ࡱ> xRoot Entry FM&*1Table}ݬWordDocumentSummaryInformation(|  !"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNO$[\]^_uvwyz{~% !"#v'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`abwxyz{|} i0@0NormalCJOJQJmH 6@6 Heading 1$dha$58@8 Heading 2$dha$562@2 Heading 3$dha$6@6 Heading 4$dha$6<A@<Default Paragraph Font6*@6Endnote ReferenceH*:@:TOC 3 X !]^:@:TOC 2 X !]^2@2TOC 1 X !], @2,Footer  !,@B,Header  !<&@Q<Footnote ReferenceCJEH2@b2 Footnote TextCJ2Or2 Single Space$a$8O8 Double Space$da$<O<Normal - Times$dha$6>`6Title$a$ h 5;CJgtgt/ުzzzzzz '1w<GQ[erqgtb*CXYg=> k"l"$$$%%2&3&&&''''(((((((((++44668899r;s;v<w<@>A>B>C>p>q> AARCSCFFGGGGGGHHqMrMZP[PQQYRZR9U:UUUXXZZc[d[\\]]]]]naoaccc)c*cchdNeeafbgghSijjkIllmnaoppqqrqGrsss tht% I have tried to do with many students, colleagues, and industrial practitioners. Generally, however, please let us try to take a long-term perspective on the debate and agree that ultimately it is only the test of time that will determine the value of the various approaches. For now, all we can hope for is a deeper understanding of some of the relevant issues. 2. Assumptions Assumption 1 (This is a Scientific/Engineering Discussion): I assume that you have an open mind, and the whole discussion is a scientific one that demands support for any claim (i.e., you do not win via humor, ignoring questions like a politician or business marketing person, or following entrenched views that happen to be popular with the audience). For example, if you tend to have a pet-approach that you have used for many years, you must be willing to submit it to criticism and comparative scientific analysis (as Lt.-Commander Data on the TV show Star Trek said in one episode (paraphrasing): is your position so weak as to not be able to stand up to criticism?). If you are a mathematician you must take into account practical issues in implementation. If you are an engineer working in industry on practical implementations, who has not seen an integral since college, you must agree that mathematics is a very useful tool for engineering, even if you do not like it, have talents in using it, or find it useful in your current job. Some do find it very useful. It is also assumed that we are throwing away hype associated with certain areas that is (was) designed to try to get funding for hot topics. Also, please lets agree not discuss the issue of the use of the word intelligent in connection with fuzzy/neural control, or any other intelligent control method as that is often counter-productive when the focus is on engineering (and not philosophy, psychology, or artificial intelligence). Assumption 2 (Control Engineering is Based on Scientific Foundations, Heuristics, and Available Technology): I assume that you recognize the value of exploiting scientific knowledge for the solution to control and automation problems, and recognize that science is progressing rapidly on many fronts and is hence likely to make future contributions to engineering (e.g., mathematics, physics, chemistry, and biology all have significant potential to contribute to sensors, actuators, computing technology, and methodology). However, it is a principle in engineering that we cannot wait for all the relevant science to be understood (e.g., to obtain perfect mathematical models) and we must get a control systems product to market; this leads to the unavoidable use of engineering heuristics in control design (there is no implemented control system that was not constructed without using heuristics). I assume that you appreciate the rapid development of computing, electrical, mechanical, and sensing/actuation technology and the potential impacts it can have on our field. The state-of-the art in these areas significantly constrains what we can achieve in control and automation. 3. Principles: Weak Foundations ARE THE ESSENCE and FUN Part OF THE PROBLEM! - First, it is important to highlight three basic principles that should cast doubt on the validity of a biomimicry, mathematics/physics, or a combined approach to control design: Modeling Principle: No mathematical model is a perfect representation of a physical system, but they can be quite good and often can be made to be better. The validity of everything you do with a mathematical model in control engineering rests on the accuracy of that model. Evolutionary Principle: Evolution did not design an organism to fly an aircraft at an altitude of 30,000ft from Columbus, Ohio, USA to Valencia, Spain with hundreds of passengers on-board, or to guide thousand-pound missiles accurately to targets; why mimic animals that do not have the capabilities to perform the task you seek to solve? At the same time, however, organisms of all sorts can sense and react to temperature, light, sound, pressure, chemicals, and electromagnetic fields and they often do this is achieving a feedback control task that there are highly robust in performing (e.g., thermoregulation, guidance, or navigation) since they were optimized via evolution. Such robust bio-loops may provide ideas on how to cope with challenging control problems. - The role of Assumption 2 should be clear here; the reality is that if you want to implement/deploy a real control system you are automatically challenged with coping with significant uncertainties in designing the controller. In an analogous manner, the controller itself must be able to cope with significant uncertainties. Uncertainty Principle: Any real control problem involves coping with uncertainty, in the design process, and for the controller while it is operating. Robustness of a control system is achieved by trying to incorporate uncertainty considerations into the design process; however, there is no way to be absolutely certain as a designer, and every implemented controller must face uncertainty in its on-line decision-making. Coping with uncertainty is the essence of the problem. - Biomimicry approaches seek to gain ideas from biological systems to develop controllers and cope with uncertainty. You would think that we could learn something from the most robust control systems known (living organisms). Conventional mathematical/physics based approaches seek to continually improve models and at the same time generalize mathematical approaches to constructing control systems based on these models. Clearly, the mathematical/physics based developments apply to biomimicry-based methods, and biomimicry offers a scientific approach to generate new ideas/methods for how to control systems (and this is becoming increasingly important as we are moving from single loop control problems to the control of complex systems). This shows some important connections between the two approaches. Moreover, it shows a route to having conventional control approaches make contributions to biological science. - I see no room for conflict, only harmony! I think that that frustrations and hence conflict only arises due to the presence of weak investigations, over-stated claims, entrenched and narrow views, a very unrealistic view by many that an approach must work for a very wide class of problems (rooted in our successes in linear system theory and delusions about how much progress that really represented), and strong personalities. Moreover, the conflict arises from a pervasive lack of comparative analysis in the literature; however, conducting such analysis is very challenging. 4. Comparative Analysis is Difficult: Hence Resolving The Central Dispute Seems ALMOST IMPOSSIBLE! - Good comparative studies must include robustness, experimentation, cost (time, materials), complexity analysis (e.g., processor complexity), mathematical analysis, simulation analysis, manufacturability, understandability, ease of use, expandability, and many other metrics. - It is too often that someone says that some approach is the best or better than some other approach without saying which metric they use to measure this. - While it is certainly the responsibility of someone introducing a new method to say what its relative advantages and disadvantages are relative to current approaches, this is difficult. - A common and often unfair comparison is to compare a nonlinear controller to a PID controller. - In conventional control good comparative analyses are relatively rare (e.g., comparisons of robust nonlinear control approaches), as they are in intelligent control. 5. fuzzy/Neural Vs. Conventional Control 5.1 Fuzzy Control (non-adaptive): - An approach to heuristic synthesis of nonlinear controllers (very similar philosophically to current practices in PID control where nonlinear application-dependent fixes are used). Provides for encoding of intuitions about how to best control a system, ones that do not have to be in the form of a mathematical model. Encoding costs in terminology and computations-is it worth it? Compared to simple interpolation or rule-based (if-then) methods based directly on the syntax of the computer language you are using? Is ease-of-understanding important for a design methodology? For some applications does the fuzzy control methodology shorten the time-to-market? How valuable is this? For non-safety (safety) critical applications? - Do fuzzy control enthusiasts actually use a type of mental model of process dynamics for design? Yes. Are mathematical models available for design? Expensive? Physics/chemistry always understood? All truth and design models are inaccurate to some extent! Hence, the mathematical analysis based on the models is in some (perhaps complicated way) proportional to the accuracy of the models (and this includes cases where robust control is used with models of uncertainty). Design models are often quite simple. But, do fuzzy control enthusiasts avoid very useful physics and mathematics in control law development? Yes, sometimes. We almost always have some relevant physics or simple (perhaps inaccurate) models that should not be ignored, especially for safety-critical applications. The control engineer must try to use all good information about how to achieve good control-it simply does not make sense to ignore good information just because it does not come in a form that your favorite design methodology cannot incorporate (e.g., a mathematical model, or a rule about what is the best thing to do in some situation). Conventional control people often ignore useful heuristic information since it does not fit into their design model form, then compensate for this via tuning/add-ons when it comes time for implementation (e.g., many industrial controllers are one-tenth based on the algorithm given by the output of a conventional control design process, and nine-tenths exception-handling, nonlinear methods to cope with start-up/shut down or different operating conditions or modes of operation). Often, due to the lack of use of a formal mathematical model fuzzy control enthusiasts apply the methods to almost any problem, including ones where well-known and very effective conventional control solutions are known. The use of mathematical models is very useful in classifying plant characteristics and thereby quantifying overall progress in the field of conventional control. How do we quantify successes in the field of fuzzy control? What are the limitations of fuzzy control? Without definitive successes, the field can stagnate since its niche may not be known. The attitude of oh, if you dont have a mathematical model then try fuzzy control is not sound methodology and can lead to failure. - Fuzzy controllers can be verified via simulation, mathematical analysis (stability analysis), and experimentation. How un-conventional is fuzzy control? Not very! It is basically the same methodology that is used for the design of most industrial PID controllers. It seems like there is some hype to make professors famous or get them tenure; I do not think it is a revolution, it is yet another epsilon-perturbation (but what is wrong with that?). - What is the real difference in methodology between fuzzy and conventional control? Where/when do you use physics-based models and mathematics, and intuitions about how to achieve good control. Fuzzy control places importance on intuitions (too much I think), and conventional control places importance on mathematical models (too much I think). We need a flexible viewpoint based on the problem at hand! Keep an open mind, and keep both methods in your toolbox. - No one should be claiming that fuzzy control is robust control without proper theoretical/analytical verification. There are several absurd statements in this regard in the literature. Engineers generally cannot accept a robustness claim for a design, let alone a design methodology, without supporting evidence! - Are there computational advantages in using fuzzy control as compared to conventional control? Doubtful! Sometimes it is quite a bit more expensive computationally due to the generality of the fuzzy system in its ability to represent a wide variety of nonlinear map shapes. However, there are tricks of the trade (often not widely known and used) for computationally efficient implementations of fuzzy controllers. - Does fuzzy control provide for a user-friendly way to tune the controller during implementation studies? Yes, sometimes this is found for some applications. Is it more user-friendly than a conventional controller? Which approach are you most familiar with? - Often, there are close relationships between the designed fuzzy controller and a competing nonlinear controller (input-output equivalent?). Fuzzy control people generally need to know more about physics for modeling and nonlinear control, and some nonlinear control people have no practical insight, only mathematical insights. There is a need for mathematicians to understand the physics that their model is based on, and the limitations of the model. 5.2 Neural Control / Adaptive Fuzzy Control: - While neural control is based on optimization-based (e.g., gradient, least squares) tuning of neural network nonlinear mappings (e.g., in indirect adaptive control to identify plant dynamics, and in direct adaptive control to estimate a controller) using plant input-output data, and adaptive fuzzy control was first based on emulation of human adaptation expertise, both can be profitably viewed as on-line function approximation based methods. Both neural networks and fuzzy systems are tunable approximators (nonlinear mappings, interpolators). The same optimization-based adaptation methods can be used to tune fuzzy systems using plant input-output data. - A variety of stable neural control and stable adaptive fuzzy control methods exist. These extend the class of nonlinear uncertain systems that is considered in conventional adaptive control to those that have unknown functions that define dynamics, not just unknown parameters. However, there are limitations to the theory: there are certain locality constraints due to an ability to only approximate over a compact set, and only certain forms of nonlinear systems are considered. The methods depend heavily on conventional mathematical analysis of adaptive control systems. - Experimental comparisons of heuristic adaptive control methods based on neural networks or fuzzy systems and stable adaptive neural/fuzzy controllers with conventional adaptive controllers shows a few advantages for the neural/fuzzy approaches: (i) ease of implementation (our experiences show it just works in places where it is very difficult to implement a conventional adaptive controller-it seems that there are fundamental issues in sensitivity that are not well-understood), (ii) ease to incorporate intuitions about how to achieve good adaptive control, and (iii) applicability to wider class of plants. Disadvantages include computational complexity and the ad hoc nature to the heuristic approaches that demands you understand your plant (but is that a real disadvantage? I think the turn the crank approaches often lead to problems). - Tuning of nonlinear in the parameter approximators is a current direction. It is hoped that solution of this problem will help broaden the class of plants, and address problems with computational complexity of the methods. It is a challenging problem. 6. Intelligent Control: Biomimicry for Control and Automation - The domains of application of neural/fuzzy control and conventional control are relatively close in several ways. In other ways they are not (e.g., for hierarchical and supervisory control). - It may be best not to think of intelligent and conventional control methods as competing, but as complements to each other! How? Note that intelligent control is much more broad than fuzzy systems and neural networks (and genetic algorithms). I see the ultimate goal of intelligent control as being the composition of many biomimicry-based methods. For example, it also involves expert systems, planning systems, attentional systems, stochastic optimization for design (e.g., via genetic algorithms), social intelligent foraging (e.g., bacteria, ants, bees), team fighting against an intelligent adversary team (with game-theoretic foundations). While often not recognized, the scope is very broad, and for some of these problems there are few if any conventional control solutions (sometimes the reason for this is the lack of a mathematical model, problems with complexity arising from stochastic hybrid system dynamics, or high levels of uncertainty), but there are often solutions from other areas of engineering (e.g., robot path planning, resource allocation, automata, optimization theory, or mathematical biology). A forth-coming book will provide a cohesive treatment of all these areas (see references). - The scope of the problems considered by intelligent control methods is very broad, and includes many complex automation problems. For example, it includes process-wide (or enterprise) optimization for large hierarchical control systems in manufactuing and process control. Also, it includes approaches for cooperative control for multiple autonomous vehicles. Such general automation problems require a more general view of the design methodology (e.g., perhaps including software engineering), but many principles studied in conventional control apply (e.g., robustness, design methodology involving truth and design models, and the role of mathematical, simulation-based, and experimental analysis for verification and certification). - Distributed and hierarchical methods grow naturally from biomimicry of groups of organisms that can communicate. Distributed rule-based, planning, attention, learning, foraging, and fighting strategies are being studied, each possibly in the context of evolution of the distributed system. - There are many relations between the various fields. For instance, foragers may use an attentional system and learning in order to plan how to find energy resources. All these processes are shaped by evolution. - It may be that the true advantages of the biomimicry viewpoint lie in these areas, where the foundations of the conventional model-based control approaches lack good models since there are high amounts of uncertainty. However, there is a continual development of mathematical models and hence applicability of conventional methods. This fact should not be ignored, and the developed methods should not be either. Do the intelligent control methods simply provide ways to solve problems now, that will later be solved in better ways via mathematical approaches? Is there a moving boundary splitting applications where conventional vs. intelligent control methods should be used? Is what we consider to be intelligent changing? - There are already many approaches to mathematical analysis of such intelligent systems. Two theoretical foundations for the methods are stability analysis and optimization theory: Optimization Theory: Optimization is used in choosing which plan to select in planning systems, what to focus on in an attentional system, and in information-gathering in learning and forgetting. Evolution is stochastic optimization for design of robust systems. There are synergistic effects between learning and evolution (e.g., evolution designs learning systems, learning can in a certain sense accelerate evolution, and there seems to be an optimal balance between genetically-determined instincts and learning capabilities). Evolution creates the optimal foraging/fighting methods used by organisms, and these in turn impact the evolution of the species. Nonlinear and Stability Analysis: There are many useful roles for nonlinear analysis in the biomimicry approaches: stability analysis of fixed neural/fuzzy controllers, discrete-event system theoretic stability analysis of expert controllers, stable planning, boundedness analysis of attentional strategies (to show that predators/prey are not ignored for too long), stable adaptive/learning control, convergence analysis of deterministic and stochastic optimization methods, stability analysis of cohesiveness properties of swarms, and theoretical game theory. - There are already efforts underway (J. Doyle, Caltech) to develop general robustness concepts/principles for biological systems and complex engineered systems. - What does the biomimicry viewpoint offer to the solution of complex control/automation problems? It offers a cohesive framework for relating all the approaches, a way to introduce new ideas and functionalities, a way to explain complex dynamical systems and concepts, and connections to underlying science (biology, physics). - Evolution designed robust organisms and this robustness is achieved via decision-making and control. By some measures, the most successful control systems on earth are biological ones. What can we learn from them? What can biological science learn from the control-theoretic viewpoint? 7. Concluding Remarks What do I wish? I hope that the hype by some about fuzzy/neural/intelligent control is toned down. I hope that the hype about some areas of conventional control is toned down. I hope we can have respectable scientific discussions about comparative analysis between methods/philosophies that clearly includes the views of experimentalists. I hope we can seek to be honest about the value of the methods that are most dear to our hearts, and as fair as possible when conducting comparative analyses. Ultimately, it is often not whether you use intelligent or conventional control methods, but whether you are intelligent in applying your chosen design methodology. Lets not be rigid in our thinking about design methodologies and seek problems to fit our solutions or to simply promote our pet approaches; let the application dictate the choice of the design methodology and solution techniques. Authors Note About References: I want to apologize for referencing only publications of my students, a few colleagues I have collaborated with, and myself. I was simply lazy here, cutting the references from my other documents and placing them in this one. I assure you that in each of the referenced publications many relevant publications of others are provided. I hope I did not offend anyone with my laziness here! A FEW ReferenceS [1] Passino K.M., Bridging the Gap Between Conventional and Intelligent Control, Special Issue on Intelligent Control, IEEE Control Systems Magazine, Vol. 13, No. 3, pp. 12-18, June 1993. [2] Passino K.M., Fuzzy vs. Conventional Control, Proc. IEEE Int. Symp. on Intelligent Control, pp. 511-512, Chicago, IL, Aug. 1993. [3] Passino K.M., Towards Bridging the Perceived Gap Between Conventional and Intelligent Control, Chapter 1 in Gupta M.M., Sinha N.K., eds., Intelligent Control: Theory and Applications, pp. 1-27, IEEE Press, Piscataway, NJ, 1996. [4] Kevin M. Passino and Stephen Yurkovich, Fuzzy Control, Addison Wesley Longman, Menlo Park, CA, 1998. [5] Moudgal V.G., Passino K.M., Yurkovich S., Rule-Based Control for a Flexible-Link Robot, IEEE Trans. on Control Systems Technology, Vol. 2, No. 4, pp. 392-405, Dec. 1994. [6] Laukonen E.G., Passino K.M., V. Krishnaswami, Luh G.-C., Rizzoni G., Fault Detection and Isolation for an Experimental Internal Combustion Engine via Fuzzy Identification, IEEE Trans. on Control Systems Technology, Vol. 3, No. 3, pp. 347-355, Sept. 1995. [7] Kwong W.A., Passino K.M., "Dynamically Focused Fuzzy Learning Control", IEEE Trans. on Systems, Man, and Cybernetics, Vol. 26, No. 1, pp. 53-74, Feb. 1996. [8] Spooner J.T., Passino K.M., "Stable Adaptive Control Using Fuzzy Systems and Neural Networks," IEEE Trans. on Fuzzy Systems, Vol. 4, No. 3, pp. 339-359, Aug. 1996. [9] Ordez R., Zumberge J., Spooner J.T., Passino K.M., "Adaptive Fuzzy Control: Experiments and Comparative Analyses," IEEE Trans. on Fuzzy Systems, Vol. 5, No. 2, 167-188, 1997. [10] Zumberge J., Passino, K.M., "A Case Study in Intelligent vs. Conventional Control for a Process Control Experiment," Journal of Control Engineering Practice, Vol. 6, No. 9, pp. 1055-1075, 1998. [11] Moore M.L., Musachio J., Passino K.M., Genetic Adaptive Control for an Inverted Wedge: Experiments and Comparative Analysis, Engineering Applications of Artificial Intelligence, Vol. 14, No. 1, pp. 1-14, Feb. 2001. [12] Lennon W.K., Passino, K.M., "Intelligent Control for Brake Systems," IEEE Trans. on Control Systems Technology, Vol. 7, No. 2, pp. 188-202, March 1999. [13] Spooner J.T., Passino K.M., Decentralized Adaptive Control of Nonlinear Systems Using Radial Basis Neural Networks, IEEE Trans. on Automatic Control, Vol 44, No. 11, pp. 2050-2057, Nov. 1999. [14] Jenkins D., Passino K.M., "An Introduction to Nonlinear Analysis of Fuzzy Control Systems," Journal of Intelligent and Fuzzy Systems, Vol. 7, No. 1, pp. 75-103, 1999. [15] Ordez R., Passino K.M., Stable Multiple-Input Multiple-Output Adaptive Fuzzy/Neural Control, IEEE Trans. on Fuzzy Systems, Vol. 7, No. 3, pp. 345-353, 1999. [16] Kwong W.A., Passino K.M., Lauknonen E.G., Yurkovich S., Expert Supervision of Fuzzy Learning Systems for Fault Tolerant Aircraft Control, Special Issue on Fuzzy Logic in Engineering Applications, Proceedings of the Inst. of Electrical and Electronics Engineers (IEEE), Vol. 83, No.3, pp. 466-483, March 1995. [17] Diao Y., Passino K.M., Fault Tolerant Stable Adaptive Fuzzy/Neural Control for a Turbine Engine, IEEE Trans. on Control Systems Technology, May 2001. [18] Ordez R., Passino K.M., Adaptive Control for a Class of Nonlinear Systems with a Time-Varying Structure, IEEE Trans. on Automatic Control, Vol. 46, No. 1, pp. 152-155, Jan. 2001. [19] Ordez R., Passino K.M., "Indirect Adaptive Control for a Class of Time-Varying Nonlinear Systems,'' To appear in the International Journal of Control, 2001. [20] Moudgal V.G., Kwong W.A., Passino K.M., and Yurkovich S., Fuzzy Learning Control for a Flexible-Link Robot, IEEE Transactions on Fuzzy Systems, Vol. 3, No. 2, pp. 199-210, May 1995. [21] Jeffrey T. Spooner, Raul Ordonez, Manfredi Maggiore, and Kevin M. Passino, Adaptive Control and Estimation for Nonlinear Systems: Neural and Fuzzy Approximator Techniques, To appear John Wiley and Sons, NY, 2001. [22] Passino K.M., Intelligent Control: An Overview of Techniques, in T. Samad, Ed., Perspectives in Control Engineering: Technologies, Applications, and New Directions,'' pp. 104-133, IEEE Press, NY, 2001 [23] Kevin M. Passino, Biomimicry for Optimization, Control, and Automation, Writing this text book now, submitted for publication, to appear 2002, I hope... 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Passino3Lyapunov:Desktop Folder:IFAC FN Wkshp:IFAC FN paperKevin M. Passino3Lyapunov:Desktop Folder:IFAC FN Wkshp:IFAC FN paper Kevin Passino=Lyapunov:Intelligent Control:IFAC FN Wkshp:IFAC Passino paperssht@D5=/;GCJOJQJmH C$Eƀb')*X<*cchdNeeafbgghSijjkIllmnaopprqGrssssetftgtA @@@R @@ @@$@A8@@@Ҭ@"@A@ @@ @@@@@ @@@@@@@@$@@.@@n@@@@,@@@@x@@@@@@@@@@@\@@@@@@\@@@@@@0@@@@d@@f@@@@@GTimes New Roman5Symbol3 Arial9New York3Times"h`UFmXF_0;dt;Fuzzy vsKevin M. Passino Kevin PassinoAC FN paperKevin M. Passino3Lyapunov:Desktop Folder:IFAC FN Wkshp:IFAC FN paper Kevin Passino=Lyapunov:Intelligent Control:IFAC FN Wkshp:IFAC Passino papersskt@ccD5cc/;GCJOJQJmH $a$;GCJOJQJmH F;GCJOJQJmH 5GCJOJQJmH F5GCJOJQJmH 0J C$Eƀb'\%)ABWXefg<cc-cckdQeedfeghhVijjkLllmndoppuqJrsssshtitjtA @@AJ @AR @@ @@ @@ @A @@@@ @ @ @@@@$@ A8@ @@Ҭ@"@A@ @@ @@A@b@@@@ @@@@@@@@$@@.@@n@@@@,@@@@x@@@@@@@@@@@\@@@@@@\@@@@@@0@@@@d@@f@@@@@GTimes New Roman5Symbol3 Arial9New York3Times"h`UFrXFb_0;d#u;Fuzzy vsKevin M. Passino Kevin Passino FMicrosoft Word DocumentNB6WWord.Document.8 ՜.+,0 hp  'Dept. Elect. Eng., OSU0#u  Fuzzy vs Title Oh+'0l   ( 4 @LT\d' Fuzzy vst uzzKevin M. PassinordeviNormal.Kevin Passinono165Microsoft Word 9.0d@rE@(P@+H*b_8. 3 kzjbjb^^^^^h<h<slHHL$ P D%$TVVVVVV,I itd~T@8T@@Tx88P88%%8ݬݬ8Biomimicry, Mathematics, and Physics for Control and Automation: Conflict or Harmony? Kevin Passino Dept. of Electrical Engineering, The Ohio State University 2015 Neil Ave., Columbus, OH 43210-1272 passino@ee.eng.ohio-state.edu http://eewww.eng.ohio-state.edu/~passino Abstract Intelligent control focuses on the emulation of biological systems to provide automation for high technology systems. This emulation, or what is sometimes called biomimicry arises in the following two ways: via emulation of biological system functionalities or behaviors (e.g., neural networks), or via automation of control tasks performed by humans or animals (e.g., via fuzzy control and human-mimicry). Conventional control focuses on the development of physics-based mathematical models (perhaps using data and system identification methods) and the use of these in mathematical approaches to controller synthesis and nonlinear analysis of control systems. What are the connections between a biomimicry and conventional methodology? Are there advantages of one approach over the other? How should we compare the methods? Only via comparative experimental implementations for practical and challenging real-world problems? For which applications? Does there really exist one best approach for all possible applications? Could it be that one method is better for one class of applications, and the other is best for another class? Or, should we ignore practical applications and implementations, assume that mathematical truth and design models are perfect, and then compare the two approaches via mathematical proofs that compare robustness properties or strength of stability/convergence results for specific classes of nonlinear systems? To answer these questions we first use a case study of fuzzy/neural control (fixed and adaptive) relative to conventional control. Then, we discuss the questions for the more general case of intelligent vs. conventional control, where more general automation problems are also considered (an appropriate context for a complete scientific discussion on this topic). 1. Introduction The author has spent over 15 years trying to determine if there are advantages to fuzzy and neural control, and more generally intelligent control, relative to conventional control methods. This paper represents an attempt to summarize, as briefly as possible, some of the findings. During the talk several questions are posed in order to seed discussions; comparative analysis is difficult and I hope to learn from you, just likeDNEN S SUUWWWWZZ[[&^'^Y`Z```GbHblcmcncqcccgghhhhhi jj^klmm[noopTqrrmstGuvvfwgwAxyyyyyy zzzzhzizjzkz Fory Lohrey-Birch Larry J. GutGary A ChastagnerDr. Dean GlaweJ. Scott CameronConnie RalstonDon JoyDr. John BrownElizabeth H. Beers F. J. Peryea Nana Simone Nobbs, C. A. Martin Sauer Larry Gut Gut, Larry J.Jack W. Jenkins Don Thomson John BrownRebec     '()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`abdfhijklmnpqrstueca RedingerChristian Krupkejim John DunleyGerald Jubb Jr. Karen Fischer BJ CarlsonDonna Jean Cofield Mac Parks Susan CobbMichael Skinner Carl Siechert Chris WoodAvery DennisonIdea ArtOn PaperCynthia Mayfield Gordon MillsCornelius F. Ivory Richard Okita Erin Adams Skinner LabCollege of EducationKatherine M. Byrne Sandra Ristow Eric MiragliaStephen C. EhrmannDr. Kevin C. FacemyerKevin C. 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PieniazekComputer Services Robert PaxtonOkntPieniazek, Norman J. JAN TENGEntwicklungsphysiologieFlinders UniversityPeter Sjstrand Sigurd Huseby It-tjenestenAnne-Lise Tufte Kari StrandeTorunn Andersen Anne StrandLP0 Jan Stensby Ralph Bchler ngemar FriesScott Camazine DENEUBOURG MutinelliEmilie von EssenShi WeiAntonio NanettisurfProf.Dr.LIONEL SEGUI GONALVES Student_PC Jenny Curtis Sally Menefee TavasolianSara A. GallaugherKelly Davis-OrrJens KleinholzAbdulaziz AL-Qarnianami s. patelStephen F. WinterRothGE Aircraft Engines John K Riege Tom Gentry Steve Matson Lee LapierreGeneral Electrickj ART STORERyoung Franchise,/O=GE/OU=AEEVENDALE/CN=RECIPIENTS/CN=ABBC23T Dan GilmoregeMahoney, Maureen (GEAE)WENNERRichard E Kehl Scott BradyNAVAIR Lee LaPierreLouis J. BernazzaniAllen SchwartzbergAircraft EnginesALAN L. GOSHERTGE Corporate Research & Dev.USER6 OnLine CenterKEN COOKFAABruce G. SchingsGEAE Bob DarbyHelen D. WashingtonJOSEPH M DEBOYBob RostBernard E. ShayAllen P Schwartzberg M. 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HoweBrowning Ronald RodgerGarver B. FaulhaberLillian A. RetallackWestinghouse SRCSRS nadine sheaBarbara A. ZaragozaAPT Dale Tuggle Scott WillmsESA-WERobert J. HanrahanDaniel RusthoiGreg Van TuyleDonna Baughman Jeff HudsonMary Ann GriffinSavannah River SiteBilly CampbellJohn P. HintonESH-EISCIC-10 Jay BlewettWestinghouse Savannah River Co.Application Administrator BILL MEYERJohn Lagomarsino Leslie GoffPaul Kim Georgia Tech Wiliam SayleSarah Abel-DeLucaJavier R. EchauzDivision of NeurologyPAUL E. JANSONOwens Corning UserSherrie S. AungerJames P. McCallionEmployeeJonathan W. KruegerjxaGail M. O'ConnorBarbie SullivanBetty Lise AndersonGary L. Kinzel Betty Lisesheena Drew SmithMichael Johnsonann foodCommunications Division, EEEBrasca Claudio Grace MarquesICCProject Room 1Dr.M.A. JohnsonDr.M.A. Johnston Reza Katebi Mike Grimble M J Grimblem spathopoulosMiBi Naveen GargIng. Radz BEVELAQUA DI C. KnollBranislav MI IETAJacqueline Wilkie jacqueline Daniel KhooUNIVER. TECHNO. 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DUNOYERSchool of EngineeringBurnhamauthorised user School of MIS Bernard DoylelbrA.Ordys van der Klugt C. de KeizerVineet P Dravid Microsoft D. Feucht Dan SimonJohn B. Peatman Lee JohnsonTrial Office 98maxJenniferPortia IversenR.H. Sandler, MDDepartment of Pediatrics CAN ResearchEd KorbHarald Vaessinliu-yjbcruzRaul and Junko Ordonezpolkd Umit Ozguner Kevin PassinoHR;E 00h@u@KDLDFFIIMJXJeNqNPPQQRRjVuVXXYYYYt[~[%d)dddketeeeeeeeafifffffffbggghhhhSi[i"j*jl"lllmmmmmmnnaohop pppppppqqqqqqrrrr&s0ssshtKevin M. Passino3Lyapunov:Desktop Folder:IFAC FN Wkshp:IFAC FN paperKevin M. Passino3Lyapunov:Desktop Folder:IFAC FN Wkshp:IFAC FN paperKevin M. Passino3Lyapunov:Desktop Folder:IFAC FN Wkshp:IFAC FN paperKevin M. Passino3Lyapunov:Desktop Folder:IFAC FN Wkshp:IFAC FN paperKevin M. Passino3Lyapunov:Desktop Folder:IFAC FN Wkshp:IFAC FN paperKevin M. Passino3Lyapunov:Desktop Folder:IFAC FN Wkshp:IFAC FN paperKevin M. Passino3Lyapunov:Desktop Folder:IFAC FN Wkshp:IFAC FN paperKevin M. Passino3Lyapunov:Desktop Folder:IFAC FN Wkshp:IFAC FN paperKevin M. Passino3Lyapunov:Desktop Folder:IFAC FN Wkshp:IFAC FN paper Kevin Passino=Lyapunov:Intelligent Control:IFAC FN Wkshp:IFAC Passino paperssht@D5/;GCJOJQJmH $a$;GCJOJQJmH F;GCJOJQJmH 5GCJOJQJmH F5GCJOJQJmH 0J C$Eƀb'D%)ABWXefg<*cchdNeeafbgghSijjkIllmnaopprqGrssssetftgtA @@AJ @AR @@ @@ @@ @A @@@@ @ @ @@@@$@ A8@ @@Ҭ@"@A@ @@ @@@@@ @@@@@@@@$@@.@@n@@@@,@@@@x@@@@@@@@@@@\@@@@@@\@@@@@@0@@@@d@@f@@@@@GTimes New Roman5Symbol3 Arial9New York3Times"Ch`UFqXFb_0;d#u;Fuzzy vsKevin M. Passino Kevin Passino FMicrosoft Word DocumentNB6WWord.Document.8 ՜.+,0 hp  'Dept. Elect. Eng., OSU0#u  Fuzzy vs Title Oh+'0l   ( 4 @LT\d' Fuzzy vst uzzKevin M. PassinordeviNormal.Kevin Passinono163Microsoft Word 9.0d@,ޑE@(P@nH*b_micry, Mathematics, and Physics BD$a$ h3 kzjbjb^^^^h<h<slHHL$ P D$HJJJJJJ,% Etvvd~H@8T@@Hx,,P,,,,Biomimicry, Mathematics, and Physics for Control and Automation: Conflict or Harmony? Kevin Passino Dept. of Electrical Engineering, The Ohio State University 2015 Neil Ave., Columbus, OH 43210-1272 passino@ee.eng.ohio-state.edu http://eewww.eng.ohio-state.edu/~passino Abstract Intelligent control focuses on the emulation of biological systems to provide automation for high technology systems. This emulation, or what is sometimes called biomimicry arises in the following two ways: via emulation of biological system functionalities or behaviors (e.g., neural networks), or via automation of control tasks performed by humans or animals (e.g., via fuzzy control and human-mimicry). Conventional control focuses on the development of physics-based mathematical models (perhaps using data and system identification methods) and the use of these in mathematical approaches to controller synthesis and nonlinear analysis of control systems. What are the connections between a biomimicry and conventional methodology? Are there advantages of one approach over the other? How should we compare the methods? Only via comparative experimental implementations for practical and challenging real-world problems? For which applications? Does there really exist one best approach for all possible applications? Could it be that one method is better for one class of applications, and the other is best for another class? Or, should we ignore practical applications and implementations, assume that mathematical truth and design models are perfect, and then compare the two approaches via mathematical proofs that compare robustness properties or strength of stability/convergence results for specific classes of nonlinear systems? To answer these questions we first use a case study of fuzzy/neural control (fixed and adaptive) relative to conventional control. Then, we discuss the questions for the more general case of intelligent vs. conventional control, where more general automation problems are also considered (an appropriate context for a complete scientific discussion on this topic). 1. Introduction The author has spent over 15 years trying to determine if there are advantages to fuzzy and neural control, and more generally intelligent control, relative to conventional control methods. This paper represents an attempt to summarize, as briefly as possible, some of the findings. During the talk several questions are posed in order to seed discussions; comparative analysis is difficult and I hope to learn from you, just likeDNEN S SUUWWWWZZ[[&^'^Y`Z```GbHblcmcncqcccgghhhhhi jj^klmm[noopTqrrmstGuvvfwgwAxyyyyyy zzzzhzizjzkz FWWZZ[[&^'^Y`Z```GbHblcmcncccgghhhh $$a$ h$a$ h$H^`Ha$ $a$ hhhi jj^klmm[noopTqrrmstGuvvfwgwAxy h^h`hd^h` hd^h` $$a$ hpq*qqqqr|rr-sIss6t8ttu;uuuuvvvw>wwx*y`yyyyyyyy zz4z5z6zSzhzjzkz\֬جBD>*CJCJ6CJCJ6B*CJph B*CJph6CJCJ2yyyyyy zzzzhzizjzkz\ЬҬԬ֬ج@B$a$ !$$a$ hhd^h`- 000P/ =!n"#S$% S|HH(FG(HH(d'@1 000P/ =!n"#S$% P S|HH(FG(HH(d'@ Keywords: Biomimicry, control, neural control, fuzzy control, adaptive control, intelligent control. WWZZ[[&^'^Y`Z```GbHblcmcncccgghhhh $$a$ h$a$ h$H^`Ha$ $a$ hhhi jj^klmm[noopTqrrmstGuvvfwgwAxy h^h`hd^h` hd^h` $$a$ hpq*qqqqr|rr-sIss6t8ttu;uuuuvvvw>wwx*y`yyyyyyyy zz4z5z6zSzhzjzkz\֬جBD 5;CJ>*CJCJ6CJCJ6B*CJph B*CJph6CJCJ4yyyyyy zzzzhzizjzkz\ЬҬԬ֬ج@B$a$ !$$a$ hhd^h`- 000P/ =!n"#S$% S|HH(FG(HH(d'@1 000P/ =!n"#S$% P S|HH(FG(HH(d'@ Keywords: Biomimicry, control, neural control, fuzzy control, adaptive control, intelligent control. micry, Mathematics, and Physics BD$a$ h