IEEE TRANSACTIONS ON EDUCATION, VOL. 33, NO. 3, AUGUST 1990

Incorporating Practical Contents 219

in Control Engineering Courses

Abstract-This paper describes two categories of practical curricula contents which are desired but often “missing” in the control system design courses taught in universities. The first encompasses known and essential expert’s knowledge which are seldom presented in textbooks. Examples are the handling of nonlinearity, multiloop design, consid- eration of load regulation, and the need for high sampling rate. The second encompasses emerging practical knowledge from recent re- search. Examples are the delta operator, auto-tuning, and knowledge- based control. Here, we advocate the viewpoint that academic course designers should adopt a balanced approach to ensure that a suflicient percentage of practical topics are incorporated into the curricula con- tents.

I. INTRODUCTION EAL-LIFE control engineering practice requires ex- R tensive knowledge in control system design, instru-

mentation, and process dynamics. The last topic, which may vary greatly from one process to another, can only be effectively mastered during practice; the introduction to this topic in textbooks, being more domain specific, will also depend greatly on the background of the authors, such as mechanical engineering, electrical engineering, and chemical engineering [1]-[8]. The second topic, al- though also domain specific but to a lesser degree, can be taught in greater detail at universities. However, only very basic instrumentation physics is normally taught owing to pressure from other more academic subjects. This is not really a major issue as most instrumentation knowledge can be readily learned in practice from experienced en- gineers or through vendor courses. Nevertheless, an ex- posure to basic instrumentation is important, for instance in component sizing [2] and means of controller imple- mentation [9], [ 101 and it would be helpful that these were addressed more frequently in basic control textbooks.

The first topic, control system design, being less do- main specific, is more universally taught in universities in a mathematically rigorous form with the main focus on systematic tools for analyzing and designing controllers. Many excellent textbooks [1]-[8] are available for this purpose. Again, under pressure to be modem, most uni- versities have introduced advanced control courses which are even more theoretical and mathematically biased. Many practical topics, which are critical to the successful operation of real-life control systems, are unfortunately

Manuscript received November 17, 1989; revised April 11, 1990. The authors are with the Department of Electrical Engineering, National

IEEE Log Number 9036948. University of Singapore, Kent Ridge 051 1, Singapore.

left out in most textbooks and hence seldom introduced to university students, worsening the well-known “gap” be- tween control theory and practice. There are strong evi- dences that some authors have begun to address this prob- lem, particularly in [2], [6], [9], [ l l ] , and more ought to be published to encourage the teaching of practical con- trol.

In this paper, we shall discuss the practical curricula contents which are desired but often “missing” in the typical control system design courses taught at the uni- versity level. They are classified into two categories. The first encompasses known and essential practical knowl- edge which are rarely treated in modem textbooks. Some of them could be traced in the literature [12]-[22] while others are kept as expert’s knowledge. They are mostly learned on the job and through possibly expensive mis- takes, or are reinvented especially at small, young orga- nizations which do not have the service of experienced designers. The second category encompasses new practi- cal knowledge which have emerged recently as a result of applied research by industrial and academic control sys- tem designers [23]-[33]. Finally, we will discuss how one may make room for such additional contents in the mod- em curricula which are already rather heavy.

11. EXPERT’S KNOWLEDGE It is well known that in recent years there has been a

worldwide shortage of control systems experts. This is not only because control systems is a difficult and multi- disciplinary subject; it is also caused by the lack of a sys- tematic way to shorten the period to gain expertise. It is of course not possible to treat all the practical knowledge in textbooks. Nevertheless, the incorporation of some key concepts and knowledge essential or useful in practice will go a long way to strengthen the practical base of the con- trol engineer; hopefully, this will ease and accelerate the acquisition of the additional knowledge and expertise.

A simple example of practical knowledge is the protec- tion against reset or integral windup. It was not treated in the textbooks in the 1970’s [l] , [4] and was only ad- dressed in some recent textbooks in the 1980’s [2], [l 13. It is still missing in many textbooks on modem control theory [3], [5], [8]. Another simple example is that the process static gain can be negative. For instance, in tem- perature control the gain of a heating process will be pos- itive while that of a cooling process will be negative. The sign of a process gain will also change depending on the

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280 IEEE TRANSACTIONS ON EDUCATION. VOL. 33. NO. 3, AUGUST 1990

choice of the control valve, being either air-to-open or air- to-close as required for operational safety. The practical solution is to have the so called “direct” or “reverse” controller action for negative and positive process gain, respectively [2]. Instrument sizing to ensure good reso- lution and to reduce signal saturation is another simple and yet important topic that is missing in textbooks, an exception being that of [2]. A very small static gain, for instance, may indicate undersizing of the actuator, thus limiting the dynamic performance of the control system. In this case, resizing of instrumentation should be consid- ered as a practical alternative to redesigning of the con- troller. In the following, we shall discuss some more practical topics of substantial significance.

A . Handling of Nonlinearity Many textbooks cover techniques of linearization and

analysis of nonlinear dynamic systems, but only very few [6], [12], [13] would address pragmatic methods of over- coming nonlinearity, such as ratio control and gain sched- uling which have been used in practice for many years. With the advent of microprocessors, these techniques are even easier to implement. For instance, gain scheduling has become a standard feature of two commercial auto- tuning PID controllers marketed by SattControl and Fisher Control [ 121. With auto-tuning, the optimal PID settings, which would change with the nonlinearity, can be auto- matically computed and stored for later recall as a func- tion of the measurable reference variable, such as flow rate, which changes with the nonlinearity. Such a simple solution should be used wherever possible, as the alter- native of nonlineadadaptive control is much more com- plicated and error-prone [ 131.

Another kind of nonlinearity occurs during the start-up/ shut-down of a process or in batch control in which the control actions are governed by logic, sequence, and other practical requirements such as maximum acceleratiodde- celeration. Linear regulators such as PID would be inap- propriate during such periods when automation should be handled by a sequence/logical control system. This is sel- dom treated in textbooks; welcome exceptions are the re- cent publications of [2], [141, [15].

B. Multiloop Design In a complex process control system there are many

variables to be regulated. Most of them are commonly controlled by single-loop controllers which are then inte- grated using multiloop strategies such as cascade, feed- forward, and decoupling control. This is well introduced in chemical engineering textbooks [6], [7] but not so com- monly found in others. There are also practical control systems which involve one manipulated variable and sev- eral controlled outputs, such as in override control and auctioneering control. There are yet other systems which involve one measurement (controlled output) and more than one manipulated variable, such as in split-range con- trol. Some chemical engineering textbooks such as [6] have started to present such multiloop control techniques.

Taking these one step further back in the design process, Bristol I161 has called these control “idioms” or control structures which are used to synthesize a large scale con- trol system in a bottom-up design approach. He has also discussed the nontrivial problems of pairing controlled variables (outputs) and manipulated variables (inputs), in- troducing techniques such as the relative-gain array anal- ysis. Again, only some chemical engineering textbooks [6], [7] have included this important topic, although the problems and solutions are quite universal.

C. Setpoint Versus Load Responses A distinction between setpoint (servo) response and load

(regulation) response is not very evident in the literature. In the servomechanism context as used in the electrical and mechanical engineering textbooks, load regulation need not be emphasized as the effect of load has either been greatly attenuated by an order of the square of the gear ratio, or diminished as very high controller gain may be used. Hence, setpoint response is usually the focus. The situation will be different in the case of direct drive motor systems in which there is no gear, or in chemical process control where the controller gain is limited by the presence of dead time or high order process dynamics. For instance, the Ziegler-Nichols tuning for PID control is found to be near optimal for load regulation and hence may not be suitable if setpoint response is to be optim- ized, and vice versa [ 171. The controller/regulator should then be tuned for load regulation while the setpoint re- sponse can be independently shaped using setpoint filter- ing or setpoint weighting [ 171, [ 181 which provides a two- degree-of-freedom control [26].

D. Sampling Rate in Digital Control The consideration of load regulation also has an impor-

tant bearing on the choice of sampling rate. For instance it is well known that pole-placement and dead-beat con- trol algorithms work well with a large sampling interval [ 113, which can be chosen as large as 1 /4 of the major process time constant. This is acceptable if only setpoint response is the major concern. For load regulation, es- pecially if the open-loop process is oscillatory or unsta- ble, a much smaller sampling interval, of the order of 1 /40 of the major time constant, may be needed to reduce the variance of a random load disturbance, or to reduce the amplitude of the output response in the case of static load disturbance [19]-[21], subject to the lower limit on sampling interval set by consideration of measurement noise. This has been known in practice but the explicit mention in the literature only appears recently.

Yet another omission in the teaching of the digital PID control algorithm is the problem of “integral offset” caused by quantization error. This is particularly serious if the integral time is long and the sampling interval is short as cautioned by Bristol [22]. The simple solution of having a much larger sampling interval for the integral term, while maintaining small sampling intervals for the

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proportional and derivative term, was not proposed in the literature until very recently [ 121.

111. NEW PRACTICAL KNOWLEDGE Most of the expert knowledge discussed in the preced-

ing section have been derived over many years by design- ers and practicing engineers. Their incorporation into practical control system designs has been rather ad hoc but it is possible to make them standard modules in com- puter aided engineering packages for control systems [23], [24]. Another class of new design methods developed in industry is dynamic matrix control (DMC), a constraint optimization technique [25] for supervisory (setpoint) control in a complex control system. It has been applied successfully in industry and only recently has appeared in a textbook [26]. A modified version called generalized predictive control (GPC) has been developed in the aca- demic world by Clarke to make it amendable for robust self-tuning control [27]. Both DMC and GPC are impor- tant practical design techniques that ought to be addressed in future textbooks in advanced control. Yet another emerging concept of supervisory control strategy is the on-line statistical process control technique [28] which aims to prevent off-spec production by manipulating set- points of control systems based on statistical and expert system models of the process.

In this section, we shall discuss other new practical knowledge which have emerged mostly as a result of re- cent academic research into practical control system de- sign. In view of their importance, they are discussed here to highlight to curricula developers and authors of future textbooks such emerging concepts and techniques.

A. Delta Operator Present day control systems are almost invariably im-

plemented using digital hardware. The controller design may be based on either a continuous-time or discrete-time approach, but the actual implementation would typically be a microprocessor-based mechanisation. The traditional discrete-time operator used in controller realizations has been the shift operator q. This is known to work rather well in situations when the sampling rate can be chosen to be moderate relative to the controller bandwidth. How- ever, it is not always possible to achieve acceptable per- formance using such sampling rates (for example, in re- jection of load disturbances [21]). In order to attain performance close to that achievable in continuous-time design, we may need sampling rates in excess of ten times the controller bandwidth. Under this condition, however, it is well known that the zeros and poles of the controller using a shift operator formulation, in a good many cases, cluster around the (1, 0) point in the z plane. This causes great problems of numerical precision.

There have been some studies into the above-mentioned problem recently. Most notably, in [29], the properties of the 6 operator, a discrete-time operator that may be used in place of the shift operator for realization of discrete- time controllers, were investigated and analyses were

given to show that the 6 operator had numerical properties superior to the shift operator when sampling rates were in excess of ten times the controller bandwidth. Some of the findings include the following:

a) The 6 operator has superior finite word length coef- ficient representation. This was analyzed for sensitivity in root locations when the parameters of a polynomial were perturbed. Worst-case first-order changes in root loca- tions were smaller for 6 operator realizations.

b) The 6 operator has superior finite word length rounding error performance. The rounding errors were modeled as roundoff noise, and the 6 operator realization was shown to have a smaller response to the roundoff noise in almost all aspects.

c) The 6 operator has superior numerical properties in manipulations involving pole-placement type of calcula- tions. The Sylvester matrix involved in the pole-place- ment equation had a smaller condition number for the 6 operator. This means that the matrix is better conditioned.

To a certain extent, these findings are not unexpected. The essence of the idea behind the 6 operator has, in fact, been in use by many control engineers who view this as an issue in numerics and hence for numbers which are close to one, it is better to represent them as deviations from one and to do computations accordingly. The main point is that a proper control engineering course should include a careful treatment of considerations in finite word- length coefficient representation of digital compensators. In relation to this, it is pertinent to point out that the choice of state realisations for the dynamical system that imple- ments the control law remains an important issue irre- spective of the discrete-time operator used for the me- chanisation. As far as possible, companion form realizations should be avoided, and well-conditioned re- alizations like the Jordan canonical form should be used instead [ 1 11.

While it is true that the explicit introduction of the 6 operator as a special operator is not absolutely necessary in the context of controller numerics, it is perhaps also fair to note that there are advantages of using such a for- malism as it enforces a good discipline and it enables a close correspondence to be drawn between discrete and continuous-time systems theory. The latter facilitates a greater use of continuous-time intuition in discrete-time design. For instance, the coefficients of a continuous-time transfer function H(s) = B(s) / A ( s ) and its sampled-data model in the 6 operator “ ( 6 ) = B ’ ( 6 ) / A r ( 6 ) are very sim- ilar when the sampling rate is large relative to the band- width of H(s) , and this may provide an additional advan- tage of insight in some situations [36].

B. Auto-Tuning

Academicians have found after many years of research in adaptive control that they should have introduced the less sophisticated automatic controller tuning (auto-tun-

282 IEEE TRANSACTIONS ON EDUCATION, VOL. 33, NO. 3, AUGUST 1990

ing) much earlier. Auto-tuning is less demanding in the- ory compared to adaptive control. It is also greatly appre- ciated in process control where a large number of controllers in a new process need to be tuned up during commissioning. Likewise, using this technique, the la- borious periodic retuning for optimization can be auto- mated. There are now many makes of commercial PID auto-tuners and they are well described in a recent mono- graph [ 121. The recent introduction of setpoint weighting [12], [18] has made it possible to achieve good setpoint response and load regulation simultaneously. New PID- tuning formulae to extend the range of tuning accuracy so that manual fine tuning can be eliminated are being de- veloped [18]. It is expected that auto-tuning of model based advanced controllers such as the Smith Predictor, pole-placement control, generalized minimum-variance control, etc. will soon be available commercially. As ex- plained before in Section 11-D, the fast sampling rate for load regulation is in conflict with the slow sampling rate required in robust parameter estimation. The emerging concept of dual-rate self-tuning or auto-tuning [ 191, [20] is aimed at resolving this conflict.

C. Knowledge-Based Control The advent of high performance microprocessors and

real-time expert system shells has stimulated the appli- cation of expert systems in process monitoring and fault diagnosis [30], [3 13. Their application to feedback con- trol is a brand-new topic of research, which has been termed knowledge-based control (KBC) or expert control. It is different from fuzzy control which aims to model the behavior of the plant operator in controlling a process and which is very domain specific and hence more difficult to generalize [30]. The approach taken in KBC is more gen- eral in that it can be used on many different processes. It has a knowledge base which contains heuristic rules; but unlike the usual expert system for process monitoring, it makes use of the emerging “deep” knowledge [32], [33] derived from theory and extensive experimentation in or- der to reduce the large number of possibly conflicting rules as used by different experts. The use of expert system technology also simplifies the updating of and addition to the knowledge base as new knowledge is learnt, and fa- cilitates the training of engineers as it can provide expla- nations in the decisions that it makes. Finally, the term “knowledge engineering” has given due respect to the otherwise neglected topics of sequencing control and pro- tection logic which are essential for practical control but not so highly regarded by academicians owing to lack of mathematical synthesis and analysis tools for these con- trol engineering methods.

A KBC should have a sufficiently comprehensive con- trol knowledge-base to be able to probe the process when necessary and monitor its behavior in order to extract the needed process knowledge automatically. An example of the first generation of KBC is reported by Arzen [30]. Among other features, it is capable of deciding from a relay experiment whether the process is PID-controllable,

and if not, will automatically initialize and commission a pole-placement controller. Further research into process characterization and control performance measure [33] will allow self-detection of off-optimum control perfor- mance which can then be used to trigger controller tuning and even changing of controller structure.

IV. CURRICULUM DEVELOPMENT What have been presented in the preceding sections are

not exhaustive; and much more practical knowledge from new experiences in applying multivariable, adaptive, and knowledge-based control will continue to appear in the literature [33]. How can we make room for more practical contents to be included in the academic program? Owing to the lack of suitable textbooks, most universities, in- cluding unfortunately many in the underdeveloped and developing countries, have adopted curricula which are more geared for the education of research engineers for large organizations in industrialized countries [ 161. This represents an extreme of the possible alternatives. The sit- uation may have arisen because the majority of the aca- demic staff have been trained in mathematical aspects of control and they continue to do research only in control theory due to pressure to publish. They may have little practical experience themselves and they are seldom seen in practice-oriented conferences. Hence, they may not even have sufficient confidence to develop a teaching lab- oratory which is essential for the effective learning of practical control [34], [35]. The good intention that “we should teach our students the most modem control theo- ries so that they can apply them in practice” is often not matched by the reality. Indeed we may risk producing control engineers who either despise practical work that do not have rigorous mathematical analysis, or what is just as bad, do not have any more faith in the new theories as they fail to appreciate their relevance after they have struggled to understand and solve engineering problems in practice by themselves. The other extreme is to teach the students all the practical solutions without much ex- posure to theory. The problem that would arise then is that the students will not be well prepared to understand the theory sufficiently to adapt the solution to a different situation. It is therefore essential that a balance between theory and applications be struck so that the students will have enough fundamentals to carry on life-long learning while being taught how typical control problems can be solved in practice.

In order to avoid extremes in curriculum development, one could first set a target on the desired percentage cov- erage of practical topics, say 20%. The scope and depth would then be determined by the total number of control subjects and course hours available. This will ensure that the opportunity for students to have the right proportion of practical education is not sacrificed by the pressure to “modernize. ” Unfortunately, this has not been the trend of most control textbooks which address more control the- ory than control engineering. The course lecturers would thus need to put in extra work to supplement these text-

HANG AND LEE: PRACTICAL CONTENTS IN CONTROL ENGINEERING COURSES 283

books with papers that are published in the more practical journals and conferences some of which are included in the reference list of this paper. Fortunately, there are also a small number of textbooks, particularly [2], which at- tempt successfully to address some of the practical topics which have been neglected in the textbooks written in the 1970’s and early 1980’s. They should serve as models for the new textbook authors who wish to address control en- gineering rather than pure control theory.

The advent of low cost control software packages for personal computers, such as the MATLAB, has made it possible nowadays to drastically reduce the time spent by students on manual computation and graph plotting. Thus, they help to create time for students to learn more about practical aspects of control through tutorial assignments. They also facilitate the demonstration and study of more realistic and practical problems through computer simu- lation. It is not the intention of this paper to discuss how one should go about setting up a laboratory to teach prac- tical control [34]; here, it suffices to simply mention that computer simulation is an economical means to provide laboratory exercises to enable the students to appreciate the practical problems of control and learn how to over- come them. It is also anticipated that the availability of flexible process simulators based on the use of low-cost single-chip controllers and computer-aided engineering packages will make it easier in future to set up the major part of a teaching laboratory [35]. Yet another option in creating room for practical curricula contents is to defer or reduce the marginally useful topics in traditional cur- ricula. An example of possible candidates is the describ- ing function analysis method, which has over the years become a specialized technique for only a small nuniber of researchers and servo designers.

Finally, a brief discussion on the special case of the under-developed and developing countries is in order here. In these countries, it would be rather rare to find indus- tries that require techniques from modem control theory such as aerospace industry and industries that design and manufacture products requiring high-performance servo- mechanisms. Industrial automation and process control to achieve improved product quality and yield to boost eco- nomic performance will probably occupy a great part or perhaps even the whole career span of a control engineer. He is supposed to be competent in applying vendor-sup- plied control system hardware and software to solve in- dustrial control problems. But the so-called “modern con- trol” is not anywhere in sight even among the modem commercial systems supplied by vendors from the most advanced countries. Where does the modem state-space design find its place in these manufacturing and process- ing industries and how much of it should be taught? Again, one should perhaps set a target, say 20%, on the desired percentage coverage of such “modern” and “future” theoretical topics which will eventually find in- dustrial applications in these countries. Classical design and theories, and more practical topics need to be given due emphasis since industries in these countries probably

also do not have sufficient number of experienced design- ers and engineers to guide the fresh university graduates.

V. CONCLUSION In this paper we have highlighted the need to incorpo-

rate practical contents in the curricula of control engi- neering courses in universities. Several important topics, encompassing those that are known but hitherto kept as expert’s knowledge and those which are emerging from recent research, are elaborated to serve as examples that could be addressed by future authors of textbooks. They include handling of nonlinearities, multiloop design, load regulation, need for high sampling rates, delta operator, auto-tuning, and knowledge-based control. It is also ar- gued that the curricula developers should adopt a bal- anced approach to ensure that a predetermined proportion of practical contents would be included in control engi- neering courses.

ACKNOWLEDGMENT The authors would like to thank Prof. K. J. Astrom,

Prof. G. C. Goodwin, and Dr. K. W. Lim for their en- couragement and constructive comments on the first draft of this paper.

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C. C. Hang (M’74-SM’90) graduated with a first class honours degree in electrical engineering from the University of Singapore, Kent Ridge, in 1970. He received the Ph.D. degree in control engineer- ing from the University of Warwick, England, in 1973.

From 1974 to 1977, he worked as a computer and systems technologist in the Shell Eastern Pe- troleum Company, Singapore, and the Shell Inter- national Petroleum Company, The Hague. Since 1977, he has been with the Department of Elec-

trical Engineering, National University of Singapore, where he is currently a professor and head of the control group. He was a visiting scientist in Yale University, New Haven, CT, and Lund Institute of Technology in 1983 and 1987, respectively. His current research interests include com- puter process control, adaptive control and expert systems applications. He has been an Associate Editor of Auromarica since 1984.

Tong-Heng Lee received the B.A. degree with first class honours in engineering tripos from Cambridge University, England, in 1980 and the M.Sc. and Ph.D. degrees from Yale University, New Haven, CT, in 1984 and 1987, respectively.

From 1982 to 1983, he worked as a research engineer at the National University of Singapore investigating methods of on-line parameter esti- mation. His Ph.D. work at Yale was in the area of adaptive systems theory. Currently, he is a lec- turer at the Department of Electrical Engineering

of the National University of Singapore. He teaches both undergraduate and graduate courses in control systems engineering. His research interests include applications of expert systems for control and issues in the design of controllers for fast servo systems.

Dr. Lee was awarded the Baker Prize by Cambridge University Engi- neering Department in 1980.