation, mechanism design, control and machine learning have opened up the potential to build mobile manipulators that can coexist and cooperate with humans. Recent research has demonstrated the significant challenges that need to be overcome in order to make a robot effectively cooperate with a human, in contrast to building an autonomous robot that operates on its own. The second half of this talk will discuss a new assistance-as-needed paradigm for physical human-robot interaction and strength augmentation. It will present the research that uses an optimization approach with a musculoskeletal model to estimate the physical capabilities of a human worker, accounting for limb dynamics and external force interactions. Methods, advantages and limitations of implementing the musculoskeletal model-based assistance-as-needed paradigm will also be discussed.

[Distinguished Lecture]

Evolving embedded systems and their vehicle applications
Speaker(s): Dimitar Filev, Ford Motor Company, USA
March 20, 2015


Host: IEEE Joint CS/RA/SMC Chapter in Vancouver Section

Time: 11:00:00 - 12:00:00

Place: McLeod 418, UBC

Speaker(s): Dimitar Filev, Ford Motor Company, USA

Speaker Bio: Dr. Dimitar P. Filev is the Executive Technical Leader - Intelligent Control & Information Systems, Ford Research & Advanced Engineering. He is conducting research in modeling and control of complex systems, intelligent control, fuzzy and neural systems, and their applications to automotive engineering. He is the recipient of the 2008 Norbert Wiener Award of the IEEE SMC Society, the 2007 IFSA Outstanding Industrial Applications Award, and the highest Ford Motor Company corporate awards - he was awarded 6 times with the Henry Ford Technology Award and with the 2010 Inaugural Dr. Haren Gandhi Research & Innovation Award for development and implementation of advanced automotive technologies, and for his long term research contributions.

He has published 4 books and over 200 papers, and holds over 60 US and foreign patents. Dr. Filev is a Fellow of IEEE. He received his PhD. degree in electrical Engineering from the Czech Technical University in Prague in 1979.

Details: The emerging trend of increasing flexibility, adaptation, and autonomy of embedded control and information systems is the driving force behind the evolving systems paradigm. Evolving systems are systems with flexible model structure that adjust to changes which cannot be solely handled by parameter adaptation. Evolving intelligent systems develop their structure and knowledge representation through continuous learning from data and interaction with the environment. They exploit synergies between two powerful concepts - real time data granulation and machine learning - with model structure that may include regression models, neural networks, fuzzy, and/or stochastic models.

Practical applications encompass a wide range of systems with variable parameters and structure, and multiple operating modes. This presentation provides an overview of the multiple facets of evolving systems theory and describes some of their automotive applications to adaptive process control, automated calibration, anomaly detection, driver state estimation, and fuel economy optimization.

[Technical Meeting]

Transient model and its application in model-based control and calibration of automotive powertrains
Speaker(s): Professor Tielong Shen, Sophia University, Tokyo, Japan
February 27, 2015


Host: IEEE Joint CS/RA/SMC Chapter in Vancouver Section

Time: 10:30:00 - 11:30:00

Place: Kaiser 2020/2030, 2332 Main Mall, UBC

Speaker(s): Professor Tielong Shen, Sophia University, Tokyo, Japan

Speaker Bio: Dr. Tielong Shen received the Ph.D. degree in Mechanical Engineering from Sophia University, Japan. From April 1992, he has been a faculty member of the Chair of Control Engineering in Department of Mechanical Engineering, Sophia University, where he currently serves as full Professor. Since 2005, he is also served concurrently "Luojia Xuezhe" Chair Professor of Wuhan University, and Visiting Professor for several universities including University of Science and Technology of China, Yanshan University, etc. He also joined Newcastle University, Australia, as Visiting Fellow in 2003, and University of Rome "Tar Vergat", Italy, as Visiting Professor, in 2009.

His research interests include control theory and applications in automotive systems, power systems, and mechanical systems. From 1997, he has been serving as Chief Editor, Regional Editor, Associate Editors, and Guest Editors for several international journals including Transaction of SICE, Japan, Inter- national Journal of Modeling, Identification and Control, International Journal on Robust and Nonlinear Control, Asian Journal of Control, Control Theory and Technology, and The IEEE Control System Society Conference Editorial Board. Dr. Shen was also serving as Chairs/co-chairs for many international conferences. He is now serving as General Chair of CCC&SICE2015, and Publicity Chair of ECC2015.

He is currently a member of the IEEE Technical Committee on Automotive Control and IFAC Technical Committee on Automotive System Control. Dr. Shen has author/co-authored eleven text books in Japanese, English and Chinese, respectively, and has published more than 140 research papers in peer-reviewed major journals.

Details: For internal combustion engines, modeling and control of transient behavior are important issues to improve the efficiency and the emission performance. Recently, the attention in automotive industry has been focused on model-based development technology. Meanwhile, in the community of control theory, mathematical model has been essential tools for control strategy design and simulation validation of dynamical systems.

This talk addresses the topic of model-based real-time optimization problems for control and identification of advanced powertrains. Three case studies will be introduced. First, model predictive control approach is presented to the torque tracking problem of gasoline engines. Second, model-based experiment design problem is addressed to achieve the transient model. Finally, an engine-in-the-loop simulation system is demonstrated with testing results of a receding horizon optimal energy management strategy of HEVs.

[Distinguished Lecture]

PAL Robotics: History, Research Activities and Collaboration Opportunities
Speaker(s): Dr. Francesco Ferro, PAL Robotics, Barcelona, Spain
August 26, 2014


Host: IEEE Joint CS/RA/SMC Chapter in Vancouver Section

Time: 11:00:00 - 12:00:00

Place: Kaiser 2020/2030, UBC

Speaker(s): Dr. Francesco Ferro, PAL Robotics, Barcelona, Spain

Speaker Bio: Francesco Ferro obtained a BSc degree in Telecommunications Engineering in 2002 at the Politecnico di Torino. He began a PhD in Computer Vision but left it in 2004 to attend a robotics humanoid project, where he still works on. He started the development of stereo vision algorithms and later he joined the autonomous robot navigation team to implement various SLAM algorithms. In 2008 he became the manager of the software department of PAL. He obtained an MBA at the UB University in Barcelona in the 2011. From the beginning of the 2011 he is the CEO of PAL Robotics, in charge of REEM's humanoids robots development.

Details: PAL Robotics is a robotics R & D company with a multi-national team from across the world, working specially in the humanoid robotics field. PAL Robotics has developed several humanoid robots: the biped REEM-A, REEM-B and the last creation REEM-C, and other with a mobile base, REEM-H1 and REEM. Its diverse team consists of people from various countries, mostly mechanic, electronic and software engineers with many years of experience in the robotics industry. The presentation consists of three main topics that will be explained in the following lines. Firstly, the history of the company and the several robots already developed will be introduced. The goals of the company, the strong relationship with the investors and the different humanoid robotics platforms and their characteristics will be also presented. Secondly, a selection of research lines will be showed. Navigation, walking, grasping, human robot interaction as well as hardware features will be presented. Finally, the different ways of collaboration with the company will be dealt: internships, co-advising master/PhD thesis, PhD programs, FP7 projects, etc. At the end some performance about REEMs events will be shown.

[Technical Meeting]

Real-time Pricing and Stabilization in Power Grids
Speaker(s): Toru Namerikawa, Keio University, Japan
August 11, 2014


Host: IEEE Joint CS/RA/SMC Chapter in Vancouver Section

Time: 10:00:00 - 11:00:00

Place: Room 2020/2030, Kaiser Bldg, UBC

Speaker(s): Toru Namerikawa, Keio University, Japan

Speaker Bio: Toru Namerikawa received the B.E., M.E and Ph. D of Engineering degrees in Electrical and Computer Engineering from Kanazawa University, Japan, in 1991, 1993 and 1997, respectively. He is currently a Professor at Department of System Design Engineering, Keio University, Yokohama, Japan. He held visiting positions at Swiss Federal Institute of Technology in Zurich in 1998, University of California, Santa Barbara in 2001, University of Stuttgart in 2008 and Lund University in 2010. His main research interests are robust control, distributed and cooperative control and their application to power network systems.

Details: The contribution of electrical power systems to global climate change has become one of the more urgent problems facing the world; accordingly, a high amount of distributed generation capacity, including photovoltaic, wind power, biomass, and co-generated power, is being planned for installation into large-scale power network systems in order to reduce greenhouse gas emissions and fossil fuel reliance. However, it is well understood that many renewable resources pose risks to power system stability in terms of adverse effects on frequency and the creation of voltage fluctuations; hence, in embedding renewables into a grid, it is necessary to create an explicit plan for plant cooperation and generation optimization in order to ensure safety.

This talk deals with a game theoretic optimal real-time pricing method based on dual decomposition and its application to load frequency control of electrical power networks. The goal of this optimal real-time pricing methodology is to solve the constrained optimization problem consist of each players' utility and social welfare under selfish players. We can show that selfish players' decision can be expressed via a kind of a Nash equilibrium solution considering their own cost functions and it can lead selfish players' decision to social welfare maximization via real-time pricing method. Finally the proposed method is applied to a load frequency control problem of power networks and the effectiveness can be shown via some numerical simulations.

[Technical Meeting]

Honeywell's Control Technologies for Papermaking
Speaker(s): Dr. Michael G. Forbes, Honeywell Process Solutions, Vancouver, Canada
May 20, 2014


Host: IEEE Joint CS/RA/SMC Chapter in Vancouver Section

Time: 12:00:00 - 13:00:00

Place: CHBE202, 2360 East Mall, UBC

Speaker(s): Dr. Michael G. Forbes, Honeywell Process Solutions, Vancouver, Canada

Speaker Bio: Michael G. Forbes, Ph.D., P.Eng. received his B.Sc. degree in the Process Control option of the Mathematics and Engineering program at Queen's University in 1998 and his Ph.D. in Process Control from the University of Alberta in 2003. In 2005 he joined Universal Dynamics, now ANDRITZ Automation, in Vancouver. Michael spent five years with Universal Dynamics working in an applications engineering role in which time he commissioned over 20 advanced control solutions for processes such as mineral processing, plastics manufacturing, and pulp production. In 2010 Michael moved to Honeywell Process Solutions to take the position of Control Research Engineer. Since joining Honeywell, he has worked on advanced solutions for paper machine controls. He is a registered professional engineer in B.C. Canada.

Details: Honeywell is a Fortune 100 company that invents and manufactures technologies to address some of the world's toughest challenges linked to global macrotrends such as energy efficiency, clean energy generation, safety and security, globalization and customer productivity. Honeywell employs approximately 132,000 employees worldwide, of which more than 22,000 are engineers and scientists.

At Honeywell's Vancouver Center of Excellence, over 150 people, including 60 scientists and engineers with advanced degrees, are employed in R&D, sales, and manufacturing of software and hardware products for the pulp and paper, and automotive industries. Paper machine control has a unique set of challenges: relatively long and variable process time delays, hundreds of measurements and actuators, and both machine direction (temporal dynamics) and cross direction (spatial dynamics) components.

This talk will discuss approaches to overcoming these control challenges from the perspective of a leading global industrial technology supplier.

[Distinguished Lecture]

Event-based optimization - a new optimization framework
Speaker(s): Professor Xi-Ren Cao, Shanghai Jiao Tong University, China
August 15, 2013


Host: IEEE Joint CS/RA/SMC Chapter in Vancouver Section

Time: 11:00:00 - 12:00:00

Place: Kaiser 2020/2030, UBC

Speaker(s): Professor Xi-Ren Cao, Shanghai Jiao Tong University, China

Speaker Bio: Xi-Ren Cao is a chair professor of Shanghai Jiao Tong University and an affiliate member of the Institute for Advanced Study at the Hong Kong University of Science and Technology (HKUST). He has worked as a consulting engineer for Digital Equipment Corporation, a research fellow at Harvard University, and a reader, professor, and chair professor at HKUST. He owns three patents in data- and telecommunications and has published three books in the areas of performance optimization and discrete event dynamic systems. Selected honors include being Fellow of IEEE and IFAC and best paper awards from the IEEE Control Systems Society and the Institution of Management Science. He is the Editor-in-Chief of Discrete Event Dynamic Systems: Theory and Applications, and has served as an Associate Editor at Large of the IEEE Transactions of Automatic Control, as a Member of the Board of Governors of the IEEE Control Systems Society, and as a Member on the Technical Board of IFAC.

His current research areas include financial engineering, stochastic learning and optimization, performance analysis of economic systems, and discrete event dynamic systems. He holds a PhD degree from Harvard University.

Details: In many practical systems, such as engineering, social, and financial systems, control decisions are made only when certain events happen. This is either because of the discrete nature of sensor detection and digital computing equipment, or the limitation of computing power, which makes state-based control infeasible due to the huge state spaces involved. The performance optimization of such systems is generally different from traditional optimization approaches, such as Markov decision processes, or dynamic programming.

In this talk, we introduce, in an intuitive manner, a new optimization framework called event-based optimization. This framework has a wide applicability to the aforementioned systems. With performance potential as building blocks, we develop optimization algorithms for event-based optimization problems. The optimization algorithms are first proposed based on intuition, and theoretical justifications are then given with a performance sensitivity based approach. Finally, we provide a few practical examples to demonstrate the effectiveness of the event-based optimization framework. We hope this framework may provide a new perspective to the optimization of the performance of event-triggered dynamic systems.

Slides: Download

[Technical Meeting]

Leader Selection for Performance, Control, and Security of Complex Networks
Speaker(s): Professor Linda Bushnell, University of Washington, USA
June 11, 2013


Host: IEEE Joint CS/RA/SMC Chapter in Vancouver Section

Time: 11:00:00 - 12:00:00

Place: UBC Kaiser 2020

Speaker(s): Professor Linda Bushnell, University of Washington, USA

Speaker Bio: Linda Bushnell is a Research Associate Professor at the University of Washington. She received her Ph.D. in EE and MA in Math from UC Berkeley in 1994 and 1989, and her MS and BS in EE from UConn in 1987 and 1985. Her research interests include networked control systems, leader-follower systems, and secure-control. She received a best paper award from WiOpt 2012. She is the author/co-author of 15 journal papers and 56 conference papers. She is a recipient of the US Government Superior Civilian Service Award, NSF ADVANCE Fellowship, and IEEE CSS Recognition Award. She is a Senior Member of the IEEE. For CSS, she is an Advisor to the Women in Control Committee, a member of the TC Control Education, and Liaison to the IEEE Women in Engineering. She was the Secretary-Administrator, Member of the Executive Committee, Member of the Board of Governors, Associate Editor of the IEEE CSM, Vice-Chair for Invited Sessions for 2001 CCA, Chair of the History Standing Committee, and Vice-Chair for Invited Sessions for 2000 CDC. For AACC, she is currently the Workshop Chair for 2013 ACC and member of the TC on Control Education. She was the Technical Program Chair for 2007 ACC, Publicity Chair for 2005 ACC, Vice-Chair for Publications for 1999 ACC, and Vice-Chair for Invited Sessions for 1998 ACC. For ACM, she was the Technical Program Chair for the Conference on High Confidence Networked Systems (HiCoNS) at CPSWeek 2013.

Details: Control of complex networks, including unmanned vehicle networks, social networks, and biological systems, is an ever-growing challenge. A standard approach is to directly control a subset of leader nodes, which then influence the remaining (follower) nodes. While the choice of leader nodes is known to impact the performance, controllability, and security of complex networks, efficient algorithms for selecting optimal leaders are currently lacking. In this talk, we give an overview of our ongoing work on leader selection in complex networks. We focus on three design criteria, namely, the robustness of the system to noise in the links between nodes, the time for the follower nodes to converge to their desired state, and the controllability to the follower nodes from the leader nodes. We present a unifying framework based on submodularity, a diminishing returns property analogous to concavity of real-valued functions, for studying each of these criteria. Our framework enables efficient leader selection based on the criteria above, with provable guarantees on the resulting system performance. Moreover, we generalize our approach to time-varying networks, including networks with random failures, arbitrary topology variations due to node mobility, and attacks by an intelligent adversary targeting one or more links.

Slides: Download

[Technical Meeting]

Sensor fusion in dynamical systems
Speaker(s): Professor Thomas B. Schön, Linköping University, Sweden
May 29, 2013


Host: IEEE Joint CS/RA/SMC Chapter in Vancouver Section

Time: 11:00:00 - 12:00:00

Place: Kaiser 2020/2030, UBC

Speaker(s): Professor Thomas B. Schön, Linköping University, Sweden

Speaker Bio: Thomas B. Schön is Associate Professor with the Division of Automatic Control at Linköping University (Linköping, Sweden). He received the PhD degree in Automatic Control in Feb. 2006, the MSc degree in Applied Physics and Electrical Engineering in Sep. 2001, the BSc degree in Business Administration and Economics in Jan. 2001, all from Linköping University. He has held visiting positions with the University of Cambridge (UK) and the University of Newcastle (Australia). He is a Senior member of the IEEE. He received the best teacher award at the Institute of Technology, Linköping University in 2009. Schön’s main research interest is nonlinear inference problems, especially within the context of dynamical systems, solved using probabilistic methods. He is active within the fields of machine learning, signal processing and automatic control. He pursues both basic research and applied research, where the latter is typically carried out in collaboration with industry. More information about his research can be found on his website: users.isy.liu.se/rt/schon/researchOverview.html

Details: Sensor fusion refers to the problem of computing state estimates using measurements from several different, often complementary, sensors. Given that the number of available sensors is skyrocketing this technology is likely to become even more important in the future. We will provide our view of the sensor fusion problem in terms of how to attack the problem. Perhaps most importantly we will also illustrate this strategy using four different industrial/research applications, very briefly introduced below.

The four applications are:
1. Real-time pose estimation and autonomous landing of the helicopter (using inertial sensors and a camera).
2. Pose estimation of a helicopter using an already existing map (a processed version of an aerial photograph of the operational area), inertial sensors and a camera.
3. Fighter aircraft navigation (using inertial sensors, radar and a terrain elevation map)
4. Indoor pose estimation of a human body (using inertial sensors and ultra-wideband).

Slides: Download

[Technical Meeting]

Tracking problems in the human-in-the-loop robotics
Speaker(s): Professor Shahram Payandeh, Simon Fraser University, Canada
May 03, 2013


Host: IEEE Joint CS/RA/SMC Chapter in Vancouver Section

Time: 11:00:00 - 11:00:00

Place: Kaiser 2020/2030, UBC

Speaker(s): Professor Shahram Payandeh, Simon Fraser University, Canada

Speaker Bio: Dr. Payandeh is a Professor at the School of Engineering Science at Simon Fraser University in British Columbia, Canada since 1991. He has received his PhD. Degree from the University of Toronto. His main area of research is in robotics and in particular in interaction modeling and coordination of networked, cooperative dynamical agents. He has more than 250 technical publications in journals and conferences. He holds 6 US patents in the field of haptic user interfaces, robotics devices and haptic rendering. He also coauthored the one of the first books in the area of medical robotics and holds one of the first patents in this area. He has publication in visual tracking of surgical tools in using laparoscopic images and developed a surgical training environment for a class of minimally invasive surgery. He is also developing a novel multi-modal surgeon computer interface for accessing information using only the surgical tools as their input devices. More recently, he has been developed a cooperative multi-camera tracking systems for even monitoring and surveillances with application to multi-dynamical agents.

Details: Human-in-the-Loop robotics is an area where robotic systems try to accommodate the feedback information received from the user to continuously re-define the tracking objective of its controller. Examples of such applications can be haptic-aided surgical application environments or visual tracking based on the notion of eye-in-the palm set-up. This talk presents an overview of solutions being investigated in the Experimental Robotics Laboratory of Simon Fraser University.

Slides: Download

[Distinguished Lecture]

On the stabilization of positive switched systems: state of the art and open problems
Speaker(s): Professor Maria Elena Valcher, University of Padova, Italy
June 25, 2012


Host: IEEE Joint CS/RA/SMC Chapter in Vancouver Section

Time: 11:00:00 - 12:00:00

Place: Kaiser 2020/30, UBC

Speaker(s): Professor Maria Elena Valcher, University of Padova, Italy

Details: A positive switched system (PSS) consists of a family of positive state-space models and a switching law, specifying when and how the switching among the various models takes place. PSS's have been adopted for describing networks employing TCP and other congestion control applications, for modeling consensus and synchronization problems, and, quite recently, for describing the viral mutation dynamics under drug treatment. As for the broader classes of hybrid and switched systems, stability and stabilizability properties have been the two major issues that attracted the researchers' attention. The most popular approach to the investigation of stabilizability of PSS's is undoubtedly the one based on common Lyapunov functions or multiple Lyapunov functions. In addition to the standard quadratic and polyhedric positive definite functions, one may resort to the broader class of copositive (linear and quadratic) functions, by this meaning Lyapunov functions that take positive values only on the positive orthant. Also, interesting conditions involving convex combinations of the subsystem matrices can be adopted to characterize stabilizability. In the talk we will provide a comprehensive picture of the stabilizability conditions for stability, and we will point out some open problems.

Slides: Download

[Technical Meeting]

Nonlinear model predictive control: the past, present and future
Speaker(s): Frank Allgöwer, University of Stuttgart, Germany
June 27, 2011


Host: IEEE Joint CS/RA/SMC Chapter in Vancouver Section

Time: 14:00:00 - 15:00:00

Place: McLeod 418, UBC

Speaker(s): Frank Allgöwer, University of Stuttgart, Germany

Speaker Bio: Frank Allgöwer is director of the Institute for Systems Theory and Automatic Control at the University of Stuttgart in Germany. He studied Engineering Cybernetics and Applied Mathematics in Stuttgart and at the University of California at Los Angeles (UCLA) respectively and received his Ph.D. degree from the University of Stuttgart. Prior to his present appointment he held a professorship in the electrical engineering department at ETH Zurich and visiting positions at Caltech, the NASA Ames Research Center, the DuPont Company, the University of California at Santa Barbara and the University of Newcastle in Australia.

Frank’s main interests in research and teaching are in the area of systems and control with emphasis on the development of new methods for the analysis and control of nonlinear systems and networks of systems. Of equal importance to the theoretical developments are practical applications and the experimental evaluation of benefits and limitations of the developed methods. Applications span a wide range from mechatronic systems to systems biology.

At present Frank is Editor for the journal Automatica and for the Springer Lecture Notes in Control and Information Sciences series and serves as Associate Editor and on the editorial board of several further journals. He is on the Council of the International Federation of Automatic Control (IFAC), is on the Board of Governors of the IEEE Control System Society (CSS) and is chairman of the International Affairs Committee of IEEE CSS.

Frank received several recognitions for his work including the appointment as IFAC Fellow, the Landeslehrpreis Baden-Württemberg (state teaching award), and the Leibniz prize, which is the most prestigious prize in science and engineering awarded by the German Deutsche Forschungsgemeinschaft (DFG).

Details: During the past decades model predictive control (MPC) has become a preferred control strategy for a large number of industrial processes. The main reasons for this popularity include the ability to explicitly handle constraints and dynamic nonlinearities, plus the possibility to consider multi-variable processes with potentially many manipulated and controlled variables.

After a discussion of the history and impact of MPC, we will give an overview over some system theoretic approaches on how to achieve stability, robustness and optimal performance and we will comment on the numerical challenges and solution approaches for solving the underlying real-time open loop optimal control problems, that have to be solved repeatedly in MPC. With a number of applications we will demonstrate that by using a proper setup and specially tailored optimization methods, even large problems, having hundreds of states, can be controlled efficiently using nonlinear MPC methods. Finally we will briefly highlight some recent developments in MPC and comment about the future of the field.

Slides: Download