1

NSF Workshopon the

Integration of Modeling and Control for Automotive Systems

June 5-6, 1999

Center for Control Engineering and ComputationMechanical and Environmental Engineering Department

University of California, Santa Barbara

2

This report was prepared by:Anna Stefanopoulou, University of California, Santa BarbaraAndrew Alleyne, University of Illinois, Urbana ChampaignRoy Smith, University of California, Santa BarbaraKarl Hedrick, University of California, BerkeleyGiorgio Rizzoni, Ohio State University

The workshop took place in June 5-6, 1999, at UCSB under the auspices of the Center for ControlEngineering and Computation (CCEC). This report serves as the Workshop proceedings and the authors are solelyresponsible for its content. We have tried to convey the general spirit of the workshop based on the input receivedfrom the participants. However, the conclusions and recommendations contained in this report are not the officialinput of either of the workshop participants, the National Science Foundation, the IEEE Control Systems Society, orthe ASME Dynamic Systems and Control Division.

Acknowledgements:The NSF/CCEC Workshop on Integration of Modeling and Control or Automotive Systems was made

possible by the National Science Foundation Grant CMS 9906020. We would like to thank Dr. Alison Flatau and Dr.Kishan Baheti of the National Science Foundation whose encouragement and general support made it possible. Dr.Flatau and Dr. Baheti provided not only the financial support to hold the workshop but also gave valuable feedbackand advice before the workshop to ensure a smooth and successful event.

The organizers are thankful to the members and technical staff of the Center for Control Engineering andComputation (CCEC), UCSB for their help with the workshop logistics. The assistance offered by the CCEC, intaking care of local arrangements was invaluable to keeping things running smoothly. Special thanks go to ChristinaLung; her assistance was invaluable. We would like to thank the international advising committee for their guidanceand vision that many times energized us. We also thank the industrial participants for their refreshing perspective onthe interplay between modeling and control. Their experience helped to clarify the needs and to pose the rightquestions. Finally, a big "thank you" goes to the student participants in the poster sessions. Their technical, and atthe same time, artistic presentations filled the Workshop space in the Pavilion with energy, enthusiasm and vibrantconversations.

Additional workshop information may be found at: http://www.engineering.ucsb.edu/~anna/wrkshop.

Participants enjoy the California sunshine at the end of the workshop!

3

Contents Page

Acknowledgements 2

1. Executive Summary 4

2. Introduction & Motivation 5

3. Challenges 5

4. Workshop Structure 6

5. Current Practices 7

6. Problem Classification 8

7. Insight Gained From Specific Examples 10

8. Complexity and Scalability 12

9. Formal Integration Feasibility 14

10. Educational Issues 15

11. Conclusions 17

Appendices

4

1. Executive Summary

In the last two decades control has had an undeniable impact on the improvement of automotive systems. Asautomotive systems become increasingly complicated the model development, analysis, controller design,optimization and verification becomes a challenging task. The lack of data, the interdisciplinary nature, and theexistence of multiple layers of modeling and controller details (all the way from distributed to discrete eventsystems) make the integration of modeling and control a critical step towards system development.

The workshop focus was on the development of a more systematic connection between modeling, analysis andcontrol design. Such an integration will allow the use of control theory early on in the design cycle of an automotivesystem. A total of 50 academic, 17 industrial, and 18 students participated. A significant number of participants (12)came from outside of the U.S., making this event an international meeting. In the two days of the workshop weconsidered specific examples of control problems, covering several areas of automotive systems which requiredifferent levels of modeling accuracy and abstraction. Examples were used as paradigms where the gaps betweenmodeling and control design were bridged or could not be bridged. The experiences and approaches used to solveprevious problems in this field were drawn upon to identify key aspects that were common to the transition betweencontrol, analysis, design, and verification.

Throughout the presentations and discussion sessions it was easy to distinguish two paradigms where modelingwas critical to the control development. The first paradigm captures the needs during the development of advancedand innovative systems where no prior experience exists. This is the case where the control objective is not wellunderstood and the input-to-output relationship is not well specified. In this case industry recognizes that controldevelopment requires serious modeling effort. The following sentence from Dr. Tabaczinsky's keynote addressexemplifies this need: "It takes time to understand the control problem, and understanding arises from good modelsthat can only be developed when the control engineer works side-by-side with the engine and the vehicle designer."It is thus, clear that the multidisciplinary involvement is the most crucial requirement in this case. This compellingneed can only be satisfied if (i) the control community recognizes and awards such multidisciplinary efforts, and (ii)the curriculum provides initiatives and develops such an involvement. NSF is encouraged to sponsor research effortsand more workshops on integration of modeling and control on other multidisciplinary areas.

The second paradigm addresses the improvement of existing controlled processes in automobiles, or control re-design after small modifications in an existing system. In this case the challenging task is to devise methods andtools that can facilitate the iterations in the control and component design. It was agreed that there is a research needfor an engineering ‘tool’ or methodology that can integrate design processes, i.e., pass and maintain information toall stages of modeling and control design. If such a tool could be developed, and standardized across differentplatforms, this would greatly aid the current product design cycle.

The need for a unified forum for the dissemination of information on the newest developments in models andintegration tools was recognized in this workshop. The control researchers involved with automotive applicationscan be found in multiple societies, and frequently the results are disseminated via a variety of avenues. To addressthis need Professors J. W. Grizzle from the University of Michigan and L. Glielmo from the Universita di NapoliFederico II developed a web-site and a monthly electronic newsletter that will serve as a forum for communicationabout Automotive Control Systems (http://143.225.169.32/automotive).

The important role of education in bridging the gap between modeling and control development has been alsoemphasized during the discussion sessions. Courses and laboratories that include design projects motivated fromindustrial problems are very motivating. Furthermore, the classical undergraduate control courses should be enrichedwith modeling, identification, and experimentation principles. Industry is encouraged to offer more summerinternship and support visits and lectures to control engineering departments.

The experts invited in this workshop have demonstrated a long involvement and lasting contributions inautomotive problems where modeling was a critical step towards a successful control design This group wasintentionally formed and selected so that there are no strong advocates of particular methodologies in the integrationof modeling with control design. We recommend that NSF supports follow-up workshops with invitees from otherindustries to determine the key aspects of this workshop that are truly universal across fields. We then stronglyencourage the NSF, and other governmental organizations to support the research directions that have beenidentified as necessary and relevant.

5

2. Introduction and Motivation

The application of automatic control concepts to current products and systems has increased dramaticallyover the past several decades. The goal of this workshop was to assemble researchers from academia and industryto examine a key aspect of the entire Control Engineering paradigm. This aspect is the Integration of Modeling andControl. However, since the field of Control Engineering is incredibly broad, to have any type of meaningfuldiscussion or analysis during a brief period required the focusing of the workshop to consider the integration aspectswithin the context of a very specific subfield. The subfield chosen was Automotive Systems. Automotive Systemswere chosen to provide structure to the workshop for two primary reasons. First of all, they have an undeniableimpact on modern society. Automotive transportation systems are at the heart of any industrialized society therebydemonstrating a large measure of importance. Any improvements that could be made to these systems would affecta large segment of the population. Secondly and perhaps more importantly, there have been clearly demonstratedsuccesses of control systems throughout all facets of the automotive arena over the last quarter century. Controlshave aided in the automation of the manufacturing processes thereby reducing costs and decreasing cycle times.They have been an enabling technology in emissions control which has had a tremendous environmental impact onsociety. Controls have also enabled automotive manufacturers to provide customers with significant performance(e.g. Cruise Control) and safety (e.g. ABS) improvement at reasonable costs. For these main reasons, theautomotive focus was felt to be necessary and appropriate.

Every control problem encompasses thefollowing stages: (i) modeling, (ii) analysis, (iii)control design, and (iv) testing through bothsimulation and experimental implementation. Thesestages are usually iterated throughout an overallsystems design cycle. However, it was realized bythe workshop organizers that current practices haveeach of these stages developed individually andindependently. In this workshop we focused on thedevelopment of a more systematic connectionbetween modeling, analysis and control design. Atthis outset, it was acknowledged that this was a verychallenging task and so much of the workshop wasspent on identifying research needs within a specifiedframework.

Individual case studies were presented and used as motivational points from which to launch discussion andexamination of the problem. By starting with specific examples drawn from the automotive arena it was possible toexamine success and failures with respect to Modeling and Control integration. Through this concurrentexamination of several specific cases, some common themes emerged. These included ways in which to formalizethe integration process as well as areas where the integration is lacking for more fundamental reasons. The contentsof the following report are taken largely from the discussions among the presenters and attendees of the two-dayworkshop. It is our hope that this information can be of value to current, as well as future Control Engineers.

3. Challenges

To illustrate the difficulty that control engineers face in developing controllers for advanced systemsconsider the following modeling examples. Mixture formation in gasoline direct injection engines (GDI) requires theanalysis of a spatially distributed system described by partial differential equations. Valve flow control for camlessengines requires crankangle-based identification and multicylinder representation of the flow pattern. Point-wise intime emission control in catalytic converters depends on the accurate modeling of the global nonlinear time varyingdynamic behavior of the catalysis.

Discrete event system modeling is required in optimizing traffic flow. Some concepts of automatedhighways rely on hierarchical system representations as well as distributed PDE models. Advanced vehicle stabilitysystems rely on highly nonlinear representations with rapidly varying and uncertain parameters. Even simplesystems such as tires can use either a mechanistic brush-type model or an empirical model fit to experimentally

6

determined parameters. Finally, engine and vehicle controller validation through a hardware in the loop environmentrequires the development of real-time computer models. Clearly, there are many different types of automotivesystems that require modeling in order to do controller design. Additionally, there are several different modelingmethodologies that are applicable. These range from first principles based models that rely on physics andchemistry to Input-output identification based models to discrete-event systems for the implementation and softwareintegration.

Similar to the number of model types and modeling techniques, there is an equally impressive array ofmodern control and analysis tools available to the astute control engineer. SISO frequency domain controllers workfor many different automotive systems, including idle speed control and cruise control. There is also an impressivearray of multivariable tools ranging from simple state feedback to H-infinity and l-1 optimal control for linearsystems to Lyapunov-based methods for nonlinear systems. Also to be included are the intelligent control methodsconsisting of Neural Networks and Fuzzy Logic and there are new control paradigms constantly emerging from thecommunity in both the identification and control areas. Clearly, there are a large variety of tools available to tacklecurrent and future control problems.

The key questions that this workshop focused on are:• How to derive and manage models for control analysis, design, optimization and verification?• How to choose the appropriate analysis/control tool for a particular system given an existing modeling

approach?It is recognized that the above questions are addressed on a case-by-case basis. The success of these approaches isusually directly linked to the resident engineering expertise and experience available. However, we believe thatgood engineering judgment should be aided by system-theoretic concepts and during the workshop we consideredmore broadly the question of a seamless integration between modeling and control design. Such an integration willallow the use of control theory early on in the design cycle of an advanced system.

4. Workshop Structure

The working examples, presentations, and panel discussions were organized in four areas in a cascadearrangement: from the level with the most detailed physical models (that define the low level control algorithms) tothe abstract component and event modeling (that define the higher level controllers):

• Engine and Powertrain Control: Featuring problems in flow, combustion, and emission control for novelengine processes. We addressed control critical powertrains that require detailed physical models of spatialflow patterns, mixing, and chemical reactions.

• Vehicle Dynamics and Control: Featuring problems related to sensor and actuator limitations, human factors,and safety. We also addressed issues related to intelligent vehicle highway systems.

• Implementation and Verification: Featuring problems implementation of control algorithms and the need forverification tools, and fault tolerant mechanisms. Here the representations are databases of componentcharacteristics, finite state machines, real time simulations and hybrid systems.

• Education: Efforts to enrich the control engineering curriculum and develop research and graduate educationprograms with emphasis on Advanced Automotive Technologies were presented. Also, the role of relationshipsbetween industry and universities was explored.

Throughout the duration of the workshop the participants ``stepped back'' from their individual work andmeaningfully engaged in the examination of a broader picture in order to distill what is fundamental to theModeling-Control interface. The presentations can be found in the workshop web page. The following sectionsprovide a summary of the discussion sessions.

7

Professor Ackermann (DLR) listens Professor Krogh (CMU) lectures

5. Current Practices

For this topic two separate breakout discussions were held; one on each day.

Group One: Engr II, Conference Room 2301 (MEEDept.) Sat. 3:30-5:00 PM

Facilitators: H. Khalil & Le Yi WangParticipants: Ackermann, Alleyne, Bailey, Bamieh,M.F. Chang, S. Chin, Cikanek, Dahleh, Diop,Freudenberg, Yanakiev

Group One Engr II, Conference Room 2301 (MEEDept.) Sun. 2:00-3:00 PM

Facilitators: J. K. Hedrick & A. StefanopoulouParticipants: Ford, von Nieustandt, Glielmo,Kolmanovsky, Tabaczynski, Teel, Kotwicki, Wang,Yanakiev, Smith

Working Session Topics and Issues for Discussion:• How does one derive models for control analysis, design, optimization and verification?• How to choose the appropriate analysis/control tool for a particular system given an existing modeling

approach?• How to incorporate the modeling, analysis and control developments at the initiation of a problem?

o An analogy would be Design for Manufacturing.

Discussion Summary:

A summary of current practices within academia and industry was given as follows:

MODELING ANALYSIS ORSYNTHESIS

VALIDATION(EXPERIMENTAL/SIMULATION)

ACADEMICPRACTICES

Simple Rigorous Simple

INDUSTRYPRACTICES

As muchaccuracy as

possible

Empiricism Exhaustive

The control academic community often relies on collaboration with researchers and practicing engineers in R&DLabs to develop models for control analysis and design. Much of the time verification is carried out in industry.Unfortunately the process is not seamless and usually is performed by different groups with not much interactionand a lot of effort duplication. The discussion groups focused on Current Practices were quite heavily populatedwith industrial participants in addition to the academic attendees. This led to a very valuable discussion on therelative merits of modeling and control as well as the need for an integration of the two.

8

In industry there are two types of models developed in parallel: (I) control-oriented models which aresimple, structured around the control objective, and can be used in stability proofs, optimization, on-line estimation,and observer design. (II) models with high accuracy, fidelity, and complexity that can be used for computer aideddesign (CAD), control calibration, verification, and hardware in the loop (HIL) tests. One of the key industrialaspects of the modeling and integration problem was the financial and business issues that often go unnoticed inmany academic practices. Automotive companies usually rely on what are termed ‘legacy’ models. That is, modelsthat have been previously developed for a particular process or system. Since it is expensive to develop new systemmodels from scratch there is a strong financial incentive to re-use models that have already been developed.Oftentimes though, models are developed using different software tools that can lead to modeling platformmismatches. This greatly hinders the industrial ability to easily integrate the modeling and control aspects. Much ofthe effort dedicated to control system design is spent in coordinating the existing legacy models.

The computer simulation tools that have been developed in recent years have greatly aided a rapidturnaround in evaluating different models as well as the effect of parameter changes within different models.Additionally, Computer Aided Control System Design (CACSD) tools have come along and make the designprocess much more tractable. Therefore, several of the industrial participants felt that detailed, theoretical controlanalysis and synthesis may have been more valuable when the computer tools were more limited. This may not bethe case currently. In fact, several of the industrial researcher participants claimed that a majority of controlproblems in industry involve the development of a thorough understanding of the particular system underexamination and coming up with a ‘reasonable’ solution. In short, the modeling aspect was given priority for themsince they felt that if they were armed with the system understanding, a control solution was readily apparent. Thereasoning for this was that oftentimes, industrial scale problems contain too many variables to perform a closed formsystem analysis. It was agreed by many of the industrial participants that this was the same for other industriesincluding Aerospace.

Despite the advanced CACSD tools mentioned above, there is still neither a concise methodology norguidelines on how to choose control analysis/design approach for a process that has been represented in a CFD or aCAD environment. Existing control tools are limited and can be applied to small subsystems, thus requiringdevelopment of control-oriented models that capture the control critical process. There are well known and widelyused rules of thumb for choosing control methodology. Examples are: "if significant modeling uncertainty isanticipated use robust control", "if parameterization is easy use adaptive control","if hard nonlinearities areimportant use nonlinear control", etc. Unfortunately, the words significant, easy, important are not well defined anddecisions are based on intuition. Most participants acknowledged that it would be very desirable to perform controlanalysis and design before hardware development. This would lead to reduced design cycle time and possibly betterproduct. This has been part of the focus of the recent Mechatronics activity in the Control system academiccommunity. However, this is not a common industrial practice today. This is a well-recognized problem inindustry and two possible ways of addressing the need were suggested. First, integrate design and control theoryeducation at both the graduate and undergraduate levels. This sentiment will be echoed in later sections. Secondly,reward research in control applications; particularly research dealing with entire engineering systems and not justcomponents. These suggestions were aimed at the entire Controls community including academia, industry andgovernment.

6. Problem Classification

Group One: Engr II, Conference Room 2301 (MEE Dept.) Sun. 3:15-4:15 PMFacilitators: A. Alleyne & K. GloverParticipants: Bamieh, Chang, Grizzle, Bailey, Hofbauer, Seborg, Guzzella, Tomizuka, Ioannou, Ford, Lukich,Tabaczynski.

Working Session Topics and Issues for Discussion:• Can models and modeling techniques be categorized along with control techniques to give a ‘look-up’ table

approach to integrated problem solutions?• If so, what. If not, why not.• What are industrial needs (wants) for an integration of modeling and control?• Is there a need or can most problems be solved efficiently without it?

9

Professors Gerdes (Stanford) and Rizzoni (OSU) during the poster session.

Discussion Summary:

Usually control-oriented models are physics-based (fixed structure with parameter identification). Theirsuccess and wide usage within the control community is based on the following facts: (a) they allow linear,nonlinear, and non-model-based control techniques to be developed and evaluated, (b) they enhance ourunderstanding of the system and reveal its limitations, (c) they do not require extensive data and thus calibrationeffort in a different platforms and incarnations of the same hardware is minimized. However, the control-orientedmodels developed to date are usually developed for a specific process, not a generalized class of systems.

Thus, there is no perceived trend in matching control-oriented models with control design and synthesismethodologies. Moreover, it might not be desirable to develop control-oriented models having in mind a specificcontrol technique unless in a later stage of the development process for refinement purposes. It may be desirable tohave low order models to give essential insight into the problem and suggest an appropriate control or diagnosticapproach. However, the models should not be posed in a framework that constrains the initial consideration ofdifferent techniques and ‘forces’ a particular solution. The only cases for which the modeling and control designmethodology are inseparable are:

• Neural networks• Fuzzy logic, and• Robust control/system ID

Neural networks are very successful when a large amount of data is available and there is a good understanding ofthe process. Fuzzy-logic based modeling and algorithms are invaluable in cases where a set of decision making iswell defined (ex., if it is hot, turn on the air-conditioning). Finally, robust control/ID methods are extensively usedin component and subsystem level.

Industry recognizes that there is a 1 year serious modeling effort involved in designing a new subsystemand integrating it into an existing system. Designing a new system often involves 3-4 years of modeling anditeration. A lot of that time is spent in the tuning of controllers. As an example, in Ford’s Powertrain ControllerDevelopment program there are ~250 Algorithm and Software specialists but ~400 Calibration specialists. Forobvious financial reasons, the main industrial needs are ways to reduce that time frame and the human effortassociated with getting the model and control integration correct as quickly as possible. The major research needthat was identified from discussions among the industrial participants was the following:

It was agreed that there is a research need for an engineering ‘tool’ or methodology that can integrate the process,i.e., pass and maintain information to all stages of modeling and control design. If such a tool could be developed,and standardized across different platforms, this would greatly aid the current industrial practice.

10

7. Insight Gained From Specific Examples

For this topic two separate breakout discussions were held; one on each day.

Group Three: MRL Bldg., Conference Room 2053(MRL) Sat., 3:30-5:00 PM

Facilitators: K. Glover & J. GrizzleParticipants: Horowitz, Kotwicki, Krogh, Lukich,Milot, Mezic, Paden, Peng,

Group Three: MRL Bldg., Conference Room 2053(MRL) Sun, 2:00-3:00 PM

Facilitators: G. Rizzo & J. C. GerdesParticipants: Chin, Fujioka, Rizzo, Glover, Gerdes,Sivashankar, Diop, Tomizuka, Shankar, Tsao, Bailey,

Working Session Topics and Issues for Discussion:• Are there commonalties to the successful integration of modeling, analysis, and control?• Based on case studies presented here in this workshop, and other information at the disposal of the workshop

participants, identify what some of these are.• What are the commonalties for the cases where integration of modeling, analysis, and control has failed?

-One example would be where the model is developed by one community (e.g. physicists) and the controlis attempted by another.

Discussion Summary:

This discussion topic was one of the key ones in the workshop. The discussion of the topic was separatedinto two different types of cases. First, an examination was made of cases where modeling and control had beensuccessful. Then cases where less than total success was achieved were examined. The idea of examining both setsof cases separately was to get a clear idea of what led to failure and what led to success. The idea was that one oftenlearns as much by failure as through success.

For the success stories, a very short initial list of examples was built up from the experiences of theparticipants. The general understanding among the participants was that the list would be augmented with additionalexamples only if they brought new dimension to the discussion. The short list of success stories that were used is asfollows:

1) Air/Fuel ratio control2) Anti-Lock Braking Systems (ABS)3) Automated following for Intelligent Vehicle Highway Systems

While it may be difficult to compare the various levels of success of each of these Automotive control systemsamong each other, it is quite clear that all of them did achieve high levels of engineering success. The first two alsoachieved a high level of commercial success by being rapidly accepted by the market. In its original concept, thethird example has not received the level of commercial, market-driven success as the first two. However, spin-offtechnology such as Automated/Intelligent Cruise Control has seen an initial market acceptance.

All of these examples share some notable common features. These are listed below in bullet form:

1) Highly perceived pay-off2) Simple physics captures 80% f the relevant phenomena3) Current sensor technology allows the measurement of the performance variables4) The actuators allow for sufficient control authority to perform desired task5) The actuators and sensors are roughly co-located6) The final controller structure can be specified to the point that there are few ‘tweakable’ parameters.

The first one is a key that is often overlooked when considering the integration aspect from a purely engineeringpoint of view. A system or program that successfully integrates modeling and control should usually have somemarket drivers behind it to ensure that the development and integration has the resources available to carry theprocess through to implementation. Without the appropriate resources available, the actual integration process facesa very severe challenge because the effort is not there to test the modeling or implement controllers, etc. Thisapplies to industrial as well as academic problems. The second point basically states that the system can be

11

represented relatively easily and a control-oriented model can be found. This is important because the controllerdesign based on that model will only have to account for 20% uncertainty. This is what the feedback aspects of anycontroller are designed to do: compensate for disturbances and/or uncertainty. With a good control-oriented model,usually comes a relatively straightforward controller which is the last point. However the key to this easyrepresentation of the system physics in a good model. The 3rd-5th points are based on the fact that the system designallowed the model to be represented in a form that was controllable and observable given the allowable sensor andactuator technology. These points are actually important in the overall system design phase that was mentionedpreviously. By including the requirements of the controller early in the system design process, an appropriate set ofsensors and actuators can be incorporated in addition to making sure that they are located appropriately.

Examples where modeling and control have been less than totally successful include the following:

1) ‘Cold Start’2) Variable Displacement Engines3) Cruise Control for heavy trucks

The discussion groups had time to discuss two of these in-depth: the Cold Start problem and the Cruise Control forheavy trucks. Cold Start has been a hard problem for the following reasons:

1) The fundamental physics is poorly understood, to the point that no one really knows how to define acontrolled experiment.2) There are fundamental physical limitations on the process, such as the amount of thermal energy that canbe generated.3) It is unknown what the achievable performance really is.4) No active sensing during this phase as the sensor technology is very similar to the three-way catalysttechnology.

Cruise control for heavy trucks is difficult for a completely different set of reasons. Here, the physics is very simpleand thus the model is well-known. Additionally the system contains many of the advantages posed earlier for theAutomated Following ‘success’ story. The main problems associated with the heavy truck cruise control problemare as follows:

1) Unknown mass of the vehicle that varies widely2) Actuators are mismatched with the plant (too slow or too limited)3) Unknown controller metric. What should be controlled?

For both the Cold Start and the cruise control there are different types of problems. First of all, there are what willbe termed ‘engineering’ problems. In the case of the cruise control, the first bullet indicates that the controller has todeal with the unknown mass of the tractor-trailer combination. This mass could be estimated on-line, but there issome fear in regards to the robustness of doing this. The second cruise control bullet points to hardware limitationsimposed by the system design. The engine is easily saturated in the torque it can put out. Moreover, there are hardlimits on the dynamics associated with acceleration and deceleration that can’t be overcome by any controller.These are a function of the system design: e.g. pneumatic brakes. These drawbacks share similarity with theproblem #4 in the Cold Start example. Basically it comes down to insufficient actuation and sensing. These arehard engineering problems that may possibly be solved by additional design.

However, there are additional problems associated with these examples that did not succeed fully. The maincold start problem is that the physics is poorly understood which makes it difficult to develop any type ofstraightforward model for it. This is a problem that is not easily surmountable by system redesign. It points to afundamental limitation in the whole modeling-control integration: the system has to be understandable to proceed.Another problem that is common to both these examples is that there is a set of unknown metrics. In the cruisecontrol case, there is a fundamental disagreement over the performance objectives by those who are doing thedesign. Is the controller goal just highly accurate speed regulation, or is adequate speed regulation plus good fueleconomy? Different trucking companies and drivers have different opinions about this, which changes completelythe gain/bandwidth of the controller design. This is not just an engineering problem but brings into in elements ofpolicy-making and business. These are often not as sharply definable as engineering terms. Similarly, in the ColdStart case, there is an unknown about exactly what is trying to be achieved because the limit of achievableperformance cannot be set as a goal a priori.

One of the key insights that this session served to highlight was that the reasons for successful integration ofmodeling and control are common among the examples considered. It is more than likely that this will hold true forseveral other examples from Automotive Systems and will probably hold true for most, if not all, other fields of

12

engineering. However, from the discussions that were carried out at this workshop, when the integration ofmodeling and control breaks down there are fewer commonalities among the ‘failures.’ In essence, thoseapproaches that succeed do so for the same reasons. Those approaches that fail, fail for many different reasons.Therefore, it may be possible in the future to examine whether or not all the components are there for a successfulproject integration beforehand and know that there will be a straightforward procedure with a high successprobability. If there is something missing, like a clear goal of exactly what will be controlled and exactly what itwill be required to do, then the control engineer can know there will be a long road ahead given current practices.

Professor Glielmo (U. Napoli) and Dr. Ponti (U. Bologna) discuss engine systems during the poster session

8. Complexity and Scalability

Group Four: Engr III, Conference Room 116 (Materials Dept.) 2:00-3:00 PM

Facilitators: Cook & Huei Peng

Participants: Cook, Guzzella, Grizzle, Hofbauer, Sun, Horowitz, Cikanek, Ponti, Alleyne, Paden, Tsugawa,Kokotovic, Krogh.

Working Session Topics and Issues for Discussion:• Can the chosen integration approach scale with problem complexity/detail or should the approach change with

problem complexity/detail- E.g. traffic flow systems vs. individual vehicle systems.- E.g. chemical combustion vs. I/O engine performance.

• Is there a breakdown by either the modeling or the controller (or both) that limits or prohibits an integration asthe problem becomes scaled to larger size of finer resolution?

• If so, what’s more likely to ‘fail’: the model representations or the control approaches

Discussion Summary

This was one of the most difficult topics that were tackled during the workshop. The reason for this wasthat most of the workshop participants have had experience dealing with systems of relatively low dynamic order.The questions that were being asked in this discussion section were whether or not the same principles that guidedsuccessful modeling and control integration of the Air Fuel ratio example in Section 5 could be applied to larger andmore complex systems of high dynamic order. These systems of high dynamic order could be generated byincreasing the scale of a particular problem, such as traffic control of an overall highway system, or by reducing the

13

resolution that one wished to look at a problem, for example revisiting the Air Fuel ratio problem from a molecularlevel.

Although there have been previous studies on the control of large scale dynamic systems with manyvariables, control engineers really don’t have a lot of success stories in dealing with these systems. Many of thecontrol tools are limited in their ability to handle systems of increasing complexity. In many of these systems, anattempt is made to decouple the problem into a large number of decentralized problems and then determine anapproach to patch the sub-problems together. Oftentimes this is done through relatively simple heuristics in actualpractice. However it is a start and does provide a direction to go in. Ideally, the goal would be to decompose theoriginal system into subsystems, develop good models for the subsystems using the previously described bestpractices, integrate that modeling approach with a control approach at the local level, and finally integrate each ofthe decentralized systems so that the overall system performs as desired. This is a type of ‘layer and nest’ approachthat has seen some success in practice.

There are difficulties in the ‘layer and nest’ approach. One is that complexity can be application specific.It may be that the overall system is made up of subsystems that are identical to each other in terms of dynamicstructure. This could be the case in an Automated Highway scenario or ‘swarms’ of robots. However, the overallsystem may also be made of subsystems that are dynamically very dissimilar; for example electrical, mechanical andchemical system in combustion processes. They may have different time scales and different systemrepresentations. This makes it very difficulty to streamline the integration at the subsystem level and also hindershandshaking between subsystems. Obviously, the system made of dynamically similar subsystems would be easierto handle and would have a higher probability of successful integration of the modeling and control. Therefore, itmight make sense to choose your complex problem carefully.

One main conclusion from this breakout session was the following. If there is a common theme that thesystem can be modeled about, and that theme can be used for subdividing the problem, then this will enhance theprobability of success. However, this common theme should be strongly physically based. It should be very closelytied to physically measurable metrics and provide an easily accessible overall goal that can be referred to repeatedlyto determine whether or not the ‘divide and conquer’ method is moving in the right direction. Additionally, if thereis a common objective, this makes it easier to scale up or down in terms of complexity. Parallel themes can be usedrather than just one as long as they can be easily linked back to the physically-based central theme. This is keybecause usually at higher levels of the system representation the paradigm can’t be the same as lower levels: e.g.finite state machines (high level) vs. transfer functions (low level).

Professor Peng (UMich) and Dr. Butts (Ford) examine software tools during one of the breaks

Although the above conclusions could be reached, there was still agreement that this was just the tip of theproblem. In general it was understood that control engineers usually don’t learn about complex systems in theirClassical Control classes and so they aren’t well prepared to attack them in a systematic way. The lack ofrecommendations and direct answers to the questions being posed in the discussion groups is perhaps a furtherindication that complex systems aren’t that well understood; even in well studied areas such as Automotive systems.However, systems are becoming more and more complex every day. Even if the physical system complexity

14

remained roughly constant (which it probably won’t), the networking and software capabilities of the systemscontrol engineers must deal with is growing rapidly. Therefore, it’s becoming evident that there is a growing needthat the field is not preparing people for or addressing. While this is a somber realization, it is also a promisingavenue for future study; both in terms of intellectual richness and societal impact.

9. Formal Integration Feasibility

For this topic two separate breakout discussions were held; one on each day.

Group Two: Engr II, Class Room 3301 (ChE Dept.) Sat 3:30-5:00 PM

Facilitators: Masayoshi Tomizuka & Takehiko FujiokaParticipants: J. Cook, C. Gerdes, L. Gielmo, Guzella, Hedrick, Hofbauer, Ioannou, Kalkkuhl, Kokotovic,Kolmanovsky

Group Two: Engr II, Class Room 3301 (ChE Dept.) Sun 2:00-3:00 PM

Facilitators: Jurgen Ackermann & Petros IoannouParticipants: Chang, Rizzoni, Bamieh, Freudenberg, Ackermann, J.D. Powell, Dahleh, Meinhart, Kalkkuhl, B.Powell, Mezic, Lukich.

Working Session Topics and Issues for Discussion:• Is it possible to integrate modeling and control in a unified analytical framework or is it an “art” best left to the

experienced control designer?• If it is possible, what is needed from the research community to seamlessly integrate modeling, analysis and

control?• Do System Theoretic tools exist?

- If so, which ones? What are their advantages and drawbacks?- If not, what needs to be developed?

Discussion Summary:

The following picture describes all the active interconnections in a control design project courtesy of Prof.Fujioka.

Discussiongroup focus

15

The focus of these breakout sessions was on the left half of the diagram. In that respect, many of the real-timeimplementation issues are not being directly considered here. It was acknowledged that seamless and systematicintegration of modeling, analysis, and control means that there exists a path from control to analysis and back tomodeling: modeling ! analysis! control ! analysis ! modeling. It was felt this is not currently the norm inmany cases. To achieve any type of a seamless integration the control researcher needs to invest time and effort inestablishing the model credibility and develop an accurate parameterization of the system at hand.

In fact, many of the participants felt that the task of modeling for control is currently perceived as an artwith few dominant guidelines that arise from previous experiences: both successful and unsuccessful. Theprobability of success is greatly increased by the level of experience of the engineer working on the particularproblem. There is no systematic methodology to assist the novice engineer or researcher in their initial controlattempt. In reality, many factors come into play with the choice of model and control, including the individualdoing the modeling, the resources available and the presence of operators or calibrators in the process. The problemstatement itself is not static and often the basic control objective changes as solutions are sought. Some specificexamples of guidelines used to assist modeling for control include the definition of “control volumes” which ensurecausality in the systems power flow. This is in line with the Bond Graph approach of Paynter that was developedfrom this ‘causal power flow’ perspective. Additionally, the model should be sufficient to identify the criticalconstraints, such as actuator saturation/rate limits or state space constraints, which limit the performance of thesystem. While the challenging task of control oriented modeling did dominate a significant portion of thediscussion, it was also determined that the control oriented models only aided in the modeling/control integration.In and of itself, the modeling for control did not provide a formal methodology for integration of both modeling andcontrol.

It was felt that problems that conform to the ‘successful’ criteria of Section 7 (Insight from SpecificExamples) might have the best opportunity for developing a system theoretic unified approach to modeling andcontrol integration. Unfortunately, for the problems that have a few of the ‘unsuccessful’ attributes of Section 7, itis unlikely that a formal integration can be made to work on any type of general basis. For the well-posed types ofproblems there already exists types of software for systematic modeling. One example is Dymola which is based onan object-oriented type of approach. There are other, non-commercial software packages such as 20-sim that alsotakes advantage of the causality-checking Bond Graph approach to ensure model consistency at the start of theprocess. It may be possible, and quite feasible, to make an extra step to providing a systematic controller synthesissequence as part of such a modeling approach. However care should be taken to incorporate all the necessaryconstraints that the system would be able to face. System theoretic tools are notorious for relying on assumptionsthat can provide difficult and numerous constraints. What would be desirable, for example, would be able to add ordelete a state or input saturation and have the system reconfigure its controller design systematically andautomatically. The controller generation procedure should be able to start with simple controller designs first, usingfamiliar methodologies, and then add more complexity as the performance requirements were increased. Thisprocedure would proceed with varying levels of input from the user, depending on the user’s experience level.Whether the push for this development should come from governmental agencies (e.g. NSF) or from industry was anopen point of discussion.

Finally, there was some significant support to focus on and reward research efforts that enhance ourunderstanding of the physical problems, the system barriers (e.g. sensing constraints), and the tool limitations ratherthan comparison of different algorithms based on some performance objective. One way to do this would be toencourage more Ph.D. investigations that develop guidelines and examples of alternative paths when the controlmethodology cannot be applied due to violation of some assumptions. This is part of what is known in Europeaninstitutions as more of a Mechatronic approach. The reason for the suggestion of this focus by the discussion groupwas that work done on understanding of the process as a whole can be utilized easily by others to incorporate it intotheir own modeling and control integration framework. Moreover, this is particularly important for the types ofproblems mentioned in Section 7 where the formal integration looks unlikely. The more insight that is gained intothese difficult processes on a case-by-case basis, the greater the probability that they can eventually be cast into aframework that is more tractable.

10. Educational Issues

Group Four: Engr III, Conference Room 116 (Materials Dept.) 3:30-5:00 PM

Facilitators: J.D. Powell & S. Sivashankar

16

Participants: Rizzo, Rizzoni, R. Shankar, R. Smith, Stefanopoulou, J. Sun, Tabaczynski, A. Teel, Tsao, Tsugawa,von Niewstadt

Working Session Topics and Issues for discussion:• What is the most appropriate and effective way to teach an integrated approach to Modeling and Control?• Change in pedagogy?• Interdisciplinary teams?• Project oriented classes instead of exam oriented classes?• “Canned” labs vs. “open ended” projects?

Discussion Summary

Many of the participants in the workshop felt that it was extremely important to introduce the integration ofmodeling and control into the education process. In particular, it was realized that current industrial practicedemands that control engineers have a holistic view of the entire problem. They should be able to set up the initialproblem in collaboration and coordination with people from other fields. One of the keys to better integration ofmodeling and control is to increase the level of interdisciplinary work. By having people with different backgrounds(Control, Fluid Mechanics, Thermodynamics, etc) working together, it may be possible to come up with moreelegant and effective solutions to the integration process. This ‘teaming’ is one of the keys in shortening industrialdesign cycles and optimizing the process. By teaming control engineers with other disciplines at the start of anytype of process it is possible to impact the design of the system with the thought of controlling it. This can be donebefore immutable commitments have been made to the design process.

The classroom is an excellent opportunity for young engineers to learn these valuable skills such asteaming and collaboration. However, this probably will require a change in the existing Control Educationpedagogy. Currently, the educational system in undergraduate and graduate Control courses is geared towardsindividual performance. Industry is more effective at forcing teaming because projects tend to be somewhat morecrisis driven. This can highly motivate and focus a lot of people on a common goal. To increase the integration ofmodeling and control, it was felt that more should be done on campus to also increase the level of teaming andcollaboration.

The most effective way to teach this integration of modeling and control is to do it in teams. Not just teamsformed to do a project in a particular class where all the students are studying the same controls topics. Instead, thefocus was on semester or year long design projects, such as senior capstone design projects, where ControlEngineering plays a central role. However, members of the design teams with a specialty in Control Engineeringwould still have to interact and work with others whose field of choice may be, for example, Materials. This wouldforce students to work in interdisciplinary teams on open ended projects. It may be possible to award prizes or someother incentive and somewhat ‘simulate’ an industrial deadline-driven project. This was felt to be the best scenariofor students to get educated in the ways of integrating modeling and control; hopefully with an eye towards affectingsystem design. As Rod Tabacynzski of Ford Motor put it in his plenary, there really is no substitute for experienceif people are to do well integrating modeling and control. It is better to get them started as early as possible.

Discussion on graduate education was similar in its scope. However, there were a few things that werespecific. It was felt that students could benefit from working on an industrially sponsored research project andactually spend time in industry during their studies. There was some sentiment that graduate courses train studentfor academic research rather than industrial research. However, it was also recognized that this may have been moreof a problem in the past than at present. With the growing presence of Mechatronics classes on various campusesand more Mechatronics-type research being done, there is a strong applications flavor to much of the graduateeducation and training currently being carried out.

17

Dr. Chang (GM) and Dr. Kotwicki (Ford) with graduate students Niklas Karllson and Lasse Moklegaard

11. Conclusions

The NSF Workshop on the Integration of Modeling and Control of Automotive Systems was deemed avery positive experience by all of the participants. Originally, the workshop was planned for approximately 35participants and was budgeted accordingly. The eventual interest raised by the workshop was so large that the actualparticipation was over 70 with many of the additional participants agreeing to cover their own expenses just to havethe opportunity to participate. The interaction among the participants was also very high with many animateddiscussions taking place during the presentations and in the breakout sessions. In fact, these discussions oftenspilled over into the breaks, lunches and dinners in an extremely healthy fashion. This report can not really capturethe spirit of the workshop in its entirety but attempts to give the interested reader some level of insight into theactual proceedings of the event. Readers are encouraged to contact the workshop organizers for further informationif they wish.

Many of the ideas generated in this workshop can serve as a springboard for further discussion and, moreimportantly, future action taken by the Control Engineering community. One example of where this has alreadytaken place is the formation of the Automotive TAB in the IEEE Control Systems Society to complement theexisting Transportation Panel of the ASME’s Dynamics Systems and Control Division. While we were able to onlyscratch the surface during our two days, a few key ideas did come to light which should be taken further.

• The integration of modeling and control is very important and should be taken into account in all practicalcontrol problems. Control engineers should not approach a problem with a methodology in hand beforethey study the overall process.

• There is a real need for the development of an engineering tool or methodology to formalize the integrationif and when possible. For a system where the successful integration is likely it should be possible toformalize the problem. ‘How’ is an open question. For systems where the successful integration isunlikely (e.g. unclear goals, poorly known physics) the problem formalization may be intractable.

• Dealing with the integration of modeling and control is very difficult for complex systems. Moreover,approaches that are successful for small scale problems are relatively hard to ‘scale’ up to more complexsystems. No definitive solutions were offered in this brief workshop. There was only theacknowledgement and reinforcement that this is a vital area that will only grow in future importance.

We hope that this report will stimulate continued discussion on this important topic. We also hope that it willmotivate new avenues of research and education. Readers’ feedback on any of the topics contained here isencouraged.

18

Appendices

Appendix A: Workshop Program

Saturday, June 5, 19998:30-8:45 a.m. Welcoming Remarks (Anna Stefanopoulou, Andrew Alleyne)

8:45-9:30 a.m.Keynote Presentation"The Interaction and Interdependence of Component Design, Modeling of Physical Systems, andControl System Capability", Rodney J. Tabaczynski, Ford Research Laboratory

9:45-10:30 a.m. "Robust Steering Control," J. Ackermann, DLR, Germany10:30-11:15 a.m. "Graduate Education and Research in Hybrid Vehicles," G. Rizzoni, Ohio State University

11:15-12:00 noon "The Cold Start problem; Modelling and Control Issues," K. Hedrick, University of California,Berkeley

12:00-1:00 p.m. Lunch Break

1:00-1:40 p.m. "Modeling will Always Be Somewhat Ad Hoc !! Examples on Automatic Transmission, IntelligentCruise Control and Rollover Warning Systems," H. Peng, University of Michigan

1:40-2:20 p.m. "Modeling Issues in the Design of Heavy Trucks for Human Controllability," C. Gerdes, Stanford

2:20-3:00 p.m. "Three Case Studies on Optimal Engine Set-Points: How optimal are they?" A. Stefanopoulou,University of California, Santa Barbara

3:00-3:30 p.m. Poster Session3:30-5:00 p.m. Discussion Sessions (I-IV)

Sunday, June 6, 19998:15-8:30 a.m. Recap on Previous Day (Andrew Alleyne)8:30- 9:10 a.m. "IC Engine Control using Observer Theory," J. D. Powell, Stanford

9:10-9:50 a.m. "Model-Based Performance Assessment of a Lean-Burn System ," J. W. Grizzle, University ofMichigan

9:50-10:30 a.m. "Control Issues in Fuel-Cell Based Vehicles," L. Guzzella, Swiss Federal Institute of Technology(ETH), Switzerland

10:30-10:45 a.m. Break10:45-11:25 a.m. "Towards Analytical Powertrain Controller Development," S. N. Sivashankar, Ford Motor Company11:25-12:05 p.m. "Verification of Discrete and Hybrid Powertrain Controllers," B. Krogh, Carnegie Mellon University12:05-1:00 p.m. Lunch Break1:00-2:00 p.m. Poster Session2:00- 3:00 p.m. Discussion Sessions (I-IV)3:00-3:15 p.m. Break3:15-4:15 p.m. Discussion Sessions (I-IV)4:15-5:00 p.m. Report on Sessions5:00-5:15 p.m. Closing Comments, A. Alleyne, University of Illinois at Urbana-Champaign

19

Appendix B: Poster

Poster 1:Authors: Schwarzhaupt, Eger, Kiencke,Title: "Model based Rollover Detection"Affiliation: University of KarlsruheE-mail, contact person:Andreas.Schwarzhaupt@etec.uni-karlsruhe.de

Poster 2:Authors: Martin SchmidtTitle: ``Influence of the accessory drive onconsumption and road performance, models forsimulation.''Affiliation: Darmstadt UniversityE-mail: MSchmidt@iat.tu-darmstadt.de

Poster 3:Authors: Christoph HalfmannTitle: "Estimation of driving resistances, vehicleparameters a longitudinal dynamics model used forsimulation and adaptive cruise control"Affiliation: Darmstadt UniversityE-mail: chalfmann@iat.tu-darmstadt.de

Poster 4:Authors: Ivan Arsie, Cesare Pianese, GianfrancoRizzoTitle: "Hierarchical Models Integration for EngineControl Applications"Affiliation: Department of Mechanical Engineering,University of Salerno, ItalyE-mail: grizzo@ponza.dia.unisa.it

Poster 5:Authors: Fabrizio PontiTitle: "Very Robust transient exhaust gas emissioncontrol algorithms"Affiliation: University of BolognaE-mail: fponti@renoir.ing.unibo.itponti@motori.eng.ohio-state.edu

Poster 6:Author: Sharon Liu, B. PadenTitle: "Continuously Variable Transmission Control"Affiliation: UCSBemail: sliu@engineering.ucsb.edu

Poster 7:Author: Niklas Karlsson, M. DahlehTitle: "Robust Nonlinear Control for ActiveSuspension"Affiliation: UCSBemail: niklas@engineering.ucsb.edu

Poster 8:Author: David Betz , I. MezicTitle: "Flow and Mixing Control for a 2-stroke Engine"Affiliation: UCSBemail: dbetz@engineering.ucsb.edu

Poster 9:Author: Mike Larsen, P.Kokotovic:Title: ``NonLinear Control for Variable TurbochargedEngine''Affiliation UCSBemail : larsenm@bode.ece.ucsb.edu,

Poster 10Author: Lasse Moklegaard, StefanopoulouTitle: "Braking Control for Heavy Duty Vehicles"Affiliation: UCSB email: lasse@engineering

Poster 11:Author: Yan Wang, A. StefanopoulouTitle: "Idle speed control for Camless Engines"Affiliation: UCSBemail: ywang@engineering.ucsb.edu

Poster 12:Author: Paul Cronin, I. Kolmanovsky, A. StefanopoulouTitle: "Modeling and Optimal Control for a HybridTurbocharger"email: paulcro2@engineering.ucsb.edu

Poster 13.Author: Fred Loquasto, D. Seaborg, A. Stefanopoulou:Title: "EGR control in a Diesel Engine: A feasibilitystudy using Variable Valve Timing"email: loquasto@engineering.ucsb.edu

Poster 14.Author: Garrick McNey, S. Hsieh, A. StefanopoulouTitle: "Engine-Dynamometer Interactions: A Challengefor Sys ID, and Controller Implementation"email: mcneyg@engineering.ucsb.edu

Poster 15:Authors: Takehiko FujiokaAffiliation: Univ. of TokyoE-mail, contact person:fujioka@vdl.t.u-tokyo.ac.jp

Poster 16Authors: Richard FordAffiliation: University of CambridgeE-mail: rgf21@eng.cam.ac.uk

20

Appendix C: List of Participants

Professor Juergen Ackermann DLR - GermanyProfessor Andrew Alleyne University of IllinoisMs. Kathy Bailey Ford Motor Company, Senior EngineerProfessor Bassam Bamieh University of California - Santa BarbaraProfessor John Brook University of California - Santa BarbaraMr. Aaron Brown Graduate Student, University of California- Santa BarbaraDr. Man-Feng Chang GM, Senior EngineerMr. Bo-Chiuan Chen Graduate student, University of MichiganDr. Steve Chin GM, Senior EngineerMs. Sue Cikanek Ford Motor Compay, Senior EngineerMr. Jeff Cook Ford Motor Company, Senior EngineerMr. Paul Cronin Graduate Student, University of California - Santa BarbaraProfessor Mohammed Dahleh University of California, Santa BarbaraProfessor Sette Diop Laboratoire des Signaux & Systemes- CNRS Univ. ParisMr. Richard Ford University of CambridgeProfessor Jim Freudenberg University of MichiganProfessor Takehiko Fujioka The University of TokyoProfessor Chris Gerdes StanfordProfessor Laura Giarre University of California, Santa BarbaraProfessor Luigi Glielmo Universita di NapoliProfessor Keith Glover University of Cambridge, UKMr. Mike Gray Graduate Student, University of MichiganProfessor Jessy Grizzle University of MichiganProfessor Lino Guzzella Swiss Federal Institute of TechnologyProfessor Karl Hedrick University of California, BerkeleyProfessor Peter Hofbauer Propulsion Research InstituteProfessor Roberto Horowitz University of California, BerkeleyProfessor Petros Ioannou Univ. of Southern CaliforniaDr. Jens Kalkkuhl Daimler Chrysler, Senior EngineerMr. Jan-Mo Kang Graduate Student, University of MichiganMr. Niklas Karlsson Graduate Student, University of MichiganProfessor Hassan Khalil Michigan State UniversityProfessor Petar Kokotovic University of California, Santa BarbaraDr. Ilya Kolmanovsky Ford Motor Company, Senior EngineerDr. Al Kotwicki Ford Motor CompanyProfessor Bruce Krogh Carnegie Mellon UniversityMr. Mike Larsen Graduate Student, University of California - Santa BarbaraMs. Sharon Liu Graduate Student, University of California - Santa BarbaraMr. Fred Loquasto Graduate Student, University of California - Santa BarbaraDr. Michael Lukich Caterpillar, EngineerMr. Garrick McNey Graduate Student, University of California - Santa BarbaraProfessor Carl Meinhart University of California, Santa BarbaraProfessor Igor Mezic University of California, Santa BarbaraProfessor Umesh Mishre University of California - Santa BarbaraMr. Lasse Moklegaard Graduate student, University of California - Santa BarbaraMr. Manabu Omae Graduate Student, Tokyo UniversityProfessor Brad Paden University of California, Santa BarbaraProfessor Huei Peng University of MichiganDr. Fabrizio Ponti University of Bologna

21

Professor J.D. Powell Stanford UniversityMr. Barry Powell Ford Motor Company, Senior EngineerProfessor Gianfranco Rizzo Universita' di SalernoProfessor Giorgio Rizzoni The Ohio State UniversityMr. Joe Schmidt Mack Trucks, Senior EngineerMr. Martin Schmidt Graduate student, Darmstadt UniversityMr. Andreas Schwarzhaupt Graduate Student, University of KarlsruheProfessor Dale Seborg University of California, Santa BarbaraMr. Raman Shankar Visteon, Senior EngineerMr. Shiva Sivashankar Ford Motor Company, Senior EngineerProfessor Roy Smith University of California, Santa BarbaraProfessor Anna Stefanopoulou University of California, Santa BarbaraDr. Jing Sun Ford Motor Company, Senior EngineerDr. Rodney Tabaczynski Ford Motor Company, Director of Ford ResearchProfessor Andrew Teel University of California, Santa BarbaraProfessor Masayoshi Tomizuka University of California, BerkeleyProfessor Tsu-Chin Tsao University of Illinois, Urbana-ChampaignProfessor Sadayaki Tsugawa Tokyo UniversityProfessor Le Yi Wang Wayne State UniversityMr. Yan Wang Graduate Student, University of California - Santa BarbaraDr. Diana Yanakiev Cummins, Senior Engineer

22

Appendix D: Presentation Abstracts

The Interaction and Interdependence of Component Design, Modeling of Physical System and ControlSystem CapabilityRodney J. TabaczynskiDirector, Powertrain and Vehicle Research

Significant advances have been made in new components that can be integrated into powertrain systems. Theseinclude integrated starter alternators, direct injected gasoline engine systems, variable cam timing devices, lean NOxtraps, advanced battery systems, elctronic throttles and continuously variable transmissions. In parallel, on boardcomputers are becoming faster and more capable, detailed physical modeling of complex systems is progressingtoward real time capability, and control system tools and theories are becoming more applicable to these complexsystems. The challenge for the systems engineering community is to determine how all these complex devices canbe used to redesign the total system to provide increased function at reduced cost and increased reliability androbustness. In this presentation, examples of engineering systems will be given that show how hardware needs tochange and be integrated in order to optimize the system and how modern control is required in order to provide theneeded functionality of the final system design. In addition, the need to integrate detailed physical models into theanalysis and design of the control system will be demonstrated via examples of cold start and lean NOx trap locationoptimization. The goal of the presentation is to show, via examples, the synergy between control, physical modelingand design in order to develop unique, robust, low cost solutions to engineering problems.

Robust Steering ControlJuergen AckermannProfessor, DLR, Germany

Comparison of steering vs individual wheel braking.Robust yaw motion control by active steering.Simulations and experimental results.Vehicle rollover avoidance by active steering and braking

Graduate Education and Research in Hybrid VehiclesGiorgio RizzoniProfessor, Ohio State University

Hybridizing automotive drivetrains, or using more than one type of energy converter, is considered an importantstep to high fuel economy. The design space for hybrid-electric vehicles, which includes conventional and pureelectric vehicles as special cases in the extremes, is vast and growing. To fully realize the potential afforded thesetechnologies requires a complete vehicle systems approach for component selection and optimization over typicaldriving situations. The control problems that arise in connection with hybrid powertrains pose significant newchallenges to powertrain control engineers. One aim of this presentation is to give an overview of control problemspertaining specifically to hybrid vehicles. In an effort to further research and graduate education in the area of hybridvehicles, the Department of Energy Office of Advanced Automotive Technologies has recently established a numberof Graduate Automotive Technology Education (GATE) Centers focused on various aspects of hybrid vehicledesign. A second aim of this presentation is to discuss the role of these Centers as enablers to foster relationshipsbetween industry and universities.

23

Model-Based Performance Assessment of a Lean-Burn SystemProfessor J. W. GrizzleElectrical Engineering and Computer Science DepartmentUniversity of Michigan

Designing a powertrain system to meet drivability, fuel economy and emissions performance requirements is acomplicated task. There are many tradeoffs to be analyzed in terms of which components to use, such as lean burntechnology versus classical components, characteristics of individual components, such as size or temperatureoperating range, and the control policies to be employed. In addition, there are performance tradeoffs to be analyzed,such as emissions level versus fuel economy. In the past, most of the powertrain design was made on the basis ofhardware, that is, on the basis of laboriously assembling and testing many possible system configurations. Today,the time-line for vehicle design is constantly shrinking, the number of possible powertrain configurations isexpanding, and the cost of doing hardware evaluations is growing. It is simply no longer feasible to make all (oreven most) of the design decisions on the basis of hardware alone. More and more of the decisions must be madeupon the basis of mathematical models and analysis.This talk will describe the use of modeling and control techniques to assist in making powertrain design decisions onthe basis of models and up-front control system analysis. The specific technology configuration analyzed hereinvolves a gasoline direct injection (GDI) engine capable of stratified operation, in series with a three way catalyst(TWC) and a lean NOx trap (LNT). One of the truly novel features of a powertrain system that includes a lean NOxtrap is that there does not exist a notion of a steady-state operating point. An LNT is fundamentally a dynamicdevice: it fills with NOx and must be emptied or ``purged'' periodically. Indeed, if the system is run continuously ata non-rich air-fuel ratio, the trap will saturate, its trapping efficiency will approach zero, and its NOx conversionefficiency will approach 20\%. This value is too low to meet emissions requirements, and thus the trap must becycled.The consequence for analysis and control is that a dynamic model of the emissions after treatment system is anecessity, and not a luxury. Why is this so different from a standard emission system based on a TWC? A three-waycatalyst is, of course, a dynamic device. However, it has the property that as long as the feedgas remains atstoichiometry, and the TWC remains sufficiently warm, the conversion efficiencies do not significantly change withvariations in mass air flow, engine speed, load, etc. As a result, many aspects of dynamic characteristics of a TWC-based emissions system can be ignored in the initial design stages of a powertrain, and most of the emphasis placedon the dynamics of the engine. For a system with an LNT, we will see that the situation is essentially reversed: thedominant dynamics are due to the emissions system.

Control Issues in Fuel-Cell Based VehiclesL. GuzzellaProfessor, Swiss Federal Institute of Technology (ETH)Zurich, Switzerland

The intention of this talk is to introduce control people to the potential of fuel-cell (FC) based vehicles and toindicate possible challenges for modeling and control in this area. The talk will be structured as follows:

• Fuel economy of FC based vehicles: The fuel economy of FC based vehicles is very much dependent of thecomplete fuel cycle ("well-to-wheel"). Tools and data needed for this assessment and some worked-outexamples are presented in this part.

• Some remarks on the electrochemistry of FC: Some elementary concepts of FC electrochemistry arenecessary to understand and to model especially the static behavior of FC systems. Concepts like total freeenergy (Gibb's potential), electrochemical limitations (activation and diffusion losses, transport phenomena,etc.) will therefore be introduced in this part.

• Modeling and control of FC systems ("stacks"): This section shows what dynamic effects are relevant in FCsystems and what control loops must be closed for correct function. Also some important auxiliary deviceslike hydrogen feed system and air supercharger are introduced.

• Modeling and control of FC vehicles: FC based vehicles are very similar to EVs, but there are somesubstantial differences: usually a "peak-shaving" component has to be included (typically"supercapacitors"), when on-board hydrogen reforming is chosen an additional "chemical reactor" has to beincluded and the fuel storage part needs also some extra attention. The hierarchical modeling, optimizationand control problems of such vehicles will be also addressed.

24

• Ongoing projects at ETH/PSI: ETH/PSI has built a 1 kW FC system that includes some critical components(stack, super-caps, etc.) and emulates the remaining part through HIL simulations. In an ongoing projectthis system is to be scaled-up to a 5 kW system with more components in the loop (only the vehicle will beemulated). Some experimental data of both systems will be shown.

Towards Analytical Powertrain Controller DevelopmentDr. S. N. SivashankarFord Motor Company

Powertrain system development is a long lead-time endeavor in the typical automotive product development cycle.With increasing regulatory and competitive pressures in the global market, automakers are increasingly usingcomputer models and tools to improve product quality and reduce time to market. This presentation focuses oncomputer model usage in the development of embedded powertrain control system software. Recent advances inmodeling and control system design tools have helped engineers to design and test control systems before theproduction hardware is available. This talk describes the current practice and explores issues and opportunities inthree key areas. These include analytical control oriented powertrain models (plant), executable specification modelsfor the production controller, and computer tools for structural and functional analysis of the powertrain controlsystem. The complexities of the design process and the production controllers necessitate the usage of large scalemodeling methods. An example powertrain controller subsystem is described to demonstrate large-scale system andcontroller specification modeling.

Verification of Discrete and Hybrid Powertrain ControllersBruce H. KroghProfessor, Carnegie Mellon University

Verification of embedded control features for automotive powertrains early in the development process offers anenormous potential for reducing the time and cost to realize production control systems. An effective verificationtool must work directly on models already being developed for simulation and automatic code generation. Recently,we have developed two tools at Carnegie Mellon University that use the Simulink/Stateflow user interface as thefront-end for symbolic verification of discrete and hybrid dynamic systems. One tool is a Matlab command, sf2smv,which generates input files for the finite-state model checker SMV directly from Stateflow charts. The other toolgenerates finite-state approximations to hybrid system dynamics represented in a Simulink/Stateflow block diagram.Logical assertions for the hybrid system behavior can then be verified. The finite-state approximations are refinedautomatically when the verification result is inconclusive. In this presentation, these two tools will be demonstratedfor representative examples of embedded powertrain controllers. The theory behind the tools will be reviewedbriefly. Directions for further development and their application to automotive control problems will also bediscussed.