October 17-19, 2012
Fort Lauderdale, FL
Hilton Fort Lauderdale Marina Hotel
Authors: Mr. Yizhai Zhang and Dr. Jingang Yi, Rutgers, The State University of New Jersey
Time: Wednesday, Oct. 17, 3:15 p.m.
Location: Salon D
Abstract: Tire/road interactions play a critical role in safe operation and efficient design of ground vehicles and mobile robots. In this paper, we report the modeling and experimental studies of stick-slip interactions between the tire and the firm road. We first propose a semi-analytical model to predict the normal force distribution on the tire contact patch. A beam-spring network modeling approach is then used to capture and compute the stress and deformation distributions on the contact patch during stick-to-slip transition. To validate the beam-spring network-based contact models and analyses, a new pressure sensitive, electric conductive rubber sensor is embedded inside the tire rubber layer to extract the three-dimensional force distributions on the contact patch. Both the analytical and the experimental results show that the friction force and rubber deformation distributions on the contact patch are dependent on the contact normal force distribution and the tire structural properties. The understanding and modeling of the stick-slip interactions provide new knowledge and methods for studying dynamic tire/road interactions.
Mr. Yizhai Zhang received the B.S. degree and the M.S. degree in information and communication engineering from Xi'an Jiaotong University, China, in 2005 and 2009, respectively. He is currently a Ph.D. candidate in mechanical and aerospace engineering at Rutgers University, Piscataway, NJ, USA. His current research interests include autonomous robotic systems, dynamic systems and control, intelligent sensing and actuation systems, and mechatronics. Mr. Zhang is a student member of ASME and IEEE. He received the Best Student Paper Award at the 2012 IEEE/ASME International Conference on Advanced Intelligent Mechatronics.
Professor Jingang Yi is an Assistant Professor in mechanical and aerospace engineering at Rutgers University, Piscataway, NJ, USA. His research interests include robotics, mechatronics, dynamic systems and control, and automation science and engineering, with applications to biomedical systems, civil infrastructure and transportation systems. Dr. Yi is a member of ASME and a senior member of IEEE. He is a recipient of the 2010 US NSF CAREER Award. His research have also been recognized by several best paper awards at major robotics/automation and mechatronics conferences. He currently serves as an Associate Editor for the IEEE Transactions on Automation Science and Engineering.
Authors: Dr. Tulga Ersal, University of Michigan; Dr. Mark Brudnak, U.S. Army Tank Automotive Research Development and Engineering Center; and Dr. Jeffrey L. Stein, University of Michigan
Time: Wednesday, Oct. 17, 3:15 p.m.
Location: Salon E
Abstract: The possibility of integrating geographically dispersed hardware-in-the-loop systems over the Internet and co-simulating them in real time holds a significant potential for changing the way we collaborate and analyze, design, evaluate, and validate complex engineering systems. Fully realizing the benefits of this vision requires addressing many challenges within the field of dynamic systems and control, including ensuring stability and fidelity despite the network delays. This talk will briefly summarize the past efforts in this area, highlight the recent developments in terms of using iterative learning control to improve fidelity, and point out potential directions for future research.
Tulga Ersal received the B.S.E. degree from the Istanbul Technical University, Istanbul, Turkey, in 2001, and the M.S. and Ph.D. degrees from the University of Michigan, Ann Arbor, MI, in 2003 and 2007, respectively, all in mechanical engineering. He is currently an Assistant Research Scientist in the Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI. His research interests include modeling and control of dynamic systems, model order and structure reduction, multibody dynamics, Internet-distributed hardware-in-the-loop simulation, and biomechanics.
Mark Brudnak received his B.S. degree in electrical engineering from Lawrence Technological University, Southfield, Michigan in 1991, his M.S. degree in electrical and computer engineering and his Ph.D. degree in systems engineering from Oakland University, Rochester, Michigan in 1996 and 2005, respectively. Dr. Brudnak is an Associate Director at the U.S. Army Tank Automotive Research Development and Engineering Center (TARDEC), Warren, MI, which is a component of the Research Development and Engineering Command (RDECOM). In this capacity he oversees the operation of laboratories for durability testing, vehicle characterization and human-in-the-loop motion base simulation. His research interests include integration of distributed hardware-in-the-loop systems, the control and modeling black-box dynamical systems, support vector machines, machine learning and multi-body dynamics.
Jeffrey L. Stein received the B.S. degree in premedical studies from the University of Massachusetts, Amherst, MA, in 1973, and the S.B., S.M, and Ph.D. degrees in mechanical engineering from the Massachusetts Institute of Technology, Cambridge, MA, in 1976, 1976, and 1983, respectively. Since 1983 he has been with the University of Michigan, Ann Arbor, MI, where he is currently a Professor of Mechanical Engineering. His research interests include computer based modeling and simulation tools for system design and control, with applications to vehicle-to-grid integration, vehicle electrification, conventional vehicles, machine tools, and lower leg prosthetics. He has particular interest in algorithms for automating the development of proper dynamic mathematical models, i.e., minimum yet sufficient complexity models with physical parameters.
Authors: Dr. Matteo Massaro, University of Padova; Dr. David J. Cole, University of Cambridge
Time: Thursday, Oct. 18, 3:00 p.m.
Location: Salon D
Abstract: The neuromuscular dynamics affect the response and stability of the driver-vehicle system. This talk addresses the most recent findings on driver steering neuromuscular properties, both for two-wheeled vehicles and four-wheeled vehicles. An analysis of the effect of neuromuscular dynamics on the stability of the driver-vehicle system is discussed. Finally, ongoing research at the University of Padova and University of Cambridge will be described.
Dr. Matteo Massaro: MScME and PhD from the University of Padova in 2005 and 2009 respectively. Research fellow since 2009. Scholarship granted by Aprilia (IT) in 2005. Internship at Harley-Davidson (US) in 2008. Visiting researcher at the University of Cambridge (UK) in 2012. He cooperated with several industrial partners. His research focuses on vehicle dynamics and control, virtual drivers, multibody modelling and control of mechanical systems.
Dr. David Cole received his first degree in Engineering from the University of Cambridge in 1985. After a short period at Rolls Royce Motors Ltd. working on ride vibration, he returned to Cambridge to work on heavy vehicle dynamics and was awarded the Ph.D. degree in 1990. Further work on heavy vehicle dynamics at Cambridge was followed by a lecturing appointment at the University of Nottingham from 1996 to 2000. Since returning to Cambridge in 2000 he has been working in the field of driver-vehicle dynamics. Dr. Cole is a fellow of the Institution of Mechanical Engineers.
Authors: Dr. Scott Moura, University of California, San Diego; Dr. Nalin Chaturvedi, Bosch Research and Technology Center; Prof. Miroslav Krstic, University of California, San Diego
Time: Thursday, Oct. 18, 3:00 p.m.
Location: Salon E
Abstract:Currently, batteries are oversized and conservatively operated. Advanced estimation and control can unlock their full potential. That is, real-time state information and optimization enables operation near the physical limits without compromising longevity. This feature is a crucial step towards catalyzing the adoption of energy storage in electrified transportation and smart grids.
In this talk, we focus on the state-of-charge (SOC) and state-of-health (SOH) estimation problem. We cast this problem as a simultaneous state (SOC) and parameter (SOH) estimation design. Unlike most approaches, our design utilizes electrochemical models, thus enabling us to directly estimate the evolution of several electrochemical phenomena. Our solution integrates several recently developed concepts in PDE control theory, adaptive estimation, and battery model reduction. We will also explore how particular state estimates can be exploited to reduce charge time, enhance energy capacity, and protect against harmful degradation processes.
Dr. Scott Moura is a Postdoctoral Fellow at the University of California, San Diego. He received the Ph.D. degree from the University of Michigan in 2011 in Mechanical Engineering. Dr. Moura is a recipient of the UC Presidential Postdoctoral Fellowship, National Science Foundation Graduate Research Fellowship, and College of Engineering Distinguished Leadership Award. He has been nominated for the Best Student Paper Award at the 2009 ASME Dynamic Systems and Control Conference and 2011 American Control Conference. His research interests include optimal & adaptive control, PDE control, energy conversion systems, smart grid systems, and batteries.
Nalin A. Chaturvedi received the Ph.D. degree in Aerospace Engineering from the University of Michigan, Ann Arbor in 2007. He is currently with the Research and Technology Center of the Robert Bosch Corporation, Palo Alto, CA. His current interests include the development of model-based control for battery management systems, nonlinear control, state and parameter estimation, and adaptive control. He is a member of the Energy Systems subcommittee of the ASME Dynamic Systems and Controls Division and an associate editor on the IEEE CSS Editorial Board and the Journal of Aerospace Science and Technology.
Miroslav Krstic holds the Alspach chair and serves as Associate Vice Chancellor for Research and founding director of the Cymer Center for Control Systems and Dynamics at UC San Diego. He is a Fellow of IEEE and IFAC, a recipient of PECASE, NSF Career, and ONR YI Awards, the Axelby and Schuck Paper Prizes, and the UCSD Research Award. He has held the Russell Severance Springer Distinguished Visiting Professorship at UC Berkeley. Krstic serves as Senior Editor in IEEE Transactions on Automatic Control and Automatica. He has has coauthored nine books and over 170 journal papers.
Authors: Chiao-Ting Li, Xiaowu Zhang, and Huei Peng, University of Michigan
Time: Friday, Oct. 19, 2:45 p.m.
Location: Salon D
Abstract:This paper presents a systematic design methodology for split hybrid vehicles using a single planetary gearset (PG) as the transmission. The design methodology consists of four steps: 1) analyze clutch locations on the PG and operation modes, 2) generate dynamic models, 3) evaluate drivability (acceleration performance) via forward simulations, and 4) optimize the fuel economy using the dynamic programming technique. The 1-PG split hybrid transmission can have 12 configurations, and each configuration can have four operation modes when three clutches are added. This methodology systematically evaluates all configuration candidates and identifies the optimal design, and we demonstrate how it design helps to identify a simplified design based in the output-split configuration used by the Chevy Volt without sacrificing much performance. The simplified design, named the Volt-, has only two of the four operation modes of the original Volt. The Volt- achieves the same fuel economy as the original Volt in the FUDS cycle, and has slightly reduced drivability and fuel economy in the HWFET cycle. In addition, an improved design based on the input-split configuration used by the Toyota Prius is also identified, named the Prius+, which has one additional mode than the original Prius. The Prius+ outperforms the Prius in both drivability and fuel economy.
Dr. Huei Peng received his Ph.D. from the University of California, Berkeley in 1992. He is currently a Professor at the Department of Mechanical Engineering and the Executive Director of Interdisciplinary and Professional Engineering, at the University of Michigan, Ann Arbor. His research interests include adaptive control and optimal control, with emphasis on their applications to vehicular and transportation systems. His current research focuses include design and control of hybrid electric vehicles and vehicle active safety systems.
He is a leading researcher at the University of Michigan Automotive Research Center, and was involved in the design of several military and civilian concept vehicles, including FTTS, FMTV, and Super-HUMMWV. His team designed the power management algorithm for a prototype hybrid electric vehicle designed by Eaton, which later becomes the basis for their commercial hybrid buses and trucks. Thousands of units have been sold worldwide. He has more than 190 technical publications, including 80 in referred journals and transactions.
Dr. Peng has been an active member of the Society of Automotive Engineers (SAE) and the ASME Dynamic System and Control Division (DSCD). He served as the chair of the ASME DSCD Transportation Panel from 1995 to 1997, and is a member of the Executive Committee of ASME DSCD. He served as an Associate Editor for the IEEE/ASME Transactions on Mechatronics from 1998-2004 and for the ASME Journal of Dynamic Systems, Measurement and Control from 2004-2009. He received the National Science Foundation (NSF) Career award in 1998. He is an ASME Fellow.
Authors: Myoungkuk Park, Texas A&M University; Krishnamoorthy Kalyanam, Infoscitex Corporation; Swaroop Darbha, Texas A&M University; Pramod Khargonekar, University of Florida; Phillip R. Chandler, Air Force Research Laboratory; Meir Pachter, Air Force Institute of Technology
Time: Friday, Oct. 19, 2:45 p.m.
Location: Salon E
Abstract:This talk deals with the development of sub-optimal decision making algorithms for a collection of robots in order to aid a remotely located human operator in the task of classification of incursions across a perimeter in a surveillance application. The operator is tasked with the classification of incursion as either a nuisance or a threat. Whenever there is an incursion into the perimeter, Unattended Ground Sensors (UGS) raise an alert and the robots service the alerts by visiting the alert location and collecting evidence in the form of video and other images and transmit them to the operator. There are two competing needs for a robot: it needs to spend more time at an alert location for aiding the operator in accurate classification and it needs to service the alerts as quickly as possible so that the evidence collected is relevant. A natural problem is to determine the optimal amount of time a robot must spend servicing an alert. In this paper, we discretize the problem spatially and temporally and recast the optimization problem as follows: Is it better for a robot to spend the next time interval at the alert location in terms of maximizing the expected, discounted payoff? The payoff associated with a state is an increasing function of the time spent by a robot servicing an alert and a decreasing function of the number of unserviced alerts.
This problem can be easily be cast as a Markov Decision Process (MDP). However, the number of states runs into billions even for a modest size problem. We consider Approximate Dynamic Programming via linear programming as this approach provides an upper (and lower) bound on the optimal expected discounted payoff and enables the construction of a suboptimal policy. The bounds may then be used to provide an estimate of the quality of sub-optimal policy employed. We also provide a computationally tractable way of computing the lower bound using linear programming. Finally, numerical results supporting our method are provided.
Myoungkuk Park received B.S. degree in Mechanical Engineering from Kyunghee University in 2002 and M.S.degree in Mechanical Engineering from Korea University in 2004. He is currently a graduate student pursuing Ph.D degree of mechanical engineering department at Texas A&M university. His research interests are control systems, with applications to robotics and UAVs, and large scale stochastic dynamic programs.
Dr. Krishnamoorty Kalyanam received the B.Tech. degree in Mechanical engineering from the Indian Institute of Technology, Madras in 2000, and the M.S. and Ph.D. degrees in Mechanical engineering from The University of California at Los Angeles, in 2003 and 2005, respectively. In Oct 2005, he joined G.E. Global Research in Bangalore, India as a Research Engineer, where he worked on Train optimal control and Wind Farm Layout Optimization. In July 2009, he moved to the U.S. Air Force Research Laboratory (AFRL) as a National Research Council sponsored Research Associate. In May 2011, he moved to the InfoSciTex Corporation in 2011 and is currently a in-house cooperative control research scientist at the Control Design & Analysis branch at AFRL, Wright-Patterson Air Force base, Ohio. His current research interests are in the cooperative control of autonomous air vehicles and the underlying stochastic optimal control methods.
Dr. Swaroop Darbha received his Bachelor of Technology from the Indian Institute of Technology - Madras in 1989, M. S. and Ph. D. degrees from the University of California in 1992 and 1994 respectively. He was a post-doctoral researcher at the California PATH program from 1995 to 1996. He has been on the faculty of Mechanical Engineering at Texas A&M University since 1997, where he is currently a professor. His current research interests lie in the development of diagnostic systems for air brakes in trucks, development of planning, control and resource allocation algorithms for a collection of Unmanned Aerial Vehicles.
Dr. Pramod Khargoenkar has worked at the Universities of Florida, Minnesota, and Michigan. He was Chairman of the EECS Department and held the Shannon Chair at Michigan. He was Dean of Engineering and holds Eckis Professorship at Florida. He is a recipient of the NSF Presidential Young Investigator Award, the American Automatic Control Council’s Eckman Award, the IEEE Baker Prize Award, the IEEE CSS Axelby Best Paper Award, the Hugo Schuck ACC Best Paper Award, and a Distinguished Alumnus and Distinguished Service Award from IIT Bombay. He is a Fellow of IEEE and is a Web of Science Highly Cited Researcher.
Dr. Meir Pachter is a Professor of Electrical Engineering at the Air Force Institute of Technology, Wright-Patterson AFB. Dr. Pachter received the BS and MS degrees in Aerospace Engineering in 1967 and 1969 respectively, and the Ph.D. degree in Applied Mathematics in 1975, all from the Israel Institute of Technology. Dr. Pachter held research and teaching positions at the Israel Institute of Technology, the Council for Scientific and Industrial Research in South Africa, Virginia Polytechnic Institute, Harvard University and Integrated Systems, Inc. Dr. Pachter is interested in the application of mathematics to the solution of engineering and scientific problems. His current areas of interest include military operations optimization, dynamic games, cooperative control, estimation and optimization, statistical signal processing, adaptive optics, inertial navigation, and GPS navigation. For his work on adaptive and reconfigurable flight control he received the AF Air Vehicle’s Directorate Foulois award for 1994, together with Phil Chandler and Mark Mears. Dr. Pachter is a Fellow of the IEEE.
Phil Chandler is the tech advisor for the Control Design & Analysis branch of the Air Force Research Lab (AFRL), Wright-Patterson Air Force Base. He received his B.S. and M.S. degrees from Wright State University. He was the principal architect of the Self Repairing Flight Control System advanced development program and recipient of the prestigious General Foulois award in 1994. He is currently researching autonomous control algorithms for unmanned combat aerial vehicles.
Authors: Chiao-Ting Li, Xiaowu Zhang, and Huei Peng, University of Michigan
Chiao-Ting Li (adviser: Huei Peng) received her B.S. and M.S. degrees in mechanical engineering from National Taiwan University, Taiwan, in 2004 and 2006, respectively. She is currently a Ph.D. candidate in mechanical engineering at the University of Michigan, Ann Arbor, USA. She currently works on control integration for plug-in electric vehicles and renewable energy sources on electric grid. Her research interests include design and control of hybrid electric vehicles, smart grid systems, and vehicle-to-grid applications.
Authors: Nima Fazeli and Dr. Jin-Oh Hahn, University of Maryland (as of FA 2012); Dr. Hee-Chan Kim, Seoul National University
Nima Fazeli (adviser: Jin-Oh Hahn) received his B.Sc. degree with honors in Mechanical Engineering specializing in Control Engineering from Amirkabir University of Technology, Tehran, Iran in 2011. He has been pursuing his M.S. degree in Mechanical Engineering since 2012 at University of Alberta, AB, Canada (January 2012-August 2012) and at University of Maryland, College Park, MD (September 2012-present). His research interests include control systems with applications to biomedicine and biomedical engineering, medical devices and automotive systems.
Abstract: This paper presents a novel active non-intrusive system identification (SYSID) approach to cardiovascular monitoring. The proposed approach uses a dual blood pressure cuff as an actuator as well as a pressure transducer for cardiovascular SYSID. In this paradigm, the dual blood pressure cuff excites the cardiovascular system to create rich trans-mural pressure waves traveling in the cardiovascular system, which are in turn measured via cuff pressure oscillations. Mathematical model was developed to describe the propagation of arterial and cuff excitation pressure waves in the cardiovascular system, which was subsequently used to study the effect of cuff maneuvers on trans-mural pressures and also to develop a methodological framework to reconstruct trans-mural pressure waveforms from cuff pressure oscillation measurements. This paper successfully demonstrated that 1) cardiovascular system can be excited non-intrusively via active cuff maneuvers, and 2) arterial and trans-mural pressure waveforms can be reconstructed accurately by judiciously processing cuff pressure oscillations.
Authors: Mahdi Jadaliha, Yunfei Xu, and Jongeun Choi, Michigan State University
Mahdi Jadaliha (adviser: Jongeun Choi) is pursuing his Ph.D. in Mechanical Engineering from Michigan State University from 2010. He also received his M.S. and B.S. degrees in Electrical Engineering from Sharif University and Iran University of Science & Technology, Tehran, Iran, in 2005 and 2007, respectively. Between 2005 and 2009, He was a Research Fellow with the Mechatronics Research LAB (MRL) at Azad University, Qazvin, Iran, and he obtained 3 robotics championships as part of the MRL team in the Robocup rescue robot leagues. His current research interests include parallel processing using GPU, robotic localization algorithms, environmental adaptive sampling algorithms, Gaussian processes, and statistical learning algorithms with applications to robotics and mobile sensor networks.
Abstract: In this paper, we develop efficient spatial prediction algorithms using Gaussian Markov random fields (GMRFs) under uncertain localization and sequential observations. We first review a GMRF as a discretized Gaussian process (GP) on a lattice, and justify the usage of maximum a posteriori (MAP) estimates of noisy sampling positions in making inferences. We show that the proposed approximation can be viewed as a discrete version of Laplace's approximation for GP regression under localization uncertainty. We then formulate our problem of computing prediction and propose an approximate Bayesian solution, taking into account observations, measurement noise, uncertain hyperparameters, and uncertain localization in a fully Bayesian point of view. In particular, we present an efficient scalable approximation using MAP estimates of noisy sampling positions with a controllable tradeoff between approximation error and complexity. The effectiveness of the proposed algorithms is illustrated using simulated and real-world data.
Authors: Andreas Hansen, Hamburg University of Technology; Mahdi Shahbakhti and J. Karl Hedrick, University of California, Berkeley
Andreas Hansen (adviser: Karl Hedrick) started his engineering coursework in 2007 and studied in his hometown of Hamburg, Germany. He first gained academic attention through winning the Jungheinrich Prize in 2010 for his outstanding design of a mechanical gear box. Furthermore, in 2010 he earned his Bachelor's degree from Hamburg University of Technology. He finished best of his year and was awarded the Buxmann Award by the local Rotary Club. Mr. Hansen’s primary research emphasis entails studying the fields of energy systems and controls which he pursues in his current Master’s degree program. In 2011, Mr. Hansen fulfilled a lifelong dream and traveled to the USA to study at the University of California, Berkeley for a yearlong exchange program. While in Berkeley he worked with Prof. J. K. Hedrick and conducted research in the field of automotive IC engine control. Currently Mr. Hansen is completing his Master's degree and plans on pursing a PhD program. Mr. Hansen is passionate about dance choreography. His talent for this artistic expression is demonstrated through his win at the world championship of the International Dance Organization in 2007. In addition he was invited to Tokyo to dance for Prince Mikasa of Japan in 2009. While volunteering with the YMCA, Mr. Hansen realized his interest in youth work and subsequently started to teach dance classes for adolescents. For the past five years Mr. Hansen has taught dance classes for adolescents in Germany. While living in California Mr. Hansen also taught dance classes to the UC Berkeley community.
Abstract: Imprecisions from controller software implementation can substantially affect a controller’s performance for leading a physical plant to a desired behavior. In this paper, a discrete-time quasi-sliding mode approach is used to design a controller with improved robustness to software implementation imprecisions. The application of the controller is illustrated for automotive cold start hydrocarbon emissions. The designed controller shows more robustness with respect to implementation imprecisions including sampling, quantization and fixed-point arithmetic when compared to its continuous-time counterpart. In particular, the influence of the sampling time is investigated and a controller configuration with minimal computational requirements is proposed. Finally, the performance of the new controller is verified with real-time simulations.