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Knowledge Based Control: Selecting the Right Tool for the Job
Copyright © IFAC Artificial Intelligence in Real-Time Control, Detft, The Netherlands, 1992
PLENARY PAPERS
KNOWLEDGE BASED CONTROL: SELECTING THE RIGHT TOOL FOR THE JOB R. Leitch Intelligent Automation Laboratory, Department of Electrical and Electronic Engineering, Heriot -Wall University , Edinburgh EHl 2HT, UK
Absract. We propose a classification of system models in terms of their knowledge classes and characteristics, and relate these to existing approaches to the use of AI methods in Control. Such an classification is a necessary precursor to developing a methodological approach to identifying the most appropriate technique (tool) for a given generic class of applications (job). Keywords . Systems Modelling, Qualitative Modelling, Expert Control, Model Based Control. Specification Methodology.
INTRODUCTION Approaches to the utilisation of Artificial Intelligence (AI) methods for extending the range of automation continues to expand at an ever increasing pace. Each technique results in a number of new potential solutions. The result is that the practising Control Engineer is bewildered by the seemingly endless procession of techniques each offering some prospects of solving a given automation problem. But how does he choose what's best? Does he go for the latest Advanced Control method based on evermore sophisticated mathematics or is he seduced by the promise of Intelligent Systems using simple qualitative methods to produce flexible and effective systems. Or does he need both! At present the Control community is not addressing this crucial problem of determining the most appropriate approach dependent upon the nature of the automation task and the characteristics of the system that is to be automated. Such a methodological approach is essential if effective use of both AI-based systems and 'conventional' control methods is to be established. A corollary to this is that we need to stop looking for a universally best approach, and put much more effort into understanding the assumptions and therefore the limitations of the various techniques. Only in this way can we select the right tool for the job.
APPROPRIATE MODELLING: - that's the secret These days the word 'model' is a heavily overworked term. In its most general form it can be used to mean any description of an entity. However what is crucial is to clearly understand the role of the model. Within engineering, models have long been used to predict the temporal evolution of the attributes of a physical system, often now called the behaviour of the system. However, recently, mainly stemming from the AI community, modelling techniques for reasoning about the topological properties or spatial position of objects and methods for representing and reasoning about the function of systems have also been developed. Although these latter developments are interesting they have not yet impacted on Control Engineering. We will, therefore, restrict the subsequent discussion to models for the purpose of predicting behaviour, sometimes called behavioural models and descriptions. Further, we must also consider the purpose (or task) for which the model is being developed. For example, it has long been recognised that models for open-loop and feedback control require different amounts of detail to achieve a similar performance. Now, with Control Engineering expanding its horizons to include other tasks, e.g. fault diagnosis, process monitoring, planning, training, etc., we must carefully consider the
relationship between task and model requirements. There will be no one model that is best suited to all tasks. This 'no best model' is fundamental to Engineering, whereas in Science, where the task of modelling is almost exclusively analytic - to describe the physical world as accurately as possible - the notion of best model may be valid. In Engineering, concerned with synthesis as well as analysis: a model is correct
given purpose, task, specification characteristics of the available knowledge.
and
APPROACHES TO MODELLING
The preceeding section argued that the approaches to developing models has expanded rapidly over the last few years. Unfortunately, most of these techniques have been developed in isolation, and partial ignorance, of other approaches and so very little understanding or taxonomic knowledge of the various approaches exists. This section makes an attempt to classify the existing assumptions behind the various approaches so that we can begin to understand the relationship between them. We first classify models into model classes and model types and then identify a number of dimensions for each. The former is used to classify the important assumptions that relate to the purpose of the model, whereas, the latter relates to the characteristics of the available knowledge.
if its satisfies its purpose.
Also, synthesis is usually expressed as a set of performance specifications for the system. So, even a best or optimal model can be difficult, and sometimes impossible to obtain. We are normally faced with a trade-off between some of the specifications. For example, accuracy of predictions and generality of the model can sometimes be conflicting requirements. Further, AI based approaches emphasise the need for 'understandability' or perspicuity of models as an important specification requirement. In fact, many of the existing AI approaches and those under development, explicitly address this issue of enhancing 'perspicuity', sometimes at the notional expense of accuracy, so that the system can be more easily modified or extended. Therefore, in developing a model we have to consider the role (behavioural prediction), the task (control, diagnosis, training, etc.) and the performance specifications (accuracy, flexibility, generality, verifiability, perspicuity - and honesty).
Model Classes This class of models reflects very fundamental assumptions about the model that are closely related to the purpose of the model. We identify three class dimensions: knowledge source, knowledge level and knowledge orientation. In fact, combinations of these dimensions lead to completely different approaches and research topics. By knowledge source we mean where the knowledge that is used to build the model comes from . Two major sources of such knowledge have been identified (Leitch,1989) as empirical and theoretical. Empirical knowledge relates to that which is obtained directly from first hand experience. It attempts to capture knowledge that has been induced from direct observation of a As such it can be highly particular system. effective but is limited in its generality. Empirical knowledge has traditionally been omitted from control systems design, sometimes resulting in reduced performance, and hence requiring subsequent empirical tuning. However, the development of Expert Systems techniques has brought such knowledge to the fore and emphasised its importance and, more recently, its limitations. On the other hand, theoretical knowledge, that is knowledge of scientific laws and principles, has long been the basis of control system design. However, the use of such knowledge has, until fairly recently, been almost exclusively restricted to numerical descriptions usually in the form of differential or difference
Honesty! What has honesty got to do with modelling? Well, what has been under development within AI based approaches are techniques that allow the modeller to represent the available knowledge in a model at the degree of precision and certainty that is confidently known no less and no more. That is, if the knowledge is uncertain, and perhaps even incomplete, we should provide representation and reasoning mechanisms to explicitly support such knowledge, and not require the modeller to make 'guesses' or estimates that he may not believe in for the model to become tractable. This last insight is particularly important and has resulted in an enormous interest in using AI techniques to develop alternative(qualitative or non-numeric) modelling approaches to cope with such issues (Weld,1989; Davis,1990; Leitch,1990) a whole plethora of techniques based on a wide range of assumptions and normally developed for specific tasks. It is important that we now try to understand the relationships between such models and most crucially identify a methodology for selecting the most appropriate technique for a
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equations. And, as discussed in the motivation, often there does not exist adequate knowledge to make use of the powerful methods associated with real-valued differential equations. The Artificial Intelligence community has, however, developed techniques that allow theoretical knowledge to be represented qualitatively and used to generate qualitative descriptions of the system's behaviour (Weld,1989; Leitch 1990). Theoretical knowledge is, of course, general and is transferable from one application to another and, in fact, removes much of the knowledge acquisition problem associated with empirical knowledge. However, it can also be inefficient, and its very generality may mean that it is less effective. Clearly, theoretical and empirical knowledge are complementary; the best solution is obtained by a symbiotic combination of the two. However, such combinations are by necessity specific to a given application (Leitch,1989) and care has to be taken to ensure that performance is indeed improved.
domains, e.g. diagnosis. We term this class dimension the knowledge-level. A further distinction has to be made, and that is whether the knowledge represents an explicit model of the physical world to be reasoned about or whether it represents our procedures for controlling or diagnosing the world. In the latter case the model would be termed implicit. Explicit models relate system inputs to outputs in the same way as the real system. They can, therefore, have a causal interpretation (Iwasaki,1986) associated with the structure of the representation. Conversely, implicit models effectively relate outputs (symptoms in the case of diagnosis) to inputs and are inherently acausal.
The second class dimension determines the subject of the knowledge. In Control Engineering terms we can have two options. We can represent the knowledge of the process itself, i.e. model-based approaches, or of the control algorithm, we term this object-level knowledge. Alternatively, we may choose to represent knowledge about the control design methods so that they can be modified on-line. We term this meta-level knowledge, as it reflects knowledge about the knowledge used to control rather than the modelling knowledge itself. Both approaches are actively being developed, both with AI-based techniques and 'traditional' control methods. For example, expert or intelligent control (Astrom,1986) can be described as a meta-Ievel approach, usually with empirical knowledge at the meta-Ievel and a conventional numerical controller(s) at the object level. In contrast, Fuzzy Logic Controllers (Mamdani,1976) can be regarded as object-level empirical knowledge (with uncertainty). Similarly, in the case of conventional control techniques, examples of object-level control are classical three term controllers or indeed an LQG derived state feedback control system. Adaptive control systems, are a common form of meta-Ievel control as the performance of the system is monitored on-line, usually in the form of some performance index, and used to modify the (design) of the object level controller. This clear separation of meta and object level knowledge allows different tcchniques (models) to be used at each level, thereby greatly expanding the range of applicability of the control techniques. This distinction is fundamental in control applications, however, it is also valid within other
object
Figure 1. Model Classes In the former case, explicit models are currently being utilised as the basis for model-based reasoning, in particular for diagnosis, but control and training are equally important tasks. Explicit models are usually from a theoretical source, however, they need not be. In fact, much of causal modelling (Console,1989)takes its knowledge from empirical sources. Implicit models can also be obtained from both sources of knowledge. Conventional control algorithms are derived from theoretical models using some design procedure, whereas Fuzzy Logic Controllers utilise implicit models based on empirical knowledge at the object level. From Figure 1, we can see that various approaches to control can be identified by appropriate combinations of the above dimensions. We believe that these class-dimensions provide an important insight into the relationships between
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many of the fundamental techniques currently under development. Model Characteristics
information about causality; that must be obtained from another source (Leitch,1987) This lack of directionality makes the representation very general but can also make the reasoning or inference mechanism inefficient. Conversely, if the available knowledge contains a strong element of directionality between the variables then a procedural language will be more effective. However, the directionality may be very specific to a given application or situation, and hence procedural representations tend to be highly specialised. The trend has been to make representations more and more declarative to increase generality and to cope with the resultant loss in efficiency by more computational power.
Whilst model class is determined by the purpose and task of the model, the properties of the available knowledge used to model the process determine the characteristics of the model that can be utilised. These characteristics are used to form dimensions along which models can be classified and hence used to identify the most appropriate model. It is in this area that AI is having the most significant impact. In fact, the issues here are exactly those that underpin knowledge representation issues within AI, and are, therefore, intrinsically fundamental to AI itself. Figure 2 illustrates the characteristic dimensions. We identify five dimensions that represent the principal assumptions for modelling and reasoning about the physical world; in other domains other characteristics may be more important.
The third characteristic dimension concerns whether the system is considered to be continuous or discontinuous. In the continuous case, the system can only evolve through adjacent states whereas in the discontinuous case any state can follow a previous one, e.g. finite state machine. This leads to different techniques for generating the behaviour of the system. Continuity is clearly an important assumption in dynamic systems. However discontinuous dynamic systems can also be important.
A fundamental choice is whether to represent the dynamic evolution of the system or not. Until fairly recently most AI-based representation schemes were based on static models of the system assumed to be in equilibrium. Such models can, indeed, be useful especially in steady-state fault diagnosis. However, in (model-based) control and in diagnosing faults during the transient behaviour of a system, dynamic models are essential. Dynamic models require the representation of state and memory to reflect the energy storage, and hence delay, that occurs in the physical world. This is often confused, at least in AI circles, with temporal reasoning that reasons about the ordering of events in time. Static models can still have time-dependent variables, and even time-varying parameters, without being dynamic. Hence many temporal reasoning applications are based on static models. The choice of static or dynamic models fundamentally effects the representation language. In the former, algebraic equations will suffice whereas in the latter differential primitives are required.
An area of intense activity, now concerning both Control Engineering and AI researchers is Qualitative Modelling (Weld,1989, Leitch 1990). Although this will not be specifically discussed in this paper, it forms one of the main characteristic dimensions of models. This dimension concerns a spectrum of representations from purely quantitative models at one end and very weak qualitative representation at the other. In fact, exploring novel representation techniques, e.g. order-of-magnitudes and fuzzy sets has been a major pre-occupation of qualitative reasoning research (Shen,1992a). The goal is to utilise a representation that 'honestly' captures the available knowledge whilst satisfying the performance specifications. Finally, the fifth characteristic dimension is whether the knowledge of the model is uncertain or exact. Not to be confused with qualitative; a model can be qualitative and exact, and even honest! However, if the knowledge is uncertain then the representation should include some way of representing this. Two main forms of uncertainty have been recognised. The first, probability theory, concerns the situation when exact (deterministic) knowledge is not available and estimates based on the frequency of occurrence, represented by a probability density function, are used. It is essentially historically or
One of the early insights to stem from AI work is the distinction between declarative and procedural representations. Declarative representations describe relationships between variables or attributes of the physical world. They do not imply a directionality in the relationship, only that a set of variables are related by the description provided. For example, Ohm's law states that the current through and voltage across a resistor can be related by an empirical constant (given real-valued descriptions of the current and the Voltage, see later) called the resistance. It does not contain any
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experimentally based. The second approach is to represent imprecision explicitly, i.e. vagueness is captured by a graded membership, a real number between (0-1), representing the degree of set membership of a particular item (Zadeh,1973). Whether probability or possibility is used does not concern this dimension, only that uncertainty is a characteristic of models that may be important, and, therefore, should be explicitly supported.
EXTENDING THE SCOPE OF CONTROL ENGINEERING Although modelling, in all its various guises, is intrinsically important, the real advantage comes when using these techniques for control applications. The last decade has seen a rapid expansion in the tasks or purposes for which Control Engineering knowledge or methodology has been applied. In this respect we take Control to mean interacting and reasoning about the real physical world for some specified purpose. The original tasks of Control: regulatory and servomechanism control by using feedback or feed forward techniques have been supplemented by a range of tasks including :- fault diagnosis, condition monitoring, critical event simulation, training etc. Each of these tasks uses some of the various approaches to modelling discussed in the previous section. However, so far there has not been a significant attempt to identify the 'best' approach to modelling for a given application. Such a methodological approach is now becoming crucial as both the classes and characteristics of models and the range of applications continually expand. What is required is a set of relations that will identify the most appropriate model, and the corresponding solution technique, for a given class of application, the class being determined by the characteristics of the domain. In this regard the model classifications presented in the preceding sections begin to form a basis from which such a methodological approach can be generated. In this section, we begin the process of attempting to identify particular solution techniques with generic problems appearing within the Control Engineering literature.
Figure 2 shows the five principle characteristic dimensions. In fact, each combination of choices on these dimensions represent a different 'type' of model. Some of these combinations represent very strong research areas, e.g. Qualitative, Dynamic, Declarative, Uncertain, Continuous, Models (Fuzzy Qualitative Simulation, (Shen,1991b) whereas others represent well established techniques, e.g. Quantitative, Dynamic, Procedural, Exact, Continuous, Models (differential equations). Still others, e.g. Quantitative, Static & Dynamic, Declarative, Continuous Systems have yet to be investigated.
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I Static
Dynamic
•
Continuous
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Qualitative
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Declarative
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Certain
•
Discontinuous
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Quantitative
•
Procedural
.
Control Systems
Uncertain
One of the obvious approaches to the utilisation of Artificial Intelligence techniques within Control attempts to use Expert System techniques (Astrom,1986) as an adjunct to conventional methods. By placing an Expert System, usually using rule-based technology 'on top of an existing numerically based control system, the range of applicability of the controller can be extended by encoding into the Expert System rules for the adjustment of the control, either by modifying the control algorithm or by replacing it with another approach altogether. This essentially puts the Control Engineer on-line so that knowledge normally only used during the (off-line) design process is available during the actual operation. This approach is now being called Expert Control,
Figure 2. Model Characteristics In this section we have proposed a classification of models into classes, reflecting their purpose, and characteristics, dependent upon the properties of the available knowledge. Our intention is that such work lays the foundation for a methodological approach that will provide, at least, a set of guidelines to identify the most appropriate technique and associated model for a particular task and application characteristics.
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and represents a major activity for Control Engineers wishing to become involved in Artificial Intelligence approaches. It is attractive in that it utilises existing techniques, and hence skills; we believe that the majority of Control Engineers have adopted this route. In terms of the model classification proposed in Section 2, this approach adopts an implicit empirical model at the metalevel (Expertise) and existing control methods which can be either implicit (feedback) or explicit (model reference) using either empirical or theoretical knowledge at the object-level.
empirical models at the object-level, with Fuzzy Sets to represent the inherent vagueness or uncertainty. This approach is appropriate whenever there is some inherent difficulty with conventional modelling (numerically-based) techniques. Further, in many cases it has been shown that equivalent control performance can be achieved, however, qualitative methods have a distinct advantage of perspicuity (Francis,1989). t Meta-Ievel control can also be used with qualitative controllers. A good example of this is again Self-Organising Fuzzy Logic Controllers (Linkens,1991) where self-organising rules are used to modify the fuzzy rule-base to improve the overall performance.
The second approach uses AI techniques directly to model the system at a level of detail consistent with the available modelling knowledge and the task to be executed. Such approaches, sometimes called Qualitative Control, can be regarded as directly 'closing the feedback loop' by using AI methods. In this way qualitative representation of the control policy is used to compute the value of the control variable. This exposes a major shortcoming of qualitative methods for control applications: practical controllers still must output a numerical value. This requires that the qualitative value be 'approximated' by a numerical value; a symbol-to-signal transformation that is highly subjective. Fuzzy Logic Controllers are prime examples of this approach. They use implicit
Awaiting development is the qualitative counterpart of Model Reference Control. In this case the techniques of Qualitative Simulation can be used to represent the 'reference model', i.e. the ideal model, and the control adjusted such that the observed behaviour approaches the predicted response. The comparison between behaviours will also require a form of symbol to signal transformation in order to identify real-valued adjustments to the controller. We are not aware of this work being reported or even pursued. In terms of the classification this approach would utilise explicit, theoretical models at the object- level
Process
numerical controller
Expert System
Qualitative Control Expert System
Expert Control
Figure 3 Generic approaches to Control
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I----W~
discrepancy detector
qualitative model
Qualitative Model-Based Control Figure 3
Autpmated Fault Djalmpsjs
The most frequent task performed by AI techniques is fault diagnosis. An enormous literature has amassed on such techniques. Many parallels can be drawn with the previous discussion on control methods. For example, initial diagnostic systems utilised Expert System techniques to represent empirical knowledge of relationships linking symptoms (observations) with possible diagnoses (actions). This approach is now termed Classification-Based diagnosis and has many examples of successful implementations. However, the approach inherits the limitations of Expert Systems (empirical knowledge) in that only known or experienced faults can be diagnosed. It conforms to the use of empirical, implicit models at the object-level. Another more general approach to diagnosis has recently been developing, based on the use of explicit models of the system to be diagnosed. Such methods are called Model-Based Diagnostic Systems (MBDS). The central idea of MBDS is the use of an explicit model of a system's structure and a simulation engine to generate the behaviour. Such a diagnostic mechanism determines some constituents of a physical system that account for the observed discrepancies, i.e. inconsistencies obtained from the model and driven by the observations.
requirement of accessibility of system states by diagnosing over time. Within systems based on iterative search (Leitch,1991) diagnosis is performed as a refining process in the following way. Initiallised by observations, an inference engine predicts a system's behaviour based on the system's model of normal behaviour, and a discrepancy detector detects the discrepancies between the predictions and the observations. The resulting discrepancies direct an iterative search of the space of possible model variations, using the most likely dimension first, until a 'matching' fault model is obtained. This requires that the possible model dimensions be identified and characterised, for instance, a dimension with the modification of the functional relationships between system variables or that with structural changes due to physical effects. From a general point of view, this method can be seen as a generalisation of the conventional numerical approaches to MBDS. Actually, although it is a new approach to applying AI modelling techniques for diagnosis, it utilises the basis concepts of the traditional approaches to model reference reasoning in control and system An important identification (Landau,1979). property of this technique is that the diagnostic process will be robust in that the recursive nature of the iterative search will provide a reduction in the sensitivity of the modelling assumptions.
The use of qualitative simulation techniques as the system model offers many exciting prospects for MBDS in that such developed diagnostic systems would allow the early detection of incipient failures (during the transient) and reduce the
These examples give some indication in the possible diversification of applications now being pursued and which can quite reasonably be considered to be a natural part of the Control Engineers remit. Similar arguments can be
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constructed for simulation application domains.
and
training
as
Expert System Classification Based Diagnosis
Process
discrepancy detector
candidate generator Qualitative Model Based Diagnosis
Figure 4. Generic Approaches to Diagnosis CONCLUSION We have attempted to show that underlying a great part of what is currently being developed, both within the AI community and the Control Engineering community, is fundamentally concerned with generating a range of approaches to the modelling of physical systems. It is here that AI is having a very profound impact on Control approaches, if not yet on theory. We have presented a classification of approaches
to modelling based on model classes and characteristics. We believe that such a classification is a necessary precursor to establishing a coherent methodological approach to identifying the most appropriate tool for a given application. By extending the formal methods of reasoning to include the prediction of the qualitative evolution of a dynamic system we now have a much greater diversity of tools with which to more effectively tackle complex applications.
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REFERENCES Leitch R.R (1990)"A Review of the Approaches to Qualitative Reasoning of Complex Physical Systems", in Knowledge Based Systems for Process Control. McGhee 1. Grimble M.l . and Mowforth P.(editors), Peter Peregrinus.
Astrom KJ., J.J. Anton and K.E. Arzen. (1986) "Expert Control", Automatica .Vol.22, No.3, pp277-286. Console L. D. Dupre and P. Torasso. (1989) "A Theory of Diagnosis for Incomplete Causal Models", Proc. 11th Int. Joint Con! on Artificial Intelligenc.
Leitch RR. and Shen Q. (1991) "Finding Faults with Model Based Diagnosis", Proc. 2nd. Int. Workshop on the Principles of Diagnosis. Milan.
Davis E. (1990) Representations in Common Sense Knowledge . Morgan Kaufmann, San Mateo, CA.
Linkens D.A. and S.B. Hasnain. (1991) "Self Organising Fuzzy Logic Control and its Application to Muscle Relaxant Anaethesia". Proc. IEEE. Part D. Vol.138. No.3. pp57-95.
Francis C.F. and R.R Leitch. (1989) "A Feedback Control Scheme Based on Causal Modelling" Jnt . Journal of Engineering Applications of Artificial Intelligence. Vol.2 No.3, ppI82-188.
Mamdani E.H. (1976) "Advances in the Linguistic Sythesis of Fuzzy Logic Controllers", Int. Journal of Man Machine Studies. Vol.8, pp669-678.
Iwasaki Y. and H.A. Simon. (1986) "Causality in Device Behaviour", Artificial Intelligence. Vol.29, pp3-23.
Shen Q. and R.R. Leitch. (1991a) "On Extending the Quantity Space in Qualitative Reasoning", To appear in The International Journal for Artificial Intelligence in Engineering.
Kuipers. B.l. (1986) "Qualitative Simulation", Artificial Intelligence. Vol.29, pp289-338.
Shen Q. and RR. Leitch. (1991b) "Combining Qualitative Simulation and Fuzzy Sets", Recent Advances in Qualitative Physics. B. Faltings and P. Struss (editors), MIT Press.
Landau Y. (1979) Adaptive Control: The Model Reference Approach. Marcel Dekker. Leitch RR (1987) "The Modelling of Complex Dynamic Systems", Proc. lEE. Part D. Vo1.134, No.3, pp245-250.
Weld, D. and J. de Kleer, (1990). Readings in Qualitative Reasoning about Physical Systems. Morgan Kaufmann, San Mateo, CA, 1989.
"Task Leitch RR and A. Stefanini. (1989) Dependent tools for Intelligent Automation", Int. Journal on Artificial Intelligence in Engineering. Vo1.14, No.3, ppI26-143 ..
Zadeh L.A. (1973) "Outline of a New Approach to the Analysis of Complex Systems and Decision Processes", IEEE Trans. Systems, Man, and Cybernetics, Vol.3, No.l.,pp28-44.
9
PLENARY PAPERS
KNOWLEDGE BASED CONTROL: SELECTING THE RIGHT TOOL FOR THE JOB R. Leitch Intelligent Automation Laboratory, Department of Electrical and Electronic Engineering, Heriot -Wall University , Edinburgh EHl 2HT, UK
Absract. We propose a classification of system models in terms of their knowledge classes and characteristics, and relate these to existing approaches to the use of AI methods in Control. Such an classification is a necessary precursor to developing a methodological approach to identifying the most appropriate technique (tool) for a given generic class of applications (job). Keywords . Systems Modelling, Qualitative Modelling, Expert Control, Model Based Control. Specification Methodology.
INTRODUCTION Approaches to the utilisation of Artificial Intelligence (AI) methods for extending the range of automation continues to expand at an ever increasing pace. Each technique results in a number of new potential solutions. The result is that the practising Control Engineer is bewildered by the seemingly endless procession of techniques each offering some prospects of solving a given automation problem. But how does he choose what's best? Does he go for the latest Advanced Control method based on evermore sophisticated mathematics or is he seduced by the promise of Intelligent Systems using simple qualitative methods to produce flexible and effective systems. Or does he need both! At present the Control community is not addressing this crucial problem of determining the most appropriate approach dependent upon the nature of the automation task and the characteristics of the system that is to be automated. Such a methodological approach is essential if effective use of both AI-based systems and 'conventional' control methods is to be established. A corollary to this is that we need to stop looking for a universally best approach, and put much more effort into understanding the assumptions and therefore the limitations of the various techniques. Only in this way can we select the right tool for the job.
APPROPRIATE MODELLING: - that's the secret These days the word 'model' is a heavily overworked term. In its most general form it can be used to mean any description of an entity. However what is crucial is to clearly understand the role of the model. Within engineering, models have long been used to predict the temporal evolution of the attributes of a physical system, often now called the behaviour of the system. However, recently, mainly stemming from the AI community, modelling techniques for reasoning about the topological properties or spatial position of objects and methods for representing and reasoning about the function of systems have also been developed. Although these latter developments are interesting they have not yet impacted on Control Engineering. We will, therefore, restrict the subsequent discussion to models for the purpose of predicting behaviour, sometimes called behavioural models and descriptions. Further, we must also consider the purpose (or task) for which the model is being developed. For example, it has long been recognised that models for open-loop and feedback control require different amounts of detail to achieve a similar performance. Now, with Control Engineering expanding its horizons to include other tasks, e.g. fault diagnosis, process monitoring, planning, training, etc., we must carefully consider the
relationship between task and model requirements. There will be no one model that is best suited to all tasks. This 'no best model' is fundamental to Engineering, whereas in Science, where the task of modelling is almost exclusively analytic - to describe the physical world as accurately as possible - the notion of best model may be valid. In Engineering, concerned with synthesis as well as analysis: a model is correct
given purpose, task, specification characteristics of the available knowledge.
and
APPROACHES TO MODELLING
The preceeding section argued that the approaches to developing models has expanded rapidly over the last few years. Unfortunately, most of these techniques have been developed in isolation, and partial ignorance, of other approaches and so very little understanding or taxonomic knowledge of the various approaches exists. This section makes an attempt to classify the existing assumptions behind the various approaches so that we can begin to understand the relationship between them. We first classify models into model classes and model types and then identify a number of dimensions for each. The former is used to classify the important assumptions that relate to the purpose of the model, whereas, the latter relates to the characteristics of the available knowledge.
if its satisfies its purpose.
Also, synthesis is usually expressed as a set of performance specifications for the system. So, even a best or optimal model can be difficult, and sometimes impossible to obtain. We are normally faced with a trade-off between some of the specifications. For example, accuracy of predictions and generality of the model can sometimes be conflicting requirements. Further, AI based approaches emphasise the need for 'understandability' or perspicuity of models as an important specification requirement. In fact, many of the existing AI approaches and those under development, explicitly address this issue of enhancing 'perspicuity', sometimes at the notional expense of accuracy, so that the system can be more easily modified or extended. Therefore, in developing a model we have to consider the role (behavioural prediction), the task (control, diagnosis, training, etc.) and the performance specifications (accuracy, flexibility, generality, verifiability, perspicuity - and honesty).
Model Classes This class of models reflects very fundamental assumptions about the model that are closely related to the purpose of the model. We identify three class dimensions: knowledge source, knowledge level and knowledge orientation. In fact, combinations of these dimensions lead to completely different approaches and research topics. By knowledge source we mean where the knowledge that is used to build the model comes from . Two major sources of such knowledge have been identified (Leitch,1989) as empirical and theoretical. Empirical knowledge relates to that which is obtained directly from first hand experience. It attempts to capture knowledge that has been induced from direct observation of a As such it can be highly particular system. effective but is limited in its generality. Empirical knowledge has traditionally been omitted from control systems design, sometimes resulting in reduced performance, and hence requiring subsequent empirical tuning. However, the development of Expert Systems techniques has brought such knowledge to the fore and emphasised its importance and, more recently, its limitations. On the other hand, theoretical knowledge, that is knowledge of scientific laws and principles, has long been the basis of control system design. However, the use of such knowledge has, until fairly recently, been almost exclusively restricted to numerical descriptions usually in the form of differential or difference
Honesty! What has honesty got to do with modelling? Well, what has been under development within AI based approaches are techniques that allow the modeller to represent the available knowledge in a model at the degree of precision and certainty that is confidently known no less and no more. That is, if the knowledge is uncertain, and perhaps even incomplete, we should provide representation and reasoning mechanisms to explicitly support such knowledge, and not require the modeller to make 'guesses' or estimates that he may not believe in for the model to become tractable. This last insight is particularly important and has resulted in an enormous interest in using AI techniques to develop alternative(qualitative or non-numeric) modelling approaches to cope with such issues (Weld,1989; Davis,1990; Leitch,1990) a whole plethora of techniques based on a wide range of assumptions and normally developed for specific tasks. It is important that we now try to understand the relationships between such models and most crucially identify a methodology for selecting the most appropriate technique for a
2
equations. And, as discussed in the motivation, often there does not exist adequate knowledge to make use of the powerful methods associated with real-valued differential equations. The Artificial Intelligence community has, however, developed techniques that allow theoretical knowledge to be represented qualitatively and used to generate qualitative descriptions of the system's behaviour (Weld,1989; Leitch 1990). Theoretical knowledge is, of course, general and is transferable from one application to another and, in fact, removes much of the knowledge acquisition problem associated with empirical knowledge. However, it can also be inefficient, and its very generality may mean that it is less effective. Clearly, theoretical and empirical knowledge are complementary; the best solution is obtained by a symbiotic combination of the two. However, such combinations are by necessity specific to a given application (Leitch,1989) and care has to be taken to ensure that performance is indeed improved.
domains, e.g. diagnosis. We term this class dimension the knowledge-level. A further distinction has to be made, and that is whether the knowledge represents an explicit model of the physical world to be reasoned about or whether it represents our procedures for controlling or diagnosing the world. In the latter case the model would be termed implicit. Explicit models relate system inputs to outputs in the same way as the real system. They can, therefore, have a causal interpretation (Iwasaki,1986) associated with the structure of the representation. Conversely, implicit models effectively relate outputs (symptoms in the case of diagnosis) to inputs and are inherently acausal.
The second class dimension determines the subject of the knowledge. In Control Engineering terms we can have two options. We can represent the knowledge of the process itself, i.e. model-based approaches, or of the control algorithm, we term this object-level knowledge. Alternatively, we may choose to represent knowledge about the control design methods so that they can be modified on-line. We term this meta-level knowledge, as it reflects knowledge about the knowledge used to control rather than the modelling knowledge itself. Both approaches are actively being developed, both with AI-based techniques and 'traditional' control methods. For example, expert or intelligent control (Astrom,1986) can be described as a meta-Ievel approach, usually with empirical knowledge at the meta-Ievel and a conventional numerical controller(s) at the object level. In contrast, Fuzzy Logic Controllers (Mamdani,1976) can be regarded as object-level empirical knowledge (with uncertainty). Similarly, in the case of conventional control techniques, examples of object-level control are classical three term controllers or indeed an LQG derived state feedback control system. Adaptive control systems, are a common form of meta-Ievel control as the performance of the system is monitored on-line, usually in the form of some performance index, and used to modify the (design) of the object level controller. This clear separation of meta and object level knowledge allows different tcchniques (models) to be used at each level, thereby greatly expanding the range of applicability of the control techniques. This distinction is fundamental in control applications, however, it is also valid within other
object
Figure 1. Model Classes In the former case, explicit models are currently being utilised as the basis for model-based reasoning, in particular for diagnosis, but control and training are equally important tasks. Explicit models are usually from a theoretical source, however, they need not be. In fact, much of causal modelling (Console,1989)takes its knowledge from empirical sources. Implicit models can also be obtained from both sources of knowledge. Conventional control algorithms are derived from theoretical models using some design procedure, whereas Fuzzy Logic Controllers utilise implicit models based on empirical knowledge at the object level. From Figure 1, we can see that various approaches to control can be identified by appropriate combinations of the above dimensions. We believe that these class-dimensions provide an important insight into the relationships between
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many of the fundamental techniques currently under development. Model Characteristics
information about causality; that must be obtained from another source (Leitch,1987) This lack of directionality makes the representation very general but can also make the reasoning or inference mechanism inefficient. Conversely, if the available knowledge contains a strong element of directionality between the variables then a procedural language will be more effective. However, the directionality may be very specific to a given application or situation, and hence procedural representations tend to be highly specialised. The trend has been to make representations more and more declarative to increase generality and to cope with the resultant loss in efficiency by more computational power.
Whilst model class is determined by the purpose and task of the model, the properties of the available knowledge used to model the process determine the characteristics of the model that can be utilised. These characteristics are used to form dimensions along which models can be classified and hence used to identify the most appropriate model. It is in this area that AI is having the most significant impact. In fact, the issues here are exactly those that underpin knowledge representation issues within AI, and are, therefore, intrinsically fundamental to AI itself. Figure 2 illustrates the characteristic dimensions. We identify five dimensions that represent the principal assumptions for modelling and reasoning about the physical world; in other domains other characteristics may be more important.
The third characteristic dimension concerns whether the system is considered to be continuous or discontinuous. In the continuous case, the system can only evolve through adjacent states whereas in the discontinuous case any state can follow a previous one, e.g. finite state machine. This leads to different techniques for generating the behaviour of the system. Continuity is clearly an important assumption in dynamic systems. However discontinuous dynamic systems can also be important.
A fundamental choice is whether to represent the dynamic evolution of the system or not. Until fairly recently most AI-based representation schemes were based on static models of the system assumed to be in equilibrium. Such models can, indeed, be useful especially in steady-state fault diagnosis. However, in (model-based) control and in diagnosing faults during the transient behaviour of a system, dynamic models are essential. Dynamic models require the representation of state and memory to reflect the energy storage, and hence delay, that occurs in the physical world. This is often confused, at least in AI circles, with temporal reasoning that reasons about the ordering of events in time. Static models can still have time-dependent variables, and even time-varying parameters, without being dynamic. Hence many temporal reasoning applications are based on static models. The choice of static or dynamic models fundamentally effects the representation language. In the former, algebraic equations will suffice whereas in the latter differential primitives are required.
An area of intense activity, now concerning both Control Engineering and AI researchers is Qualitative Modelling (Weld,1989, Leitch 1990). Although this will not be specifically discussed in this paper, it forms one of the main characteristic dimensions of models. This dimension concerns a spectrum of representations from purely quantitative models at one end and very weak qualitative representation at the other. In fact, exploring novel representation techniques, e.g. order-of-magnitudes and fuzzy sets has been a major pre-occupation of qualitative reasoning research (Shen,1992a). The goal is to utilise a representation that 'honestly' captures the available knowledge whilst satisfying the performance specifications. Finally, the fifth characteristic dimension is whether the knowledge of the model is uncertain or exact. Not to be confused with qualitative; a model can be qualitative and exact, and even honest! However, if the knowledge is uncertain then the representation should include some way of representing this. Two main forms of uncertainty have been recognised. The first, probability theory, concerns the situation when exact (deterministic) knowledge is not available and estimates based on the frequency of occurrence, represented by a probability density function, are used. It is essentially historically or
One of the early insights to stem from AI work is the distinction between declarative and procedural representations. Declarative representations describe relationships between variables or attributes of the physical world. They do not imply a directionality in the relationship, only that a set of variables are related by the description provided. For example, Ohm's law states that the current through and voltage across a resistor can be related by an empirical constant (given real-valued descriptions of the current and the Voltage, see later) called the resistance. It does not contain any
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experimentally based. The second approach is to represent imprecision explicitly, i.e. vagueness is captured by a graded membership, a real number between (0-1), representing the degree of set membership of a particular item (Zadeh,1973). Whether probability or possibility is used does not concern this dimension, only that uncertainty is a characteristic of models that may be important, and, therefore, should be explicitly supported.
EXTENDING THE SCOPE OF CONTROL ENGINEERING Although modelling, in all its various guises, is intrinsically important, the real advantage comes when using these techniques for control applications. The last decade has seen a rapid expansion in the tasks or purposes for which Control Engineering knowledge or methodology has been applied. In this respect we take Control to mean interacting and reasoning about the real physical world for some specified purpose. The original tasks of Control: regulatory and servomechanism control by using feedback or feed forward techniques have been supplemented by a range of tasks including :- fault diagnosis, condition monitoring, critical event simulation, training etc. Each of these tasks uses some of the various approaches to modelling discussed in the previous section. However, so far there has not been a significant attempt to identify the 'best' approach to modelling for a given application. Such a methodological approach is now becoming crucial as both the classes and characteristics of models and the range of applications continually expand. What is required is a set of relations that will identify the most appropriate model, and the corresponding solution technique, for a given class of application, the class being determined by the characteristics of the domain. In this regard the model classifications presented in the preceding sections begin to form a basis from which such a methodological approach can be generated. In this section, we begin the process of attempting to identify particular solution techniques with generic problems appearing within the Control Engineering literature.
Figure 2 shows the five principle characteristic dimensions. In fact, each combination of choices on these dimensions represent a different 'type' of model. Some of these combinations represent very strong research areas, e.g. Qualitative, Dynamic, Declarative, Uncertain, Continuous, Models (Fuzzy Qualitative Simulation, (Shen,1991b) whereas others represent well established techniques, e.g. Quantitative, Dynamic, Procedural, Exact, Continuous, Models (differential equations). Still others, e.g. Quantitative, Static & Dynamic, Declarative, Continuous Systems have yet to be investigated.
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One of the obvious approaches to the utilisation of Artificial Intelligence techniques within Control attempts to use Expert System techniques (Astrom,1986) as an adjunct to conventional methods. By placing an Expert System, usually using rule-based technology 'on top of an existing numerically based control system, the range of applicability of the controller can be extended by encoding into the Expert System rules for the adjustment of the control, either by modifying the control algorithm or by replacing it with another approach altogether. This essentially puts the Control Engineer on-line so that knowledge normally only used during the (off-line) design process is available during the actual operation. This approach is now being called Expert Control,
Figure 2. Model Characteristics In this section we have proposed a classification of models into classes, reflecting their purpose, and characteristics, dependent upon the properties of the available knowledge. Our intention is that such work lays the foundation for a methodological approach that will provide, at least, a set of guidelines to identify the most appropriate technique and associated model for a particular task and application characteristics.
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and represents a major activity for Control Engineers wishing to become involved in Artificial Intelligence approaches. It is attractive in that it utilises existing techniques, and hence skills; we believe that the majority of Control Engineers have adopted this route. In terms of the model classification proposed in Section 2, this approach adopts an implicit empirical model at the metalevel (Expertise) and existing control methods which can be either implicit (feedback) or explicit (model reference) using either empirical or theoretical knowledge at the object-level.
empirical models at the object-level, with Fuzzy Sets to represent the inherent vagueness or uncertainty. This approach is appropriate whenever there is some inherent difficulty with conventional modelling (numerically-based) techniques. Further, in many cases it has been shown that equivalent control performance can be achieved, however, qualitative methods have a distinct advantage of perspicuity (Francis,1989). t Meta-Ievel control can also be used with qualitative controllers. A good example of this is again Self-Organising Fuzzy Logic Controllers (Linkens,1991) where self-organising rules are used to modify the fuzzy rule-base to improve the overall performance.
The second approach uses AI techniques directly to model the system at a level of detail consistent with the available modelling knowledge and the task to be executed. Such approaches, sometimes called Qualitative Control, can be regarded as directly 'closing the feedback loop' by using AI methods. In this way qualitative representation of the control policy is used to compute the value of the control variable. This exposes a major shortcoming of qualitative methods for control applications: practical controllers still must output a numerical value. This requires that the qualitative value be 'approximated' by a numerical value; a symbol-to-signal transformation that is highly subjective. Fuzzy Logic Controllers are prime examples of this approach. They use implicit
Awaiting development is the qualitative counterpart of Model Reference Control. In this case the techniques of Qualitative Simulation can be used to represent the 'reference model', i.e. the ideal model, and the control adjusted such that the observed behaviour approaches the predicted response. The comparison between behaviours will also require a form of symbol to signal transformation in order to identify real-valued adjustments to the controller. We are not aware of this work being reported or even pursued. In terms of the classification this approach would utilise explicit, theoretical models at the object- level
Process
numerical controller
Expert System
Qualitative Control Expert System
Expert Control
Figure 3 Generic approaches to Control
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discrepancy detector
qualitative model
Qualitative Model-Based Control Figure 3
Autpmated Fault Djalmpsjs
The most frequent task performed by AI techniques is fault diagnosis. An enormous literature has amassed on such techniques. Many parallels can be drawn with the previous discussion on control methods. For example, initial diagnostic systems utilised Expert System techniques to represent empirical knowledge of relationships linking symptoms (observations) with possible diagnoses (actions). This approach is now termed Classification-Based diagnosis and has many examples of successful implementations. However, the approach inherits the limitations of Expert Systems (empirical knowledge) in that only known or experienced faults can be diagnosed. It conforms to the use of empirical, implicit models at the object-level. Another more general approach to diagnosis has recently been developing, based on the use of explicit models of the system to be diagnosed. Such methods are called Model-Based Diagnostic Systems (MBDS). The central idea of MBDS is the use of an explicit model of a system's structure and a simulation engine to generate the behaviour. Such a diagnostic mechanism determines some constituents of a physical system that account for the observed discrepancies, i.e. inconsistencies obtained from the model and driven by the observations.
requirement of accessibility of system states by diagnosing over time. Within systems based on iterative search (Leitch,1991) diagnosis is performed as a refining process in the following way. Initiallised by observations, an inference engine predicts a system's behaviour based on the system's model of normal behaviour, and a discrepancy detector detects the discrepancies between the predictions and the observations. The resulting discrepancies direct an iterative search of the space of possible model variations, using the most likely dimension first, until a 'matching' fault model is obtained. This requires that the possible model dimensions be identified and characterised, for instance, a dimension with the modification of the functional relationships between system variables or that with structural changes due to physical effects. From a general point of view, this method can be seen as a generalisation of the conventional numerical approaches to MBDS. Actually, although it is a new approach to applying AI modelling techniques for diagnosis, it utilises the basis concepts of the traditional approaches to model reference reasoning in control and system An important identification (Landau,1979). property of this technique is that the diagnostic process will be robust in that the recursive nature of the iterative search will provide a reduction in the sensitivity of the modelling assumptions.
The use of qualitative simulation techniques as the system model offers many exciting prospects for MBDS in that such developed diagnostic systems would allow the early detection of incipient failures (during the transient) and reduce the
These examples give some indication in the possible diversification of applications now being pursued and which can quite reasonably be considered to be a natural part of the Control Engineers remit. Similar arguments can be
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constructed for simulation application domains.
and
training
as
Expert System Classification Based Diagnosis
Process
discrepancy detector
candidate generator Qualitative Model Based Diagnosis
Figure 4. Generic Approaches to Diagnosis CONCLUSION We have attempted to show that underlying a great part of what is currently being developed, both within the AI community and the Control Engineering community, is fundamentally concerned with generating a range of approaches to the modelling of physical systems. It is here that AI is having a very profound impact on Control approaches, if not yet on theory. We have presented a classification of approaches
to modelling based on model classes and characteristics. We believe that such a classification is a necessary precursor to establishing a coherent methodological approach to identifying the most appropriate tool for a given application. By extending the formal methods of reasoning to include the prediction of the qualitative evolution of a dynamic system we now have a much greater diversity of tools with which to more effectively tackle complex applications.
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