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Computer-Aided tuning and Diagnosing Based on Personal Computers Usage
Copyright @ IFAC Intelligent Components IIld Instruments for Control Applications, Budapest, Hungary, 1994
COMPUTER-AIDED TUNING AND DIAGNOSING BASED ON PERSONAL COMPUTERS USAGE lB. Y ADYKIN* and E.K. KORNOUSHENKO· • Institute ofCoDtroJ Sciences, Profsouyuzt1aya, 65, J17806, Moscow, Russia
AhIIract. Wide-rIIlge usage of the advanced controllers are changing the views concerning technical service system assignment (a tuning. diagnosing. reconfigurating). These service tasks could be solved by means of reference model principle and expert systems in Computer-Aided Tuning and Diagnosing System (CATDS). This paper treates the design principles, architectural features and ways of CATDS realization on PC. The main requirements which must be fulfilled by modem CATDS are specified and the approach to design ofCATDS depending on their applications and "intelligence" level is proposed. Key words. Automatized controller tuning. redundancy, intelligent machines. signal processing. failure detection.
are based on the use of analytical redundancy contained in mathematical representation of the system under supervision (the hardware redundancy is awkward and in many cases expensive). The use of analytical redundancy for diagnosing has been considered by many authors and various diagnostic approaches have been proposed (see Frank (1990) for survey). On-line fault detection and isolation in a large-scale control system is a very hard problem . from the mathematical point of view.
1. INTRODUCTION
Now the modern personal computers provide a good opportunity to integrate control and technical service functions in intelligent distributed control systems (Astrom, 1987; Keviczky ct aI, 1978; Navarro ct aI, 1990; Tsypkin and Avedian, 1985). It is obvious that computational procedures designing by means of using parallel processing and background processing allows to gain the efficiency of an application of such systems. Since hardware and software of modern distributed control systems get more sophisticated, the significance of maintenance operation grows. Controller tuning is the complex, difficult and expensive process even in case of PlO controllers, because it requires high skill and deep knowledge. There is an objective trend towards increasing the range of industrial controllers tuning and developing more stringent requirements to the tuning quality. That is favored by the trend towards the optimization of the process characteristics, multiple modes of technological rules, introduction of flexible manufacturing systems, and more strict technological standards due to the ecological safety requirements.
Modern control systems are characterized by some peculiarities making difficult the implementation of the known methods for on-line fault detection and isolation: - the ramified structure with a large number of units and interconnections and with a small number of accessible points to be used for sampling information on system functioning; - the presence of strongly nonlinear units as well as nonstationary and (in some cases) logical units; - the presence of both unobservable disturbances and unit approximate models of units. The direct implementation of known diagnostic methods for such system may have a small effect in practical situations. Therefore, developing new methods suitable to diagnose systems with above mentioned peculiarities is a very urgent problem.
Modern control systems with reinforced requirements for their functioning (such as CATDS) are designed using sufficiently exact mathematical models of their elements. It is due to the availability of such models that these systems may be diagnosed using subtle analytical methods with considering latest results from control theory and system analysis. Analytical diagnostic methods
The creation of CATDS would not be possible, but for power computational hardware and software due to PC development. The commercialization of PC has permitted a convenient interface with an operator, namely, a tuner on the basis of computer 163
graphics. It also allows the data archiving for tuning and provides the utilization of a simulation modelling methods for better tuning forecasting. The main fields of CATDS applications are shown in Fig. 1.
the required model construction is executed by some method (Goodwin and Sin, 1984). After the determination of the mathematical model of the plant the controller parameters can be evaluated by the following methods: 1. Euristic procedures of the Nickols-Ziegler rule
COMPlITER AIDED TIJNING AND
type (Ziegler and Nickols, 1942).
DIAGNOSTIC SYSTEMS
2. A direct synthesis of a controller by means of CATS (Jamshidi and Herget, 1985). 3. Adaptive procedures for the control loop adjustment with the reference model (Horn, 1988; Yadykin, 1989). 4. Neural network procedures (Astrom, 1987). 5. Fuzzy procedures (Kwok ct al., 1990)
Automated operator position for controiioop adjuster
3. THE MAIN REQUIREMENTS FOR CATDS
Fig. 1. The main fields of CATDS application
Let us specify the main requirements to be satisfied with CATDS : - CATDS must operate, as a rule, in a real time and have a short tuning time for each variant of tuning; - the requirements to the tuner-operator skill must be minimal; - CATDS must be friendly with a tuner, i.e., propose to him alternative solutions, assist him in seeking the optimal tuning variant with taking into account his personal preferences; - CATDS is to be preferably used in the mode of the normal plant operation with a controller, however it should permit to use small-amplitude test signals, as well as to solve the tuning problems by stages; - CATDS is to be preferably used to determine the degree of tuning of a control loop; - CATDS must allow the archiving of tuning variants with common database for diagnostics and tuning; - CATDS must allow to comparison of various variants of tuning in a convenient (graphical) form.
2. PRINCIPLES OF CATDS DESIGN In case of a manual controller tuning under varying conditions the tuner seeks to use the variation of controller tuning to provide similarity between the closed loop tuning response and some response realizing the desired performance of the control system. If the mathematical models of the plant and other control loop elements are known, then it is possible to obtain full mathematical description of the closed control loop in the time or frequency domain corresponding to the transition process. The obtained description corresponds to an implicit reference model (IRM), and the set of parameters of that description determines assigned IRM (Yadykin, 1989). In the case of an automatic tuning the computer varies these parameters in order that to approximate the closed loop operator to the reference model operator. In Computer-Aided Tuning System (CATS) the tuner is informed about preferable tunings and it makes a final decision. Therefore, the problem of controller tuning is the basic one among the problems of the parametric adaptive control with an implicit or explicit reference model. The solution of this problem is well known in the adaptive control theory. The solution s of this problem may be obtained on the basis of a direct or indirect adaptive control using search or nonsearch methods (Astrom, 1987; Goodwin and Sin, 1984; Wiemer ct al., 1989; Krasovsky, 1987; Rotach ct aI. , 1984). In most cases existing CATS are based on the above principle, excluding expert and neural network CATS (AstrOID, 1987). The indirect control principle is realized with the use of parametric identification procedures to determine the mathematical model of the plant. It is usually assumed that the structural identification stage for
Let us consider the main requirements for the software package of the workstation on the PC base. It is preferable to use multiple windows into process simultaneously. Each window should be separate, might be animated and should correspond to some part of the control loop. The software package must support real timelhistorical trend of process and XY plots on line changing span, tag, scales and display rates. It should support the Windows Data Dynamic Exchange client/server interface, dynamically links real time data to the program such as spreadsheets, databases and statistical packages. It is preferable to use AUTO-cwR like windows display builder. It let user to create process diagrams, dynamic attributes and key macro functions. The software package must
164
provide on-screen alarm summary, customized alarm groups, alarm log storage. The conditional historic classification of CATDSs is shown in Fig. 2. Many of the above requirements are inherent in modem intelligent control systems as well, moreover, some important CATDS function can be realized in them as embedded firmware subsystems. Multicriterial tuning system optimization Structural identification of a plant. Multivariant tuning. Fault diagnosing. Control loop reconfiguration. Tuning expert systems. Tuning neural systems. MIMO plant tuning. Forecasting of before emergency states. Exiting condition estimation. Tuning degree estimation. Fuzzy tuning.
2. The problem of diagnosing control system is in a certain sense secondary being the main problem to provide the control quality required. Hence, a minimal number of additional accessible points needed for diagnostic purposes only should be required. 3. Diagnostic inferences should be developed very quickly having prevented a system to transfer into an accident state.
-
IV gen eration
m. gen eration
SISO plant tuning. Multiple test signals. Euristic tuning algorithms. Parametric identification of a plant.
IT gen eration
Tuning of simple controllers and compensators. Euristic diagnostic algorithms. Nickols-Ziegler tuning rule.
I gen eration
Fig. 2. Four generations of CATDS
4. FAULT DIAGNOSIS FOR CATDS The computer pertaining to CATDS introduces the opportunities to diagnose the control system as a whole as well as its elements using information to be treated during the control process. It is obvious that to attain and to maintain over time the required performance of industrial control system is undoubtedly a very important practical problem and its complete solution affects economic efficiency of system being considered. As far as the system's accident has occured, the expenditures on recovering of system performance may essentially exceed the ones needed for timely diagnosing. It should be noted that diagnosing process has some peculiarities one has to take into account before developing diagnostic procedures for CATDS. These peculiarities are as follows . 1. A fault having appeared in control system may change a system functioning regime whence it follows that unit models (linearized, less order, etc.) having been used for control purposes are unapplicable for diagnostics. Thus, effective diagnostic algorithms should be based on unit diagnostic models (may be nonlinear) tolerated to large deviations of their parameters.
The known diagnostic methods may be bad suitable for diagnosing CATDS due to a disregard of the requirements 1-2. The approach proposed in Komoushenko and Gerasimov (1990) seems to be more suitable in this case. This approach is based on a feedforward and/or backward recalculation of observable signals of the control system to inputs and outputs of its internal units and an analysis of compatibility of unit input-output representations obtained with their diagnostic models. After drawing the observable input and output signals of CATDS to inputs and outputs of some internal unit in CATDS, we have the estimated input-output representations of this unit. Using such presentation, ordinary identification to estimate the unit diagnostic model parameters is to be done. The diagnostic model of a unit is its extended parameterized mathematical model such that the adtnissible fault appearence implies the deviations of some coordinates in its parameter vector. The deviations may be either constant or varying over time. If CATDS is faulty, some unit representations obtained will be "incorrect" due to the fact that some observable signals were drawn along the ways contained the faulty units. In these situations the least squares solution residual norm for such units will be much more in comparison with this norm in fault free case. Obviously, the parameter estimates obtained for these units will have no common with their true parameter values. The diagnostic models of the units in CATDS are assumed to be the linear and bilinear either stationary or nonstationary systems, nonlinear static systems, logical systems, etc. Having been developed the applicable deconvolution procedures for these systems (Kornoushenko, 1992; Kornoushenko and Gerasimov, 1994) and keeping in mind that the corresponding modelling procedures are obvious, fault detection and isolation in CATDS to be composed of these "basic" units is no difficult problem. Really, using the observable signal drawing, one is able to turn an unobservable behavior of internal units into the "observable" one. The defect of an approach considered is the same as in any parameter estimation method: its performances deteriorate with occurring uncertainties in the unit mathematical models. In other words, it has no robust properties. It is the
165
6. CONCLUSION
price that we have to paid for possibility to diagnose large-scale control systems (such as CAlDS) with suffi~ient completeness.
The advent of PC and PC-based workstation in the field of industrial automation has permitted the development of automated tuning and diagnosing systems for industrial controllers. Such systems can be installed on lap-top PC as a part of standard subsystems of tuning degree monitoring and diagnostics for operator and process engineer station in modem distributed control systems. CATS based on ADACOM solves the controller tuning problems via the principle of adaptive control with reference model. The utilization of the MS DOS includes the entry of the plant 110 data into PC and determination of controller tuning by some methods depending of preference of the operator-tuner. The CATS ADACOM operation experience has shown its high efficiency especially for complex controller tuning for industrial MIMO plants.
5. PURPOSE AND STRUCTURE OF CATS ADACOM CATS ADACOM has been developed in Institute of Control Sciences and the fino "Tunex" Ltd to automatize the tuning of industrial control systems and in CAD DSC problems. The considered CAD allows: - specification of requirements for obtaining the desired dynamic characteristics of a tuned controller; - centralized control of tuning accuracy indexes of control loops; - determination, mapping and registration of the dynamical characteristics of control plant; - generation of test signals; multivariant tuning of PIO-controller parameters in order to use them as recommendation for an operator-tuner; - automatic tuning of microprocessor of the PlO laws family having a supervisor parametric control of the tuning parameters; - interaction with operator-tuner.
7. REFERENCES Astrom, K.l (1987). Adaptive feedback control. Proc.IEEE, 75, 185-217. Frank, P.M. (1990). Fault diagnosis in dynamic systems using analytical and knowledge based redundancy - A survey and some new results. Automatics, 26, 459-474. Goodwin, G.C., and K.S. Sin (1984). Adaptive filtering prediction and control. Prentice-Hall, Englewwood Cliffs. Horn, B.C. (1988). Optimal self-tuning of PIOcontrollers. In: Proc. ACC, pp. 2362-2367. Atlanta. Jarnshidi, M., and C.l Herget (Eds.) (1985).
The system is realized on the basis of the ADACOM application software package for IBM PC which includes the following program modules: identification of the plant structure, parameters and disturbances; evaluation of multivariant tuning controller parameters; estimation of a tuning degree; assignment of the control loop reference model; dispatcher; expert system; data archive; computer graphics; support programs (see Fig. 3). ADACOM application software package: 3000 operators, 356 Kb RAM, MS DOS, written in Fortran-77 and Pascal.
I I Identification
I
MONITOR
I Twtingwith reference model
I I
archives
I
Expert twting
Reference model I)1Ithcsis
Comparable analysis oftwting
Reference model library
I Data
Computer-aided control systems
I Control plant simulation
engineering.
North Holland, Amsterdam-New York-Oxford. Keviczky, L., 1. Hetthessy, M. Hilger, and 1. Kolostory (1978). Self-tuning adaptive control of cement raw material blending. Automatiea, 14, 1-8. Komoushenko, E.K. (1992). Input signal recovery from stored data in discrete nonstationary linear systems. Autom. Remote Control, 53, 18521862. Komoushenko, E.K., and V.V. Gerasimov (1990). Diagnostics of large-scale dynamic systems on the basis of finite records of their behavior. In:
Proc. 7-tb Intem. Conf. on Diagnostics, pp. 477-484. Helsinki.
Technical
Komoushenko, E.K., and V.V. Gerasimov (1994). The least squares deconvolution procedures for and bilinear discrete-time both linear nonstationary systems. To be published in
Fig. 3. Structural scheme of ADACOM
Autom. Remote Control, 55. Krasovsky, A.A. (Ed.) (1987). Reference book on automatic control theory. Nauka, Moscow [in Russian) .
166
Yadykin, LB. (1989). Principles of construction, architecture and software of the automatic tuning systems of industrial controllers. In: Computer Facilities. Systems. Control, pp. 25-36. Int. Center of Science and techn. inf. and Inf. Center on Tech. transfer., Moscow-Sofia. Yadykin, LB., and E .K. Komoushenko (1990). Integrated Intelligent Control Systems Conceptual Approach. In: Proc. JJ-th !FAC World Congress, Vol. 8, pp. 152-157. Pergamon Press, London. Ziegler, lG., and N.B. Nichols (1942). Optimum settings for automatic controllers. Trans. ASME, 64, 759-768.
Kwok, D.P., P. Tarn, C.K. Li, and P. Wang (1990). Linguistic PID controllers. In: Proc. JJ-th !FAC World Congress, Vol. 9, pp. 192-197. Pergamon Press, London. Navarro I.L., P. Albertos, M. Martinez, and F. Morant (1990). Intelligent industrial control. In: Proc. JJ-th IFAC World Congress, Vol. 10, pp. 87- 92. Pergamon Press, London. Rotach, V.Ya., V.F. Kuzishev, A.S. Klujev et al . (1984) . Automation of the tuning of control
systems. Energoatomizdat, Moscow [in Russian]. Tsypkin, Ya.Z., and A.D. Avedyan (1985). Discrete adaptive control systems for detenninistic plants. In: Results of Sciences. Cybemetics, pp. 45-77. VINITI publ., Moscow [in Russian] . Wiemer, P., G. Olejua-Torres, and H. Unbehauen (1988). A robust adaptive controller for systems with arbitrary zeros. In: Proc. Intem. Cont: Control-88, pp. 598- 603 . Oxford.
167
COMPUTER-AIDED TUNING AND DIAGNOSING BASED ON PERSONAL COMPUTERS USAGE lB. Y ADYKIN* and E.K. KORNOUSHENKO· • Institute ofCoDtroJ Sciences, Profsouyuzt1aya, 65, J17806, Moscow, Russia
AhIIract. Wide-rIIlge usage of the advanced controllers are changing the views concerning technical service system assignment (a tuning. diagnosing. reconfigurating). These service tasks could be solved by means of reference model principle and expert systems in Computer-Aided Tuning and Diagnosing System (CATDS). This paper treates the design principles, architectural features and ways of CATDS realization on PC. The main requirements which must be fulfilled by modem CATDS are specified and the approach to design ofCATDS depending on their applications and "intelligence" level is proposed. Key words. Automatized controller tuning. redundancy, intelligent machines. signal processing. failure detection.
are based on the use of analytical redundancy contained in mathematical representation of the system under supervision (the hardware redundancy is awkward and in many cases expensive). The use of analytical redundancy for diagnosing has been considered by many authors and various diagnostic approaches have been proposed (see Frank (1990) for survey). On-line fault detection and isolation in a large-scale control system is a very hard problem . from the mathematical point of view.
1. INTRODUCTION
Now the modern personal computers provide a good opportunity to integrate control and technical service functions in intelligent distributed control systems (Astrom, 1987; Keviczky ct aI, 1978; Navarro ct aI, 1990; Tsypkin and Avedian, 1985). It is obvious that computational procedures designing by means of using parallel processing and background processing allows to gain the efficiency of an application of such systems. Since hardware and software of modern distributed control systems get more sophisticated, the significance of maintenance operation grows. Controller tuning is the complex, difficult and expensive process even in case of PlO controllers, because it requires high skill and deep knowledge. There is an objective trend towards increasing the range of industrial controllers tuning and developing more stringent requirements to the tuning quality. That is favored by the trend towards the optimization of the process characteristics, multiple modes of technological rules, introduction of flexible manufacturing systems, and more strict technological standards due to the ecological safety requirements.
Modern control systems are characterized by some peculiarities making difficult the implementation of the known methods for on-line fault detection and isolation: - the ramified structure with a large number of units and interconnections and with a small number of accessible points to be used for sampling information on system functioning; - the presence of strongly nonlinear units as well as nonstationary and (in some cases) logical units; - the presence of both unobservable disturbances and unit approximate models of units. The direct implementation of known diagnostic methods for such system may have a small effect in practical situations. Therefore, developing new methods suitable to diagnose systems with above mentioned peculiarities is a very urgent problem.
Modern control systems with reinforced requirements for their functioning (such as CATDS) are designed using sufficiently exact mathematical models of their elements. It is due to the availability of such models that these systems may be diagnosed using subtle analytical methods with considering latest results from control theory and system analysis. Analytical diagnostic methods
The creation of CATDS would not be possible, but for power computational hardware and software due to PC development. The commercialization of PC has permitted a convenient interface with an operator, namely, a tuner on the basis of computer 163
graphics. It also allows the data archiving for tuning and provides the utilization of a simulation modelling methods for better tuning forecasting. The main fields of CATDS applications are shown in Fig. 1.
the required model construction is executed by some method (Goodwin and Sin, 1984). After the determination of the mathematical model of the plant the controller parameters can be evaluated by the following methods: 1. Euristic procedures of the Nickols-Ziegler rule
COMPlITER AIDED TIJNING AND
type (Ziegler and Nickols, 1942).
DIAGNOSTIC SYSTEMS
2. A direct synthesis of a controller by means of CATS (Jamshidi and Herget, 1985). 3. Adaptive procedures for the control loop adjustment with the reference model (Horn, 1988; Yadykin, 1989). 4. Neural network procedures (Astrom, 1987). 5. Fuzzy procedures (Kwok ct al., 1990)
Automated operator position for controiioop adjuster
3. THE MAIN REQUIREMENTS FOR CATDS
Fig. 1. The main fields of CATDS application
Let us specify the main requirements to be satisfied with CATDS : - CATDS must operate, as a rule, in a real time and have a short tuning time for each variant of tuning; - the requirements to the tuner-operator skill must be minimal; - CATDS must be friendly with a tuner, i.e., propose to him alternative solutions, assist him in seeking the optimal tuning variant with taking into account his personal preferences; - CATDS is to be preferably used in the mode of the normal plant operation with a controller, however it should permit to use small-amplitude test signals, as well as to solve the tuning problems by stages; - CATDS is to be preferably used to determine the degree of tuning of a control loop; - CATDS must allow the archiving of tuning variants with common database for diagnostics and tuning; - CATDS must allow to comparison of various variants of tuning in a convenient (graphical) form.
2. PRINCIPLES OF CATDS DESIGN In case of a manual controller tuning under varying conditions the tuner seeks to use the variation of controller tuning to provide similarity between the closed loop tuning response and some response realizing the desired performance of the control system. If the mathematical models of the plant and other control loop elements are known, then it is possible to obtain full mathematical description of the closed control loop in the time or frequency domain corresponding to the transition process. The obtained description corresponds to an implicit reference model (IRM), and the set of parameters of that description determines assigned IRM (Yadykin, 1989). In the case of an automatic tuning the computer varies these parameters in order that to approximate the closed loop operator to the reference model operator. In Computer-Aided Tuning System (CATS) the tuner is informed about preferable tunings and it makes a final decision. Therefore, the problem of controller tuning is the basic one among the problems of the parametric adaptive control with an implicit or explicit reference model. The solution of this problem is well known in the adaptive control theory. The solution s of this problem may be obtained on the basis of a direct or indirect adaptive control using search or nonsearch methods (Astrom, 1987; Goodwin and Sin, 1984; Wiemer ct al., 1989; Krasovsky, 1987; Rotach ct aI. , 1984). In most cases existing CATS are based on the above principle, excluding expert and neural network CATS (AstrOID, 1987). The indirect control principle is realized with the use of parametric identification procedures to determine the mathematical model of the plant. It is usually assumed that the structural identification stage for
Let us consider the main requirements for the software package of the workstation on the PC base. It is preferable to use multiple windows into process simultaneously. Each window should be separate, might be animated and should correspond to some part of the control loop. The software package must support real timelhistorical trend of process and XY plots on line changing span, tag, scales and display rates. It should support the Windows Data Dynamic Exchange client/server interface, dynamically links real time data to the program such as spreadsheets, databases and statistical packages. It is preferable to use AUTO-cwR like windows display builder. It let user to create process diagrams, dynamic attributes and key macro functions. The software package must
164
provide on-screen alarm summary, customized alarm groups, alarm log storage. The conditional historic classification of CATDSs is shown in Fig. 2. Many of the above requirements are inherent in modem intelligent control systems as well, moreover, some important CATDS function can be realized in them as embedded firmware subsystems. Multicriterial tuning system optimization Structural identification of a plant. Multivariant tuning. Fault diagnosing. Control loop reconfiguration. Tuning expert systems. Tuning neural systems. MIMO plant tuning. Forecasting of before emergency states. Exiting condition estimation. Tuning degree estimation. Fuzzy tuning.
2. The problem of diagnosing control system is in a certain sense secondary being the main problem to provide the control quality required. Hence, a minimal number of additional accessible points needed for diagnostic purposes only should be required. 3. Diagnostic inferences should be developed very quickly having prevented a system to transfer into an accident state.
-
IV gen eration
m. gen eration
SISO plant tuning. Multiple test signals. Euristic tuning algorithms. Parametric identification of a plant.
IT gen eration
Tuning of simple controllers and compensators. Euristic diagnostic algorithms. Nickols-Ziegler tuning rule.
I gen eration
Fig. 2. Four generations of CATDS
4. FAULT DIAGNOSIS FOR CATDS The computer pertaining to CATDS introduces the opportunities to diagnose the control system as a whole as well as its elements using information to be treated during the control process. It is obvious that to attain and to maintain over time the required performance of industrial control system is undoubtedly a very important practical problem and its complete solution affects economic efficiency of system being considered. As far as the system's accident has occured, the expenditures on recovering of system performance may essentially exceed the ones needed for timely diagnosing. It should be noted that diagnosing process has some peculiarities one has to take into account before developing diagnostic procedures for CATDS. These peculiarities are as follows . 1. A fault having appeared in control system may change a system functioning regime whence it follows that unit models (linearized, less order, etc.) having been used for control purposes are unapplicable for diagnostics. Thus, effective diagnostic algorithms should be based on unit diagnostic models (may be nonlinear) tolerated to large deviations of their parameters.
The known diagnostic methods may be bad suitable for diagnosing CATDS due to a disregard of the requirements 1-2. The approach proposed in Komoushenko and Gerasimov (1990) seems to be more suitable in this case. This approach is based on a feedforward and/or backward recalculation of observable signals of the control system to inputs and outputs of its internal units and an analysis of compatibility of unit input-output representations obtained with their diagnostic models. After drawing the observable input and output signals of CATDS to inputs and outputs of some internal unit in CATDS, we have the estimated input-output representations of this unit. Using such presentation, ordinary identification to estimate the unit diagnostic model parameters is to be done. The diagnostic model of a unit is its extended parameterized mathematical model such that the adtnissible fault appearence implies the deviations of some coordinates in its parameter vector. The deviations may be either constant or varying over time. If CATDS is faulty, some unit representations obtained will be "incorrect" due to the fact that some observable signals were drawn along the ways contained the faulty units. In these situations the least squares solution residual norm for such units will be much more in comparison with this norm in fault free case. Obviously, the parameter estimates obtained for these units will have no common with their true parameter values. The diagnostic models of the units in CATDS are assumed to be the linear and bilinear either stationary or nonstationary systems, nonlinear static systems, logical systems, etc. Having been developed the applicable deconvolution procedures for these systems (Kornoushenko, 1992; Kornoushenko and Gerasimov, 1994) and keeping in mind that the corresponding modelling procedures are obvious, fault detection and isolation in CATDS to be composed of these "basic" units is no difficult problem. Really, using the observable signal drawing, one is able to turn an unobservable behavior of internal units into the "observable" one. The defect of an approach considered is the same as in any parameter estimation method: its performances deteriorate with occurring uncertainties in the unit mathematical models. In other words, it has no robust properties. It is the
165
6. CONCLUSION
price that we have to paid for possibility to diagnose large-scale control systems (such as CAlDS) with suffi~ient completeness.
The advent of PC and PC-based workstation in the field of industrial automation has permitted the development of automated tuning and diagnosing systems for industrial controllers. Such systems can be installed on lap-top PC as a part of standard subsystems of tuning degree monitoring and diagnostics for operator and process engineer station in modem distributed control systems. CATS based on ADACOM solves the controller tuning problems via the principle of adaptive control with reference model. The utilization of the MS DOS includes the entry of the plant 110 data into PC and determination of controller tuning by some methods depending of preference of the operator-tuner. The CATS ADACOM operation experience has shown its high efficiency especially for complex controller tuning for industrial MIMO plants.
5. PURPOSE AND STRUCTURE OF CATS ADACOM CATS ADACOM has been developed in Institute of Control Sciences and the fino "Tunex" Ltd to automatize the tuning of industrial control systems and in CAD DSC problems. The considered CAD allows: - specification of requirements for obtaining the desired dynamic characteristics of a tuned controller; - centralized control of tuning accuracy indexes of control loops; - determination, mapping and registration of the dynamical characteristics of control plant; - generation of test signals; multivariant tuning of PIO-controller parameters in order to use them as recommendation for an operator-tuner; - automatic tuning of microprocessor of the PlO laws family having a supervisor parametric control of the tuning parameters; - interaction with operator-tuner.
7. REFERENCES Astrom, K.l (1987). Adaptive feedback control. Proc.IEEE, 75, 185-217. Frank, P.M. (1990). Fault diagnosis in dynamic systems using analytical and knowledge based redundancy - A survey and some new results. Automatics, 26, 459-474. Goodwin, G.C., and K.S. Sin (1984). Adaptive filtering prediction and control. Prentice-Hall, Englewwood Cliffs. Horn, B.C. (1988). Optimal self-tuning of PIOcontrollers. In: Proc. ACC, pp. 2362-2367. Atlanta. Jarnshidi, M., and C.l Herget (Eds.) (1985).
The system is realized on the basis of the ADACOM application software package for IBM PC which includes the following program modules: identification of the plant structure, parameters and disturbances; evaluation of multivariant tuning controller parameters; estimation of a tuning degree; assignment of the control loop reference model; dispatcher; expert system; data archive; computer graphics; support programs (see Fig. 3). ADACOM application software package: 3000 operators, 356 Kb RAM, MS DOS, written in Fortran-77 and Pascal.
I I Identification
I
MONITOR
I Twtingwith reference model
I I
archives
I
Expert twting
Reference model I)1Ithcsis
Comparable analysis oftwting
Reference model library
I Data
Computer-aided control systems
I Control plant simulation
engineering.
North Holland, Amsterdam-New York-Oxford. Keviczky, L., 1. Hetthessy, M. Hilger, and 1. Kolostory (1978). Self-tuning adaptive control of cement raw material blending. Automatiea, 14, 1-8. Komoushenko, E.K. (1992). Input signal recovery from stored data in discrete nonstationary linear systems. Autom. Remote Control, 53, 18521862. Komoushenko, E.K., and V.V. Gerasimov (1990). Diagnostics of large-scale dynamic systems on the basis of finite records of their behavior. In:
Proc. 7-tb Intem. Conf. on Diagnostics, pp. 477-484. Helsinki.
Technical
Komoushenko, E.K., and V.V. Gerasimov (1994). The least squares deconvolution procedures for and bilinear discrete-time both linear nonstationary systems. To be published in
Fig. 3. Structural scheme of ADACOM
Autom. Remote Control, 55. Krasovsky, A.A. (Ed.) (1987). Reference book on automatic control theory. Nauka, Moscow [in Russian) .
166
Yadykin, LB. (1989). Principles of construction, architecture and software of the automatic tuning systems of industrial controllers. In: Computer Facilities. Systems. Control, pp. 25-36. Int. Center of Science and techn. inf. and Inf. Center on Tech. transfer., Moscow-Sofia. Yadykin, LB., and E .K. Komoushenko (1990). Integrated Intelligent Control Systems Conceptual Approach. In: Proc. JJ-th !FAC World Congress, Vol. 8, pp. 152-157. Pergamon Press, London. Ziegler, lG., and N.B. Nichols (1942). Optimum settings for automatic controllers. Trans. ASME, 64, 759-768.
Kwok, D.P., P. Tarn, C.K. Li, and P. Wang (1990). Linguistic PID controllers. In: Proc. JJ-th !FAC World Congress, Vol. 9, pp. 192-197. Pergamon Press, London. Navarro I.L., P. Albertos, M. Martinez, and F. Morant (1990). Intelligent industrial control. In: Proc. JJ-th IFAC World Congress, Vol. 10, pp. 87- 92. Pergamon Press, London. Rotach, V.Ya., V.F. Kuzishev, A.S. Klujev et al . (1984) . Automation of the tuning of control
systems. Energoatomizdat, Moscow [in Russian]. Tsypkin, Ya.Z., and A.D. Avedyan (1985). Discrete adaptive control systems for detenninistic plants. In: Results of Sciences. Cybemetics, pp. 45-77. VINITI publ., Moscow [in Russian] . Wiemer, P., G. Olejua-Torres, and H. Unbehauen (1988). A robust adaptive controller for systems with arbitrary zeros. In: Proc. Intem. Cont: Control-88, pp. 598- 603 . Oxford.
167