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Robust Multivariable Control System Designs Through Real-Time Supervisory Knowledge-Based Systems
Copyright © IFAC Advanced Control of Chemical Processes, Kyoto, Japan, 1994
ROBUST MULTIVARlABLE CONTROL SYSTEM DESIGNS THROUGH REAL-TIME SUPERVISORY KNOWLEDGE-BASED SYSTEMS Scott J. Kendra, Michael R. Basila and Ali <;:inar Department of Chemical Engineering lllinois Institute of Technology Chicago, lllinois 60616 ABSTRACT. A supervisory knowledge-based system (KBS) is utilized to provide robust multi variable control of a chemical reaction process with reduced conservatism . The KBS monitors the process in order to detect control system faults or deterioration in controlled system performance due to changes in the process operation . If a fault or change in performance is detected, the KBS formulates and implements the necessary corrective action such as controller tuning or restructuring . This paper focuses on control loop tuning. The underlying mechanisms are discussed and validation results , using a rigorous simulation of the chemical reactor process, are presented . Key Words. Knowledge engineering, multi variable control, robust control, computer-aided controller design
1. INTRODUCTION The objective of intelligent process control is to incorporate the knowledge and experience of technical and operating personnel into the process control system . An intelligent control system would automatically tailor itself to changes in the process behavior and faults in the process or control system. Such a system would seek to maintain the process at the highest level of pefrormance under all circumstances. One key step towards intelligent control is the development of a supervisory knowledgebased system (KBS) that is capable of monitoring the control system performance and of automatically tuning or restructuring the control system as needed. In this communication, the extension of the Model-Object Based Supervisory Expert Control System (MOBECS) (Basila and <;inar, 1992) is reported to provide fault-tolerant, robust control of multivariable processes. Concern for safety, and for robust operation against instrument failures and complex plant dynamics due to time variant and non· linear process behavior create several challenges in most industrial plants . These challenges can be overcome by incorporating the expertise of the control engineer and plant operator into a KBS that supervises the regulatory process controls. Given the limitations in accurately modeling the dynamic process behavior, it is necessary to characterize the uncertainty in the model and design the controller such that it will be insen· sitive to mismatch between the true plant and the modeL In the frequency domain , many powerful results have been devel oped in recent years to guarantee robust stability. For example, the Hoc methodology allows the control engineer to guarantee robust stability in the presence of norm bounded uncertainties on the process modeL Unfortunately, frequency domain representations of uncertainty tend to encompass a larger set of plant models than is necessary to describe the true plant when all of the reasonable time domain uncertainties are included . Control systems are made robust to process model uncertainties by limit · ing the degree of closed-loop performance attained . The process model set considered to accomodate the uncertainty is often too large. causing design of controllers with overly sluggish performance. Despite the conservatism introduced by the robust con· trol methodology. at present it is one of the most promising tools for realistic multi variable control system design . The controller retuning and restructuring capabilities introduced by the supervi· sory KB control system reduce the conservatism and provide high performance controllers for all manifestations of the plant.
stochastic phenomena, such as input uncertainty, model parameter uncertainty, and uncertainty introduced through model reduction. Usually, a suitable norm bound is selected , and the control system is designed accordingly. Making this norm bound as small as possible will provide increased performance. Structured uncertainty describes deterministic phenomena such as the errors introduced by dynamic process nonlinearities . If the process displays a large degree of nonlinearity, when the process is moved from its steady state operating point through a set point change or nonstationary disturbance , the original process representation will be inaccurate and the control system can display poor performance. Therefore, uncertainty can be reduced if a new process model can be created, and the corresponding optimal controllaw developed. If the process was modeled in detail. it simply becomes a matter of relinearization about the new operating conditions. Such a change should improve the disturbance rejection properties of the system . In the event such a detailed model of the process is unavailable , a closed loop process identification scheme can be utilized to generate an updated linear process model for control system design . Controller tuning based on the current process model, development of a new process model followed by controller redesign, performance assessment of the current system, as well as other decisions a control engineer would make are all tasks which can be represented within a real-time, knowledgebased system setting. In general , robust control techniques use a priori information about the uncertainty in the process model to design a controller which will work well despite the process/model uncertainty. Hoc norm optimal methods indicate that most typical forms of uncer· tainty limit the achievable closed· loop bandwidth of the control system. As the uncertainty increases , the closed-loop bandwidth must be reduced. Information about process/model uncertainty is utilized in order to create a baseline control system design. Upon implementation, certain critical measures of the control system performance are assessed. Based upon this assessment, decisions concerning the adequacy of the control system must be made. The tasks involved in the decision process usually followed by a control engineer are representable in a real·time, KBS setting. The outcome of such an analysis results in the need for controller tuning based on the current process model , development of a new process model followed by controller redesign . or simply maintaining the current control strategy with the knowledge that the current level of performance is sufficient.
Defining accurate. tight bounds on the modeling uncertainty is an extremeh· difficult task . Most times it is necessarv to obtain a rough esti~ate the uncertainty and be satisfied with· the potentially conservative control system design result . If the uncertainty in the model can be reduced , the closed-loop performance can be improved. There are two types of uncertainty, structured and unstructured. Unstructured uncertainty is useful for describing
MOBECS is capable of emulating the steps typically carried out in
redesigning the multi variable control system and implementing the new control law with the entire control system remaining under automatic controL Such redesign efforts can be initiated by the operating personnel in an intuitive manner without deep understanding of the underlying design procedure, or MOBECS can be instructed to assess the performance level automatically and take appropriate redesign actions. MOBECS is also capable of recomputing a process model in response to process operating condition
Currently, S.J. Kendra is with Mobil Oil Company and M.a. Basila is with Amoco Chemical Company
371
The KBS was developed using a hybrid knowledge representation structure. The structure of the process and control system are captured in an object oriented representation paradigm. The knowledge of the process control engineer and operators is incorporated into rules and procedures. Rules are used to infer the state and performance of the process. Procedures attached to rules and object slots are used to perform specific function s during the inference process. The class-object structure of the knowledge base mirrors the structure of the process control system function blocks. Because the data structures are equivalent, both the process control system and the supervisory KBS are initialized from a common set of data files . The entire MOBEes prototype consists of four software components: Neuron Data's HEXPERT OBJECT shell , the MOBEes knowledge base, MOBEes application program, and deep knowledge programs (Basila,et al., 1990 ). The KBS is embedded in the application program , which performs all of the functions necessary to support the KBS. Some portions of the deep knowledge in MOBEeS, such as controller tuning, are also encoded as functions within the application program . This program provides data management, inference tracing and control, alarm , and report generation func tions .
changes. This in turn allows the development of new controllers with increased performance as compared to a controller which must be robust to all anticipated operating conditions. The KBS design and implementation are validated using a pilot scale tubular , fixed-bed CO oxidation reactor. Although this KBS is applied specifically to the control of a tubular reactor, the KBS design was generalized for application to any process or uni t operation . This paper focuses on the control system design results obtained using MOBECS. A detailed description of the KBS structure and development was presented previously (Basila and Ginar , 1990).
2_ PROCESS, CONTROL AND KBS DESCRIPTION In the experimental process, carbon monoxide (CO) is oxidized over a platinum-on-alumina catalyst to carbon dioxide in a tubular, jacketed reactor (Figure 1). The multivariable process control objective is to regulate the exit concentration and the reactor bed temperature by manipulating the feed gas temperature and CO concentration at the inlet to the reactor catalyst bed . Reaction kinetics are highly nonlinear in the selected operating region . The reactor is operated autothermally, which creates a thermal feedback loop within the process. This feedback loop , combined with the nonlinear dynamics causes the CO reactor to exhibit multiple steady state (bifurcation) behavior. As a result , control algorithms based upon linearized reduced order plant models are valid only over a relatively narrow range of operating conditions. In general , the performance of model based controllers deteriorates quickly as the operating conditions move away from the design point. The requirements for a control system under the supervision of a KBS are more demanding than that of simple regulatory control. Of primary importance is the structure of the data representation in both the process control system and the KBS . The two systems should use common data structures to simplify the problem of data transfer between the systems. The KBS must also have
3. AUTOMATIC Heo CONTROLLER TUNING The simplest form of corrective action that MOBEes can take is to retune the controllers. This is also the first step that the KBS takes when more drastic action , such controller restructuring , is necessary. In such cases, detuning the controller can stabilize the system while a more long term solution is sought. For SISO systems, Basila and Ginar (1992) noted that one of the most attractive features of IMC is the transparency of controller tuning . IMC provides a single tuning parameter, the filter time constant G, that is used to detune the algorithm to account for varying degrees ofplant/model mismatch. Decreasing G increases the speed of controller response, at the expense of robustness . Increasing Q results in more robust control loop behavior at the expense of controller performance. This transparency of tuning also makes Q a good measure of control law validity (plant/model mismatch). The development of Heo optimal controllers for multivariable systems is quite similar. With the Heo controller, there are no on-line tuning parameters such as the filter time constant. However , the weighting functions used in the development of the controller do share a similar interpretation in that they define the closed-loop bandwidth and the resonant peak of the controlled system . H eo optimal control theory is a frequency domain approach which attempts to minimize the H eo-norm of a closed loop transfer function matrix. The Heo- norm of a transfer matrix is the maximum over all frequencies of its largest singular value. The transfer function matrix of interest is usually chosen to represent the performance objectives as well as possible. Closed-loop performance and robustness characteristics can often be descri bed by the frequency domain properties of the sensitivity function 5( s ) and complementary sensitivity function T( 5) .
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Fig. 1. The autothermal reactor global access to every facet of the control system configuration and operation . This implies that the control system be completely configurable with built in features to implement parameter and configuration changes in a bumpless manner. The control system should perform routine data analysis calculations to provide the KBS with more meaningful real time information and reduce the data transfer and computational load on the KBS . Information that is process specific must be adequately compartmentalized to maintain the systems's generality. Finally. to adequately demon strate the utility of a supervisory KBS . the process control system for this study provides all of the process monitoring and control . operator interface. historical data collection. and event logging functions found in commercial distributed control systems. Based upon these requirements. a function block architecture was chosen for the process control system software. In this type of program structure, all of the control algorithms are coded as func tions or subroutines. The data associated with each algorithm is stored in an array or portion of an array which is referred to as the function or control block . Blocks can be linked together to form the control schemes. Control schemes are con figured by initializing the data values in the function block and specifying the block signal interconnections . As in commercial systems. the function blocks can be associated with different types of operator displays and data collection utilities . All of the information in a DCS is con figured by the process control engineer . !!DBEeS emulates the actions of the control engineer when it tunes or restructures the control loops.
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Figure 2 depicts the standard unity gain feedback setup. 5(5) defines the relation between the reference signal r and the error e. It also describes the effect of disturbance d on the output y . The properties of this function are useful in determining the perfor· mance of the closed-loop system. If the "size" of 5 (5) is "small" over the expected frequency range of the disturbances and reference inputs , the performance will be good, where "size" is usually defined for multi\'ariable systems as the maximum singular value. A useful measure of the closed-loop performance is system bandwidth . The frequency at which u (5 ( s )) becomes greater than ~ is defined as the system bandwidth , WB . The bandwidth of 5(,,) is a measure of the speed at which the system will reject disturbances at the plant output. Another useful measure of the
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372
Here in addition to minimizing the H 00 norm , one must enSUT~ that it is ~ 1. This is known as the H 00 small gain condition which guarantees robust stability and nominal performance for the specified weighting functions . With the supervisory KBS, we seek to increase the performance as much as possible without violating the conditions for robust stability by using the "I iteration method. By increasing "I , the scalar component of W I , the performance is increased until the small gain condition can no longer be met. This, is the optimal solution . The appeal of the H 00 optimal control problem solution is that it allows exact shaping of the singular values of the important closed-loop transfer matrices. With other methodologies, for example the H2 optimal control formulation, these shapes can only be obtained through clever selection of weighting matrices not directly related to closed-loop characteristics .
closed-loop performance is the resonant peak, the infinity norm of the sensitivity operator , IIS(3)1I 00' This measure indicates the maximum amplification of the worst possible system disturbance . Large values of the bandwidth indicate a fast speed of response and large values of II S lIoo indicate the potential for highly underdamped response . In order to impose specifications on the disturbance rejection , frequency dependent weights which bound the sensitivity function are defined . u (S(jw» ~ h -IWI-I(jw)1 IWI- I (jw )1 is chosen to be small in the low frequency range since most reference and disturbance signals exist in this range. Also, it is desirable to limit the maximum magnitude at high frequency , to prevent excessive high frequency disturbance amplification. An adjustable scalar weight , "I , is used to obtain the maximum performance possible without adjusting the shape of W I . As "I is increased, the performance is increased. A typical sensitivity func tion weighting specification would prescribe at least -80 dB of disturbance attenuation at steady state, a maximum disturbance amplification of 3 dB and a minimal bandwidth of 0.01 rad/sec . The complementary sensitivity function T (s ) = 1- S (s ) defines the relationship between the reference signal and the process out· put. Ideally, for good setpoint tracking we desire u (T (s » = I but because of the strictly proper nature of the open loop transfer function , lim w _ oo TUw ) = O. T( s ) also defines the transfer function between measurement noise and process outputs . In this case, u(T(s» = 0 is sought. This contradiction is resolved by noting that measurement noise is usually a high frequency phenomena and set point tracking signals are of low frequency . The same resonant peak and bandwidth considerations apply to the shaping ofthe complementary sensitivity operator, however , limitations on the achievable performance are most severely restricted by process/model uncertainty. Model uncertainty can be characterized as a family of linear time invariant models which includes the actual plant . The family is defined to be just large enough to con tai n all of the possi ble expected dynamic plant behavior. In this study, we consider multiplicative output perturbations 6.Mo on the nominal plant model
5. PERFORMANCE ASSESSMENT The KBS requires a method for the performance assessment of a multi variable system. The method developed enables the KBS to make direct comparisons between the attained and desired performance levels and to take decisions related to tuning multivariable controllers. The method utilizes system identification techniques to estimate the closed-loop transfer functions of interest , the sen· sitivity and complementary sensitivity functions . The frequency response of these estimated transfer functions is then compared to the design specifications "I , WI , and W3 . Significant deviations indicate a control system that is not behaving as designed. If certain key quantities in the estimated frequency response are unacceptable, a retuning strategy can be implemented and the revised controller should move the system in the direction of increased performance. The main parameters of interest in the performance assessment problem are the bandwidths and peak magnitudes of the sensi tivity and complementary sensitivity. The elements on the main diagonal of the transfer function matrix should possess as large a bandwidth as possible, subject to limitations in the peak magnitude of the respective transfer functions , and limitations on the input signal magnitude. The peak magnitude of the elements on the main diagonal should be limited , since this quantity can be loosely associated with the damping ratio of the closed· loop response. Ideally, the closed-loop response should be decoupled, hence significant gain in any off-diagonal element at any frequency is sub-optimal and is an indication of poor control system performance. Interest in these key quantities simplifies the system identification problem since a good fit is needed only in the range of frequencies that contains the expected bandwidths of Sand T . It is usually safe to ignore the low frequency asymptotes since we will always design a sufficient amount of gain into the system to assure reasonably offset free tracking and disturbance rejection .
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where u( .) denotes the maximum singular value of the operator.
4. CLOSED-LOOP ROBUSTNESS The effects of output multiplicative plant variations , 6.Mo(S) , on robust stability can be characterized by inspecting the closed-loop transfer functions . The multiplicative stability margin is defined to be the "size" of the smallest stable 6.Mo(S) that destabilizes the system. Given this information, the following condition must be satisfied for robustness to multiplicative output uncertainty:
6. SUPERVISORY KBS PERFORMANCE A KBS embedded within the regulatory and supervisory control layers of a distributed control system offer a means of capturing and implementing the knowledge of a control system design engineer on line. In this particular implementation of KBS technology. the end user realizes a H 00 optimal multi variable control system that is easily tunable on line. This enhancement of the op· timal control design procedure should have the effect of increasing the acceptance of advanced control systems by process operators and engineers since important characteristics of the closed loop response. such as speed of response , and overshoot can be influ· enced without knowledge of the intricate, mathematically intense details of the control system design methodology. In the event the direction of performance adjustment is unclear , a performance assessment experiment can be conducted by the KBS followed by controller redesign to adjust the relevent performance measures within accepted bounds . At each stage, process model redevel · opment is implemented if the process is currently operating at a significant distance from its nominal design conditions . The KBS monitors the process and control system for performance level achievement or operator controller tuning requests . If the control system performance is degrading or instability is detected, the supervisory KBS will first attempt to retune the controller. Detuning the controller to improve robustness usually results in operation at some suboptimal level of controller performance and is to be avoided if at all possible. In most cases, the required fix is to develop a process model which more accurately reflecu the current process operating conditions and then to redesign the control law accordingly.
Multiplicative Output Stability Robustness: Given that the nominal unity feedback system is stable. the smallest stable 6.Mo that destabilizes the system satisfies
Thus the smaller u(TUw» is , the larger the size of multiplicative output uncertainty that can be handled without destabilizin,; th e syskm. Thus we have another reason to limit the "size" of the complementary sensitivity function by defining frequency depen · dent functions which bound the respective uncertainties as tightly as possible and specify these as bounds on the relevent transfer function. The above methods work well for describing various uncertainties however . thev can lead to somewhat conseT\
The performance aasessment rules are placed on the inference agenda when automatic tuning is requested by the operator. Th_
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rules call the procedure for estimating the actual transfer functions and determine the corresponding bandwidths and peak magnitudes of the individual sensitivity and complementary sensitivity functions as well as the estimated values of 11511"" and IITII",, · Based on comparing these estimated values with expected performance measures , namely the peak values and bandwidths of the sensitivity and complementary sensitivity operators, a decision is made as to whether the closed loop bandwidth should be changed . Since the estimation proced ure is approximate , and design based on the singular values of the system mayor may not be excessively conservative, the maximal tolerable value of the estimated sensitivity and complementary sensitivity are allowed to be as large as 10 dB prior to controller detuning . Values less than 6 dB are considered candidates for increasing the performance of the controller . The goal is to maximize the closed loop bandwidth while limiting the peak values of the true process sensitivity and complementary sensitivity. These quantities , while specified at the design stage using the nominal model mayor may not be the same as that achieved on the true process due to uncertainty in the nominal model description. This step closes the KBS design loop in a nonconservative manner , since to the best of our knowledge , the true physical closed loop system has met with im posed design constraints. This is significantly different than the procedure of taking a linear model , designing a controller and implementing the controller, hoping for reasonable performance on the true system.
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Fig. 3. Design specifications, bandwidth=0.002 to maximize performance, based either on the operator 's intuition of the degree of performance that can be expected , or on a quantitative measure , such as the performance assessment method described previously. In the following , we will see how the KBS manages both situations and determines the appropriate tuning action s. In the first case , a potentially conservative control system with a design bandwidth of 0.002 is initialized by th e process engi neer (Fig. 3). Upon implementation , the test suite was run on the dosed-loop system . Figure 4 presents the results of a 5% step change in the effluent concentration, no interaction was observed and the response was overdamped with a time constant of about 1200sec. Figure 5 illustrates the results of trying to track a 3° h' step change in the reactor bed while maintaining a constant reactor effluent concentration. This is a difficult controller performance test , given the exponential influence of temperature upon reaction rate. There is a small degree of interaction in Figure 5A , and a time constant of approximatel y 500sec . At this point , neither the KBS or the operator can reall y say anything about the response characteristics of the system , except for the fact that the responses were overdamped with a time constant of over lOOOsec , and some small degrees of interaction were apparent . This mayor may not be the best performance attainable by this system . The only way to ascertain whether increased performance is possible is through an experiment, either a controller with a larger bandwidth can be implemented, and then tested to see if the resulting performance has improved , or the frequency domain performance can be estimated . The second procedure is followed here, and the KBS is allowed to completely manage the entire assessment/design procedure. The key feature resulting from the performance assessment in Figure 6, is the significant gain in the T12 element. Significant gains in the off diagonal elements of 5 and T operators are a strong in dicator of process / model mismatch . The uncertainty of th e model used in the design procedure is minimal , since there was no significant mismatch introduced by the process operating conditions , and both models were derived from the same source equations .
The multivariable design , assessment , and reconfiguration func tions within MOBECS were validated using the rigorous nonlinear dynamic reactor model discussed by Adomaitis and Ginar (1988). The test cases used for validation of the KBS include H"" controller tuning based on the performance assessment method , operator driven KBS controller tuning , and controller restructuring, which involves the development of a process model for control system development , followed by controller design. These tests of the KBS are aimed at addressing some of the principal criticisms preventing use of true multi variable control systems within the chemical processing industry. Firstly, that multi variable, optimal control systems are not easily tuned on line, secondly. they can be overly sensitive to the nonlinear , poorly modeled nature of chemical processes, and finally, that typical designs based on the robust control system methodology are excessively conservative. The multivariable control functions within MOBECS can easily carry out the sophisticated process modeling, controller design, implementation , and assessment issues that must be addressed . The KBS design methodology is essentially transparent to the operator , who must only decide that controller tuning is required , and whether the closed loop response should be slower or faster . If this decision cannot be made accurately by the operator, he need only request a fully automated tuning procedure. In this case, the performance assessment is carried out first, then hased on the estimated bandwidths and peak magnitudes of the sensitivity and complementary sensitivity, an appropriate tuning action is generated by the KBS rule network. Controller restructuring is only considered if the process has moved a considerable distance from its nominal operating conditions, and this move has induced a degradation in the performance measures . Restructuring is con sidered during each episode of controller tuning .
7. KBS VALIDATION TESTS
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Set point following tests include the tracking of a 5% step change in the desired effluent concentration of carbon monoxide. and also the tracking of a 3° h- step change in the desired reactor bed temperature. Rejection of disturbances . such as total flow variations, and inlet coolant temperature variations were also considered and reported elsewhere ( Kendra. 1992 ). The test suite results contain al l significant aspect s of the reactor operation . In the fig ures that display reactor variable responses the following conventions have been used . In Figure (A). the solid line represents the exit concentration. the dashed line represents the exit concentration setpoint , and the dotted line represents the inlet concentration . In Figure (B ). the solid line represents the bed temperature. the dashed line represents the bed temperature setpoint. and the dotted line represents the inlet coolant temperature. Finally. Figure (C ) represents the total flow rate and Figure ( D) represents the quench flow rate . In all simulations. random disturbances were added to the total flow rate and process output measurement signals.
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(Fig. S) . The last performance assessment results , using this controller indicate that the threshold value of 6dB has been reached . Figure 9 presents the results of a 5% step change in the effluent concentration, no interaction was observed and the response was overdamped with a time constan t of about 300s, a factor of four improvement over the initial design . Figure 10 illustrates the results of trying to track a 3° K step change in the reactor bed while maintaining a constant reactor effluent concentration. The interaction has been decreased significantl y ( Fig. lOA ), and the time constant of approximately 500sec has been reduced to about 150sec.
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The pseudo steady-state assumption used in solving the large system of nonlinear ODEs during simulation is the likely culprit, introducing significant error in the high frequency dynamics of the nonlinear model. The bandwidths of the estimated transfer functions on the main diagonal are in accordance with the design specifications (Fig. 6) . The estimated singular value ~ode plots are given in Figure 7 _ The significant spread in the maxJ~a1 and minimal singular values indicates the potential for certain in put directions to lead to worse results than in others. The peak magnitude is 4.95dB , a value less than the threshold for acceptable performance. Based on these results, the KBS determined that controller performance could be improved by increasing the closed-loop bandwidth at the next design iteration. After two passes through the estimation/design procedure by the KBS , the limiting bandwidth was determined to be O.OOS rad/ s
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MDBECS uses additional rules and methods to develop the new con-
It has been demonstrated that the KBS has the capability to assess key performance parameters and act on them to improve the closed loop performance. Whether this result is also conservative remains to be Sei!n . If the operator dei!med the last result to be too conservative, he has the option to induce a change in the controller tuning from the console. The next design results, for a bandwidth increased to 0.032 indicate that a poor choice was made in retuning the controller for more performance (Fig. 11 ). The resulting design is highly susceptible to noise amplification and even drives the controller into saturation conditions, Figure llD .
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trol system configuration . Control system function blocks are classified as primary function blocks and secondary function blocks. Primary function blocks are those that have a physical realization in an analog control system and include controllers, analog input and output scaling blocks, and manual loading station blocks . Every primary control block in the control system is represented by an object in the KBS . Secondary control blocks include blocks used to calculate performance indices as well as blocks used to store data for primary control blocks. The information contained in secondary function blocks is incorporated into the KBS as slots in the individual objects.
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The entire procedure is performed in real -time without intervention and the process continues to operate at the highest possible level of control while restructuring is underway. Control solutions do not have to be prespecified for any particular problem or set of operating conditions. A new controller is synthesized from the process model and the current operating conditions. Controller input-output selections are determined during the model reduction process from the lists of available plant inputs and outputs . P06itive results have been obtained for the case when the process has changed operating conditions , requiring the KBS to perform automatic controller restructuring ( Kendra, 1992).
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ACKNOWLEDGEMENT: The Dlinois Institute of Technology Research Institute (IITRI) Fellowship awarded to S. Kendra. and the IIT-IITRI Synergy Grant awarded to M. Basila are gratefully acknowledged .
Automatic controller restructuring. One of the most important features of the supervisory KBS is the ability to automatically restructure the process control algorithms. The nei!d for restructuring can arise from several different causes. If a primary transmitter or sensor fails, control may be lost over one or more plant inputs. Multiple input-multiple output (MIMO) systems are particularly vulnerable to input failures. In addition , the process behavior may change as the result of changes in operating conditions, different operating modes (e.g. startup or circling), and variations such as exchanger fouling and catalyst deactivation . Control laws developed from a linearized process model are generally valid over a limited region around the design point. As the process moves away from the design conditions, the controller performance may deteriorate and become unacceptable.
REFERENCES Basila, M. R. , A. Ginar , and G . Stefanek, 1990. A Model-Object Based Supervisory Expert System for Fault Tolerant Chemical Reactor Control Comput . Chem . Engng. , 14 , 551-560. Basila, M. R. and A. GlOar, 1992. Reliable Control of a Chemical Reactor with a Supervisory KBS Pm::. Srd IFA C Symp. D YCORD 92, College Park, MD 333-338. Kendra, S. J. and A. GlOar , 1991. On the Relation Between Model Reduction and Robust Control System Design. In Proc . 1991 A ICHE Annual Meeting, San Francisco Ca. Kendra, S. J ., 1992. Robust Multivariable Controller Design In Intelligtmt Control By Knowledge Based Systems. Ph.D . Thesis, Dlinois Institute of Technology.
MDBECS is capable of automatically identifying the nei!d to restruc-
ture the control algorithm and then developing and implementing the necessary corrective action . Consequently, the control system is never allowed to deviate far from the maximum level of con troller performance. In restructuring , the first step is to identify the nei!d for restructuring. The KBS continuously monitors the process operating conditions and determines if persistent dist urbances or a request for new operating conditions have forced the nei!d for redevelopment of a new process model. Another means of triggering the restructuring process is the detection of a primary sensor malfunction . The next steps in the restructuring process are to formulate a new control law and block configuration . Finally, the new configuration must be implemented in the control system . The reasoning and actions of the control en ginei!r to develop a system configuration are largely procedural . Consequently. the KBSs dei!p knowledge concerning control law design and system configuration are incorporated into methods that are called by the restructuring rules . Using a list of available process sensors, MDBECS creates a linearized process model based upon the current operating conditions . performs a model reduction ( Kendra. 1991 ). and then formulates the new Hoc control law from the reduced order model. These actions are performed by functions in the application program and tasks that operate on a separate workstation. The resultant control law is expressed as a discrete state-space representation .
376
ROBUST MULTIVARlABLE CONTROL SYSTEM DESIGNS THROUGH REAL-TIME SUPERVISORY KNOWLEDGE-BASED SYSTEMS Scott J. Kendra, Michael R. Basila and Ali <;:inar Department of Chemical Engineering lllinois Institute of Technology Chicago, lllinois 60616 ABSTRACT. A supervisory knowledge-based system (KBS) is utilized to provide robust multi variable control of a chemical reaction process with reduced conservatism . The KBS monitors the process in order to detect control system faults or deterioration in controlled system performance due to changes in the process operation . If a fault or change in performance is detected, the KBS formulates and implements the necessary corrective action such as controller tuning or restructuring . This paper focuses on control loop tuning. The underlying mechanisms are discussed and validation results , using a rigorous simulation of the chemical reactor process, are presented . Key Words. Knowledge engineering, multi variable control, robust control, computer-aided controller design
1. INTRODUCTION The objective of intelligent process control is to incorporate the knowledge and experience of technical and operating personnel into the process control system . An intelligent control system would automatically tailor itself to changes in the process behavior and faults in the process or control system. Such a system would seek to maintain the process at the highest level of pefrormance under all circumstances. One key step towards intelligent control is the development of a supervisory knowledgebased system (KBS) that is capable of monitoring the control system performance and of automatically tuning or restructuring the control system as needed. In this communication, the extension of the Model-Object Based Supervisory Expert Control System (MOBECS) (Basila and <;inar, 1992) is reported to provide fault-tolerant, robust control of multivariable processes. Concern for safety, and for robust operation against instrument failures and complex plant dynamics due to time variant and non· linear process behavior create several challenges in most industrial plants . These challenges can be overcome by incorporating the expertise of the control engineer and plant operator into a KBS that supervises the regulatory process controls. Given the limitations in accurately modeling the dynamic process behavior, it is necessary to characterize the uncertainty in the model and design the controller such that it will be insen· sitive to mismatch between the true plant and the modeL In the frequency domain , many powerful results have been devel oped in recent years to guarantee robust stability. For example, the Hoc methodology allows the control engineer to guarantee robust stability in the presence of norm bounded uncertainties on the process modeL Unfortunately, frequency domain representations of uncertainty tend to encompass a larger set of plant models than is necessary to describe the true plant when all of the reasonable time domain uncertainties are included . Control systems are made robust to process model uncertainties by limit · ing the degree of closed-loop performance attained . The process model set considered to accomodate the uncertainty is often too large. causing design of controllers with overly sluggish performance. Despite the conservatism introduced by the robust con· trol methodology. at present it is one of the most promising tools for realistic multi variable control system design . The controller retuning and restructuring capabilities introduced by the supervi· sory KB control system reduce the conservatism and provide high performance controllers for all manifestations of the plant.
stochastic phenomena, such as input uncertainty, model parameter uncertainty, and uncertainty introduced through model reduction. Usually, a suitable norm bound is selected , and the control system is designed accordingly. Making this norm bound as small as possible will provide increased performance. Structured uncertainty describes deterministic phenomena such as the errors introduced by dynamic process nonlinearities . If the process displays a large degree of nonlinearity, when the process is moved from its steady state operating point through a set point change or nonstationary disturbance , the original process representation will be inaccurate and the control system can display poor performance. Therefore, uncertainty can be reduced if a new process model can be created, and the corresponding optimal controllaw developed. If the process was modeled in detail. it simply becomes a matter of relinearization about the new operating conditions. Such a change should improve the disturbance rejection properties of the system . In the event such a detailed model of the process is unavailable , a closed loop process identification scheme can be utilized to generate an updated linear process model for control system design . Controller tuning based on the current process model, development of a new process model followed by controller redesign, performance assessment of the current system, as well as other decisions a control engineer would make are all tasks which can be represented within a real-time, knowledgebased system setting. In general , robust control techniques use a priori information about the uncertainty in the process model to design a controller which will work well despite the process/model uncertainty. Hoc norm optimal methods indicate that most typical forms of uncer· tainty limit the achievable closed· loop bandwidth of the control system. As the uncertainty increases , the closed-loop bandwidth must be reduced. Information about process/model uncertainty is utilized in order to create a baseline control system design. Upon implementation, certain critical measures of the control system performance are assessed. Based upon this assessment, decisions concerning the adequacy of the control system must be made. The tasks involved in the decision process usually followed by a control engineer are representable in a real·time, KBS setting. The outcome of such an analysis results in the need for controller tuning based on the current process model , development of a new process model followed by controller redesign . or simply maintaining the current control strategy with the knowledge that the current level of performance is sufficient.
Defining accurate. tight bounds on the modeling uncertainty is an extremeh· difficult task . Most times it is necessarv to obtain a rough esti~ate the uncertainty and be satisfied with· the potentially conservative control system design result . If the uncertainty in the model can be reduced , the closed-loop performance can be improved. There are two types of uncertainty, structured and unstructured. Unstructured uncertainty is useful for describing
MOBECS is capable of emulating the steps typically carried out in
redesigning the multi variable control system and implementing the new control law with the entire control system remaining under automatic controL Such redesign efforts can be initiated by the operating personnel in an intuitive manner without deep understanding of the underlying design procedure, or MOBECS can be instructed to assess the performance level automatically and take appropriate redesign actions. MOBECS is also capable of recomputing a process model in response to process operating condition
Currently, S.J. Kendra is with Mobil Oil Company and M.a. Basila is with Amoco Chemical Company
371
The KBS was developed using a hybrid knowledge representation structure. The structure of the process and control system are captured in an object oriented representation paradigm. The knowledge of the process control engineer and operators is incorporated into rules and procedures. Rules are used to infer the state and performance of the process. Procedures attached to rules and object slots are used to perform specific function s during the inference process. The class-object structure of the knowledge base mirrors the structure of the process control system function blocks. Because the data structures are equivalent, both the process control system and the supervisory KBS are initialized from a common set of data files . The entire MOBEes prototype consists of four software components: Neuron Data's HEXPERT OBJECT shell , the MOBEes knowledge base, MOBEes application program, and deep knowledge programs (Basila,et al., 1990 ). The KBS is embedded in the application program , which performs all of the functions necessary to support the KBS. Some portions of the deep knowledge in MOBEeS, such as controller tuning, are also encoded as functions within the application program . This program provides data management, inference tracing and control, alarm , and report generation func tions .
changes. This in turn allows the development of new controllers with increased performance as compared to a controller which must be robust to all anticipated operating conditions. The KBS design and implementation are validated using a pilot scale tubular , fixed-bed CO oxidation reactor. Although this KBS is applied specifically to the control of a tubular reactor, the KBS design was generalized for application to any process or uni t operation . This paper focuses on the control system design results obtained using MOBECS. A detailed description of the KBS structure and development was presented previously (Basila and Ginar , 1990).
2_ PROCESS, CONTROL AND KBS DESCRIPTION In the experimental process, carbon monoxide (CO) is oxidized over a platinum-on-alumina catalyst to carbon dioxide in a tubular, jacketed reactor (Figure 1). The multivariable process control objective is to regulate the exit concentration and the reactor bed temperature by manipulating the feed gas temperature and CO concentration at the inlet to the reactor catalyst bed . Reaction kinetics are highly nonlinear in the selected operating region . The reactor is operated autothermally, which creates a thermal feedback loop within the process. This feedback loop , combined with the nonlinear dynamics causes the CO reactor to exhibit multiple steady state (bifurcation) behavior. As a result , control algorithms based upon linearized reduced order plant models are valid only over a relatively narrow range of operating conditions. In general , the performance of model based controllers deteriorates quickly as the operating conditions move away from the design point. The requirements for a control system under the supervision of a KBS are more demanding than that of simple regulatory control. Of primary importance is the structure of the data representation in both the process control system and the KBS . The two systems should use common data structures to simplify the problem of data transfer between the systems. The KBS must also have
3. AUTOMATIC Heo CONTROLLER TUNING The simplest form of corrective action that MOBEes can take is to retune the controllers. This is also the first step that the KBS takes when more drastic action , such controller restructuring , is necessary. In such cases, detuning the controller can stabilize the system while a more long term solution is sought. For SISO systems, Basila and Ginar (1992) noted that one of the most attractive features of IMC is the transparency of controller tuning . IMC provides a single tuning parameter, the filter time constant G, that is used to detune the algorithm to account for varying degrees ofplant/model mismatch. Decreasing G increases the speed of controller response, at the expense of robustness . Increasing Q results in more robust control loop behavior at the expense of controller performance. This transparency of tuning also makes Q a good measure of control law validity (plant/model mismatch). The development of Heo optimal controllers for multivariable systems is quite similar. With the Heo controller, there are no on-line tuning parameters such as the filter time constant. However , the weighting functions used in the development of the controller do share a similar interpretation in that they define the closed-loop bandwidth and the resonant peak of the controlled system . H eo optimal control theory is a frequency domain approach which attempts to minimize the H eo-norm of a closed loop transfer function matrix. The Heo- norm of a transfer matrix is the maximum over all frequencies of its largest singular value. The transfer function matrix of interest is usually chosen to represent the performance objectives as well as possible. Closed-loop performance and robustness characteristics can often be descri bed by the frequency domain properties of the sensitivity function 5( s ) and complementary sensitivity function T( 5) .
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Fig. 1. The autothermal reactor global access to every facet of the control system configuration and operation . This implies that the control system be completely configurable with built in features to implement parameter and configuration changes in a bumpless manner. The control system should perform routine data analysis calculations to provide the KBS with more meaningful real time information and reduce the data transfer and computational load on the KBS . Information that is process specific must be adequately compartmentalized to maintain the systems's generality. Finally. to adequately demon strate the utility of a supervisory KBS . the process control system for this study provides all of the process monitoring and control . operator interface. historical data collection. and event logging functions found in commercial distributed control systems. Based upon these requirements. a function block architecture was chosen for the process control system software. In this type of program structure, all of the control algorithms are coded as func tions or subroutines. The data associated with each algorithm is stored in an array or portion of an array which is referred to as the function or control block . Blocks can be linked together to form the control schemes. Control schemes are con figured by initializing the data values in the function block and specifying the block signal interconnections . As in commercial systems. the function blocks can be associated with different types of operator displays and data collection utilities . All of the information in a DCS is con figured by the process control engineer . !!DBEeS emulates the actions of the control engineer when it tunes or restructures the control loops.
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Figure 2 depicts the standard unity gain feedback setup. 5(5) defines the relation between the reference signal r and the error e. It also describes the effect of disturbance d on the output y . The properties of this function are useful in determining the perfor· mance of the closed-loop system. If the "size" of 5 (5) is "small" over the expected frequency range of the disturbances and reference inputs , the performance will be good, where "size" is usually defined for multi\'ariable systems as the maximum singular value. A useful measure of the closed-loop performance is system bandwidth . The frequency at which u (5 ( s )) becomes greater than ~ is defined as the system bandwidth , WB . The bandwidth of 5(,,) is a measure of the speed at which the system will reject disturbances at the plant output. Another useful measure of the
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372
Here in addition to minimizing the H 00 norm , one must enSUT~ that it is ~ 1. This is known as the H 00 small gain condition which guarantees robust stability and nominal performance for the specified weighting functions . With the supervisory KBS, we seek to increase the performance as much as possible without violating the conditions for robust stability by using the "I iteration method. By increasing "I , the scalar component of W I , the performance is increased until the small gain condition can no longer be met. This, is the optimal solution . The appeal of the H 00 optimal control problem solution is that it allows exact shaping of the singular values of the important closed-loop transfer matrices. With other methodologies, for example the H2 optimal control formulation, these shapes can only be obtained through clever selection of weighting matrices not directly related to closed-loop characteristics .
closed-loop performance is the resonant peak, the infinity norm of the sensitivity operator , IIS(3)1I 00' This measure indicates the maximum amplification of the worst possible system disturbance . Large values of the bandwidth indicate a fast speed of response and large values of II S lIoo indicate the potential for highly underdamped response . In order to impose specifications on the disturbance rejection , frequency dependent weights which bound the sensitivity function are defined . u (S(jw» ~ h -IWI-I(jw)1 IWI- I (jw )1 is chosen to be small in the low frequency range since most reference and disturbance signals exist in this range. Also, it is desirable to limit the maximum magnitude at high frequency , to prevent excessive high frequency disturbance amplification. An adjustable scalar weight , "I , is used to obtain the maximum performance possible without adjusting the shape of W I . As "I is increased, the performance is increased. A typical sensitivity func tion weighting specification would prescribe at least -80 dB of disturbance attenuation at steady state, a maximum disturbance amplification of 3 dB and a minimal bandwidth of 0.01 rad/sec . The complementary sensitivity function T (s ) = 1- S (s ) defines the relationship between the reference signal and the process out· put. Ideally, for good setpoint tracking we desire u (T (s » = I but because of the strictly proper nature of the open loop transfer function , lim w _ oo TUw ) = O. T( s ) also defines the transfer function between measurement noise and process outputs . In this case, u(T(s» = 0 is sought. This contradiction is resolved by noting that measurement noise is usually a high frequency phenomena and set point tracking signals are of low frequency . The same resonant peak and bandwidth considerations apply to the shaping ofthe complementary sensitivity operator, however , limitations on the achievable performance are most severely restricted by process/model uncertainty. Model uncertainty can be characterized as a family of linear time invariant models which includes the actual plant . The family is defined to be just large enough to con tai n all of the possi ble expected dynamic plant behavior. In this study, we consider multiplicative output perturbations 6.Mo on the nominal plant model
5. PERFORMANCE ASSESSMENT The KBS requires a method for the performance assessment of a multi variable system. The method developed enables the KBS to make direct comparisons between the attained and desired performance levels and to take decisions related to tuning multivariable controllers. The method utilizes system identification techniques to estimate the closed-loop transfer functions of interest , the sen· sitivity and complementary sensitivity functions . The frequency response of these estimated transfer functions is then compared to the design specifications "I , WI , and W3 . Significant deviations indicate a control system that is not behaving as designed. If certain key quantities in the estimated frequency response are unacceptable, a retuning strategy can be implemented and the revised controller should move the system in the direction of increased performance. The main parameters of interest in the performance assessment problem are the bandwidths and peak magnitudes of the sensi tivity and complementary sensitivity. The elements on the main diagonal of the transfer function matrix should possess as large a bandwidth as possible, subject to limitations in the peak magnitude of the respective transfer functions , and limitations on the input signal magnitude. The peak magnitude of the elements on the main diagonal should be limited , since this quantity can be loosely associated with the damping ratio of the closed· loop response. Ideally, the closed-loop response should be decoupled, hence significant gain in any off-diagonal element at any frequency is sub-optimal and is an indication of poor control system performance. Interest in these key quantities simplifies the system identification problem since a good fit is needed only in the range of frequencies that contains the expected bandwidths of Sand T . It is usually safe to ignore the low frequency asymptotes since we will always design a sufficient amount of gain into the system to assure reasonably offset free tracking and disturbance rejection .
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4. CLOSED-LOOP ROBUSTNESS The effects of output multiplicative plant variations , 6.Mo(S) , on robust stability can be characterized by inspecting the closed-loop transfer functions . The multiplicative stability margin is defined to be the "size" of the smallest stable 6.Mo(S) that destabilizes the system. Given this information, the following condition must be satisfied for robustness to multiplicative output uncertainty:
6. SUPERVISORY KBS PERFORMANCE A KBS embedded within the regulatory and supervisory control layers of a distributed control system offer a means of capturing and implementing the knowledge of a control system design engineer on line. In this particular implementation of KBS technology. the end user realizes a H 00 optimal multi variable control system that is easily tunable on line. This enhancement of the op· timal control design procedure should have the effect of increasing the acceptance of advanced control systems by process operators and engineers since important characteristics of the closed loop response. such as speed of response , and overshoot can be influ· enced without knowledge of the intricate, mathematically intense details of the control system design methodology. In the event the direction of performance adjustment is unclear , a performance assessment experiment can be conducted by the KBS followed by controller redesign to adjust the relevent performance measures within accepted bounds . At each stage, process model redevel · opment is implemented if the process is currently operating at a significant distance from its nominal design conditions . The KBS monitors the process and control system for performance level achievement or operator controller tuning requests . If the control system performance is degrading or instability is detected, the supervisory KBS will first attempt to retune the controller. Detuning the controller to improve robustness usually results in operation at some suboptimal level of controller performance and is to be avoided if at all possible. In most cases, the required fix is to develop a process model which more accurately reflecu the current process operating conditions and then to redesign the control law accordingly.
Multiplicative Output Stability Robustness: Given that the nominal unity feedback system is stable. the smallest stable 6.Mo that destabilizes the system satisfies
Thus the smaller u(TUw» is , the larger the size of multiplicative output uncertainty that can be handled without destabilizin,; th e syskm. Thus we have another reason to limit the "size" of the complementary sensitivity function by defining frequency depen · dent functions which bound the respective uncertainties as tightly as possible and specify these as bounds on the relevent transfer function. The above methods work well for describing various uncertainties however . thev can lead to somewhat conseT\
The performance aasessment rules are placed on the inference agenda when automatic tuning is requested by the operator. Th_
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rules call the procedure for estimating the actual transfer functions and determine the corresponding bandwidths and peak magnitudes of the individual sensitivity and complementary sensitivity functions as well as the estimated values of 11511"" and IITII",, · Based on comparing these estimated values with expected performance measures , namely the peak values and bandwidths of the sensitivity and complementary sensitivity operators, a decision is made as to whether the closed loop bandwidth should be changed . Since the estimation proced ure is approximate , and design based on the singular values of the system mayor may not be excessively conservative, the maximal tolerable value of the estimated sensitivity and complementary sensitivity are allowed to be as large as 10 dB prior to controller detuning . Values less than 6 dB are considered candidates for increasing the performance of the controller . The goal is to maximize the closed loop bandwidth while limiting the peak values of the true process sensitivity and complementary sensitivity. These quantities , while specified at the design stage using the nominal model mayor may not be the same as that achieved on the true process due to uncertainty in the nominal model description. This step closes the KBS design loop in a nonconservative manner , since to the best of our knowledge , the true physical closed loop system has met with im posed design constraints. This is significantly different than the procedure of taking a linear model , designing a controller and implementing the controller, hoping for reasonable performance on the true system.
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The multivariable design , assessment , and reconfiguration func tions within MOBECS were validated using the rigorous nonlinear dynamic reactor model discussed by Adomaitis and Ginar (1988). The test cases used for validation of the KBS include H"" controller tuning based on the performance assessment method , operator driven KBS controller tuning , and controller restructuring, which involves the development of a process model for control system development , followed by controller design. These tests of the KBS are aimed at addressing some of the principal criticisms preventing use of true multi variable control systems within the chemical processing industry. Firstly, that multi variable, optimal control systems are not easily tuned on line, secondly. they can be overly sensitive to the nonlinear , poorly modeled nature of chemical processes, and finally, that typical designs based on the robust control system methodology are excessively conservative. The multivariable control functions within MOBECS can easily carry out the sophisticated process modeling, controller design, implementation , and assessment issues that must be addressed . The KBS design methodology is essentially transparent to the operator , who must only decide that controller tuning is required , and whether the closed loop response should be slower or faster . If this decision cannot be made accurately by the operator, he need only request a fully automated tuning procedure. In this case, the performance assessment is carried out first, then hased on the estimated bandwidths and peak magnitudes of the sensitivity and complementary sensitivity, an appropriate tuning action is generated by the KBS rule network. Controller restructuring is only considered if the process has moved a considerable distance from its nominal operating conditions, and this move has induced a degradation in the performance measures . Restructuring is con sidered during each episode of controller tuning .
7. KBS VALIDATION TESTS
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Set point following tests include the tracking of a 5% step change in the desired effluent concentration of carbon monoxide. and also the tracking of a 3° h- step change in the desired reactor bed temperature. Rejection of disturbances . such as total flow variations, and inlet coolant temperature variations were also considered and reported elsewhere ( Kendra. 1992 ). The test suite results contain al l significant aspect s of the reactor operation . In the fig ures that display reactor variable responses the following conventions have been used . In Figure (A). the solid line represents the exit concentration. the dashed line represents the exit concentration setpoint , and the dotted line represents the inlet concentration . In Figure (B ). the solid line represents the bed temperature. the dashed line represents the bed temperature setpoint. and the dotted line represents the inlet coolant temperature. Finally. Figure (C ) represents the total flow rate and Figure ( D) represents the quench flow rate . In all simulations. random disturbances were added to the total flow rate and process output measurement signals.
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MDBECS uses additional rules and methods to develop the new con-
It has been demonstrated that the KBS has the capability to assess key performance parameters and act on them to improve the closed loop performance. Whether this result is also conservative remains to be Sei!n . If the operator dei!med the last result to be too conservative, he has the option to induce a change in the controller tuning from the console. The next design results, for a bandwidth increased to 0.032 indicate that a poor choice was made in retuning the controller for more performance (Fig. 11 ). The resulting design is highly susceptible to noise amplification and even drives the controller into saturation conditions, Figure llD .
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trol system configuration . Control system function blocks are classified as primary function blocks and secondary function blocks. Primary function blocks are those that have a physical realization in an analog control system and include controllers, analog input and output scaling blocks, and manual loading station blocks . Every primary control block in the control system is represented by an object in the KBS . Secondary control blocks include blocks used to calculate performance indices as well as blocks used to store data for primary control blocks. The information contained in secondary function blocks is incorporated into the KBS as slots in the individual objects.
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The entire procedure is performed in real -time without intervention and the process continues to operate at the highest possible level of control while restructuring is underway. Control solutions do not have to be prespecified for any particular problem or set of operating conditions. A new controller is synthesized from the process model and the current operating conditions. Controller input-output selections are determined during the model reduction process from the lists of available plant inputs and outputs . P06itive results have been obtained for the case when the process has changed operating conditions , requiring the KBS to perform automatic controller restructuring ( Kendra, 1992).
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8. SUMMARY AND CONCLUSIONS The supervisory KBS developed is capable of assessing the process control performance and tailoring the control system configuration to match changes in the process behavior or faults in the process control system. MDBECS emulates the reasoning of the plant operator and process control enginei!r to formulate corrective actions to maintain the process at the highest possible level of control.
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ACKNOWLEDGEMENT: The Dlinois Institute of Technology Research Institute (IITRI) Fellowship awarded to S. Kendra. and the IIT-IITRI Synergy Grant awarded to M. Basila are gratefully acknowledged .
Automatic controller restructuring. One of the most important features of the supervisory KBS is the ability to automatically restructure the process control algorithms. The nei!d for restructuring can arise from several different causes. If a primary transmitter or sensor fails, control may be lost over one or more plant inputs. Multiple input-multiple output (MIMO) systems are particularly vulnerable to input failures. In addition , the process behavior may change as the result of changes in operating conditions, different operating modes (e.g. startup or circling), and variations such as exchanger fouling and catalyst deactivation . Control laws developed from a linearized process model are generally valid over a limited region around the design point. As the process moves away from the design conditions, the controller performance may deteriorate and become unacceptable.
REFERENCES Basila, M. R. , A. Ginar , and G . Stefanek, 1990. A Model-Object Based Supervisory Expert System for Fault Tolerant Chemical Reactor Control Comput . Chem . Engng. , 14 , 551-560. Basila, M. R. and A. GlOar, 1992. Reliable Control of a Chemical Reactor with a Supervisory KBS Pm::. Srd IFA C Symp. D YCORD 92, College Park, MD 333-338. Kendra, S. J. and A. GlOar , 1991. On the Relation Between Model Reduction and Robust Control System Design. In Proc . 1991 A ICHE Annual Meeting, San Francisco Ca. Kendra, S. J ., 1992. Robust Multivariable Controller Design In Intelligtmt Control By Knowledge Based Systems. Ph.D . Thesis, Dlinois Institute of Technology.
MDBECS is capable of automatically identifying the nei!d to restruc-
ture the control algorithm and then developing and implementing the necessary corrective action . Consequently, the control system is never allowed to deviate far from the maximum level of con troller performance. In restructuring , the first step is to identify the nei!d for restructuring. The KBS continuously monitors the process operating conditions and determines if persistent dist urbances or a request for new operating conditions have forced the nei!d for redevelopment of a new process model. Another means of triggering the restructuring process is the detection of a primary sensor malfunction . The next steps in the restructuring process are to formulate a new control law and block configuration . Finally, the new configuration must be implemented in the control system . The reasoning and actions of the control en ginei!r to develop a system configuration are largely procedural . Consequently. the KBSs dei!p knowledge concerning control law design and system configuration are incorporated into methods that are called by the restructuring rules . Using a list of available process sensors, MDBECS creates a linearized process model based upon the current operating conditions . performs a model reduction ( Kendra. 1991 ). and then formulates the new Hoc control law from the reduced order model. These actions are performed by functions in the application program and tasks that operate on a separate workstation. The resultant control law is expressed as a discrete state-space representation .
376