r ctiv Thomas Ting

ver the past scvcra1 dccades, control theorists have gcnaated an iiiiprcssive array OS new techniques [hat span a wide variety oSSields, including classical control, optimal control, adaptive control, robust control, nonlineer control, hierarchical control, and

intelligent control. Concurrcntly, however, one of the dissp- pointincnts within sonic segments of the ctintrol community i s the perceived slow rale at which rhcse techniques have bccn adapted into industrial practicc. The purpose of this special scc- tion i s to address this issuc and propose somc siiggcslions to as- s i s t in tailoring thcory for practice from an acrospacc perspective.

Control thcnrists oftcn wonder why industry i s not always anx- ious Lo implement the latest dcvelopmcnts in control theory. The b ,' ribic answcr i s that control engineers in industry will almost 81- ways iinpleincnl the simplest, most cost-cfrectivc control mcthod- ulogy that satisfies their iiiiincdiate goals. Many applications liave huge software, production, and personnel training incentives that minimize the magnitude of changes from a previous gencration of designs. Thus, rhc next-generation design inay oftcn he structur- ally identical to'thc previous one, with changes only in some tuncd parameters (easily changeable in soltware). In more drastic cases, the changes inay include ail hoc reSinements to 8n existing control algorithm structure to trcal some dil'ficult operating conditions. Rarely, though, does ;in industrial application control design start Srom a blank sheet olpapcr.

New control tcchnologies arc thus cmhnicedhy industry only when there i s n clcpr aiid present nccd that c;lnnot be adcquately addressed through currently employed methods. This necd can hc performance bascd (improved closcd-loop syslcin character- istics), c~impuretionally hascd (less computation required), dc- sign based (reduced development time), cost bascd, end there arc any iiuinbcr ol' othcr possibilities. Therefore, the ability of con- trol theorists to inSluence industry adaptation of new control techniques i s somewhat analogous tu h a t of ;in individual at- tempting to push on a ropc. It's much easier ii the individual holding tlic othcr end US the ropc i s willing to pull.

A Process for Turning Theory to Practice The entire proccss of developing and using new control the-

ory can he divided into three major steps: innovation, facilita- lion, ;md implementation. The first step essentially refers to theurctical development of ti new control concept. The second involves applying new control concepts Lo prcvioosly untried ap- plications and cvaluating the rcsults. The final step i s the proce- dure of embedding a new approach into the standard engineering design process. These three steps are oftcn performed by differ- ent groups of individuals. A standard communication tlowchart among these three groiips i s shown in Fig. I. Note that the facili- tators bear primary responsibility for communicating with the other two groups. Some additional deteils ol'the three steps are discussed below.

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:P, 1. Communication flow.hart tbr control theorv devclmment

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(typically an advanccd dcvelopmenl engineer or engineers at an industrial research ccntcr). This facilitator then bceins to search

fit for a speciSic application, and on-the-fly ad hoc modifications are required. Engincers who conduct this work must possess both sufficient application-specific domain knowledge and rele- vant control theory capabilities. Good communication skills and tcchnical versatility areessential for this role because thesccngi- neers musi interface effectivcly with both the innovalors and the implemcntcrs.

implementation

This step consists of the nuts-and-bolts integration of a new design approach into a mainstream engineering procedure. This is ultimately the respoiisihility of thc ciad users (typically design or developmcnt cngineers in an industrial corporation, national laboratory, or governmcnt scientific center), because they are the ones who will be using the approach on an ongoing hasis. How- ever, thc interface hetwccn the facilitatnrs and implemcntcrs is crucial because the facilitators must take care to provide adc- quare resources to ensure a completc technology lransfer. This may involve preparatinn of user-friendly software design tools, thorough yct readable technical documentation, on-site techni- cal support, and technical training for pcrsonnel receiving thc new technology. As a last rcsort, a facilitator may lranskr to a project and begin working as an implementer, hut this is not al- ways a prefcrred approach.

Communication is the Key The absolute key to thc slructure prcscnled above is coinmuni-

cation. 111 this framework, all ofthe pull has to come from the end user. The control enginecr synthesising the production designs (the implementer) has to convey his or her needs to ii facilitator

and implementation. for oossible methods for solviiie the txoblern. Durinr this process, I . 1 . or perhaps oncc candidate stnitcgies have bccn identified, the h - cilitator may communicate these needs or seek assistancc Crom an appropriate control innovator. It is cssential for each party within this process to undersland and address the weds and requircmcnts of its "customer." They iiiust all "go the extra mile" to cnsiire that the customer can operate indepcndently with the new technology being dclivered. If not, after a few iterativc attempts lo resolve tbe problem, the engineers hecomc frustrated with the new technol- ogy and either seekalternativesolutions or try to gct by with modi- fications

abstract in nature, is usually conducted by university professors and graduate students. The results arc communicated to others in the technical communiiy via technical presentations and conler- ence or journal papers. Thc ideas emanating from this step pro- vide a continuous flow of design options for facilitators. The communication link between the innovators and the facilitators is essential to maintaining quality of this The facilita- tors must communicate their necds effectively to thc innovators, who in mnSt beresponsive to those needs toellsure the high- est potential applicability of their idcas. It is still possible for in- their existing solulions~

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novators to promote new concepts through technology push, but the probability of success is greatly dependent on thc pull at the

In reality, there are two m:~,jor struclural obstacles that inter- fere with this ideal conlrol theory development and implemcnta-

other end

Facilitation This step consisis or maintaining awareness of the state of the

art in control theory and analyzing the relative benefits n S new techniques in applications where performance or design im- provements are required. Much of the woi-k in this step involves ensuringcompatibility between assumptions required by the the- ory and the reality of a specific application. Critical components of this step include model development, controller synthesis, analysis, simulation and evaluation, and model and conirollcr or- der reduction. Often, theoretical approaches will not he a perrect

tion process. First, control engineers in industry do not place a high priority on publicizing their technical performance road- blocks. I n an industrial setting, publishing and presenting techni- calpapcrsisnotan overwhcliningpriority.'lhisisespecially true in situations whcre time dedicatcd to preparing publications would conic at the expense of existing pro,ject work. Second, in many industries, proprietary in~ormation concerns precludc the preparation of technical papcrs. Companies are highly sensitivc to the ever-increasing threat of global coinpctition and are very proteciivc of their classified information. Thus, passively identi- fying technical issues rclcvant to industry may he extremely dif- ficult for control innovators to do.

46 IRKR Control Sy.stems

Special Section Contents The thrcc articles in this special section havc hcen selected to

provide a rangc of viewpoints about some of the current and fii- ture challenges lacing the control community. The authors have pretlomiiiantly aernspace or aciidcmic hackgrounds, so their ill- puts may not necessarily rcflect the needs US othcr control appli- cation domains. Although I did not always agree with tlie authors’ vicwpoints, I found each d t h c articles to he enlighten- ing. I hope you, thcreader, have asirnilarcxpericiice. I3ricf intro- ductions to each of the tliree articles follow.

Tactical Missile Control As described above, a successful application di~main for new

control thcory has two requircments: a performance-based nccd aiid a iinvel control approach that can help meet this nccd. Sincc it i s unreasonable to expect a control cngiiiecr to addrcss hoth US these issues simultaneously. the initial question i s usually one such as: “Arc my current control algurithiii synthesis approaches capable of producing designs Ihat meet my present (and possibly luturc) performance requirements?” If the answer to this ques- tion isalfirinative,nnostlikelynotliing will changc. Iftheanswer i s no, the control engineer inay begin a search Sor p~issible alter- native methods.

Based on this approach, the tactical missile industry appears to bc a textbook example of an application domain poiscd to de- inand and reap the benefits of advanced control theory. Many current inissilc algorithm implcrnentations s t i l l feature varia- tions of long-standing classical dcsigii approaches such as pro- portional i i iv igat i i in guidance and PIU control. The cffectivencss of such designs i s undeniable, and, until recently, they were morc than capable of achieving desired performance levels. But the fundamental performance demands of this indus- try are now changing. For example, the increascd agility of tar- gets requires that missiles possess an unprcccdented level of agility over an expanded envelope of flight conditions. Concur- rently, the desire for hit-to-kill capability requires devclopmcnt o l guidance strategies that can achieve pinpoint accuracy while siinultaneously meeting terminal-state constraints needed for penetration,

Ridgely and McParland elucidate many ol‘lhc control sysleni needs for present and future statc-of-the-art missile systcnis. They discuss general performancc requircments and design con- straints and review the current algorithm synthesis procedurc. The erticle culminates with an identification nf several key en- abling control technologies of particular intcrcsl to tlic tactical inissilc industry: lixed-structure control, paramctcr optiniization methods, linear paramctcr varying control, nonlincar dyilainic inversion, and integrated guidance and autopilot design. Past and present work in each of these areas i s reviewed, as well as out- slanding challenges for the future.

High-Performance Design: Flexible Space Structures

Control system design has historically heen treated, in an overall system context, as a last-step optimizing technology. In most applications, thc open-loop plant structurc lias long been established and Sroren heforc the cnntrol engineer i s asked to synthcsize a controller that wi l l “optimirc” tlie closed-loop syr- tern performance. A I this point, tlic control engineer inust at- tempt to achieve prespccil‘ied performancc and robustness goals

within a heavily constrained Sramcwnrk. Ifthesc goals are nnt at- tain;ihle via iteration on the controller design, they are typically deemed unachievnble and are relaxed.

The implications nf such an approach arc dramatic. Control theory comes to be viewed, not as an enabling technology, but as a means to “do the best you ci i i i ’ with an cxisling plant striicture. System hiirdware designers do not routincly consult with control engineers a priori ahout the drawbacks of a potcntially slow or saturating actuator, or the hcnefits of a slightly innre expcnsivc hut morc accuratc sensor. These types of decisions, made inde- pendently of the control enginecr, often havc a significant impact oii overall closed-loop system pcrSormance.

Joshi addresses this issuc by outlining a ncw role for control scieiicc in high-performance systems and its implication for cnn- trol theory. Spccilically, he challcnges the “last-step control” ap- proach that has histnrically limited the influence of control scicncc and identifies the need for ii new systcm-coiitr~il-analysis-synthe- sis (S-C-A-S) apprmich that links system and controller design. This apprnach advocates cxpansion of the standard design process to include siinultaneous iterations on hoth the system and contrn- ler design. Such an approach rcquires control engineers to get in- volvedearlicr in the design phase when maior systcinchangcs arc still possible. The examples in this arlicle focus 1111 tlexible spacc structure applications and how the cost and performance require- ments of such applic:itions ncccssitate an S-C-A-S design. Past controls research rclaled to this design approach i s reviewed, and future research needs concentrating on sensitivity, onccrtainty, ro- bustncss, and optimality are identil‘icd.

An Innovator’s Viewpoint

An nld saying in the control lield is Ihal “most ol the work in a control dcsign is in developing the model.” With the current em- phasis on virtual design through computer-aided design packages, and tlie desire to implement inore sophisticatcd, model-based con- trol techniques, this saying appcars to be more true today than ever. Modern control techniques can achievc rnagnificcnt results with state-space models. Not cven the inost robust controller, however, can fully compensatc for a poor modcl.

Complicating this issue is the fact that developing a quality inodel typically requires a significant amount of domain cxper- tise. For instancc, im aernspace enginccr who i s highly proficient at dcveloping aircraft models may not hc the best choice for de- veloping models Sor a chcniical process control system. Con- verscly, a chemical engineer inay not bc tlie hest choicc for developing aircraft models. A good control design modcl i s one that slrikcs a proper balance between fidelily and simplicity. To achieve this halancc, tlje engineer must have an adequatc feel for which charactcristics nf the system must he included in the model iiiid which may ,he discarded. This i s usually hest gnined through firsl-hand cxpericnce.

Bernstcin provides an overall perspectiveof some ufthe chal- lenges wc a11 face in tailoring control theory to practicc. These thoughts areclassified as “general” only in the sense that they are iiutdrawnfroin aspcciOcapplicalionspcrspectivc.The tlicmeof his article i s that each olus in thecontrol held must pay attention to tlctails because, in most cascs, cven tlic sinilllest unaddressed detail can thwart innplementalion of new control ideas. This arti- cle i s suhdividcd into two m j o r sections: one that focuses on modeling issues and another that focuses on control issues.

Decemher 1999 47

The modeling scction emphasizes llic nced for lurlhcr rc- search in 1he modcling area. In certain cascs, it calls lor oil-line

, idcntification techniques for developing murc :scurale models, while in other cases it secks to reduce the clfort associaled with modcling. The main idea is to develop a modcl of appropriate complexily 10 gcl tlic job done.

Thc control ~ectioii discusses complic;itions that lypically arise in control algorithm iiiiplcmcntations-coinputing power requirements. transitions hotwccn continui~us- and discrete-time systems, impact of saturations, limitations imposed by sciisor noise, and distinclions bctwceii siiiooLli and nonsinooth noiilinearities. Although these issues inay iiot seem critical i n control theory dcvelopment, if not properly addressed they can easily discouragc or iiltogether prcclmle implementation of these new ideas.

Conclusions Thc control engineering discipline, likc almost any technicel

field, can he dividcd into sevel-al broad subclassilicatioiis: inno- vation, facilitation, and implcmentation. For tlic inosl parl, cach of these functions is performed by ii differenl set of cngineers. Effective internclion iiinong these lhrcc groups is an csscntial factor in lailoring control theory for pnicticc. Good co~niiiuiiica- tion between iiinovaturs hiid facilitators and betwccii fiicilitators and iinplementcrs is a prerequisitc for rapid, succcssful technol- ogy transfer.

The contributing authors to this special section rcpreseiil in- dustry, government, and academia, thus offering a widc range of perspectives. Thc oh,jective of lhis spccial seclion is to articulate

some OS thc rcasoiis Sor thc gap belwccn the lhcory arid practice ol'conlml and to suggest some ways to bridge this gap. Thc spe- cific appli~ations discussctl are aerospace oncs, iilthough inany ofthegcncralpointsmadcarerelevant tootherdumninsss wcll.

Acknowledgments I want to thank Tnriq Samad Cor his assislance in dcvcloping

Ilicco~iceptforll~is spccia1iasuc;ind inidentifyingaiidrecruiting some of the contribuling iiutliors. This prujcct w ~ i u l d not have been pussihlc without his help. Thanks also to Jim Cloulier for his a ~ s i ~ l a n c c in identilying potenliel contrihulors, Finally, 1 waul lo thank each olthc contribuling authors lor taking the tiinc to thoughtfully compose and share their llioughts and pcrspcc- livcs with our rcadcrship.

'l'homss I,. Ting mccivcd B.S. dcgrccs i n Elecriical Engiiacring iind Matlmaalics in 1982 aiitl M.S. iliiil l'11,Il. dcglocs in ElcctiiCal Enginecriog io 19x4 and 1987, rcspcctivcly, $111 from the Univcisity 0 1 Illi- aois-Urlxwi. Siiicc 1994 lie l i i i s bccn with Gcncral Mo- iors Rcswrcli iiiitl Devclopmcnt and Plcming i n Warrcn, Michigan, whc11. he i s currcnlly a staff re^

reaich aigincer. His CIIIKII~ work focuscs 01, &xcliq>-

iox and developing u i f i e d Inodel-h;iaerl iiiitoinolive ix~wel- tiiiiii coiiliiil and diagnaslic aigorithais. klc lias lpiior work experience in Ihe aernsp;sc indusll-y, desigrriiig ani1 tmilyiiog Right ~ o n t r o l syslcins lor Hoiicywcll Tcchiiirlogy Centcr (Ibnnerly Himcywcli Systems Sr Rcscarch Ccnler) fruin 1988 10 1494, imd in robotics, clcvcloping p;ith planning mtl iiiolioii coiilrol algorithms for Stinilia Naliuniil Ltihnwlories l i i m 19x7 IO

1988. DI-. 'I'hg is a mcinbcr of I

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