This paper describes the basic elements of an integrated computer control system for wastewater treatment plant operation and control. One of the key elements of this approach is the role played by simulation. The dynamic model of the wastewater treatment plant is critical in developing an optimal control strategy. Modelling of large-scale facilities is complicated by the fact that wastewater treatment plants are not only complex in their design but also in their operation. The simulation tool must be able to reflect process and operational details with some accuracy. Results on the application of the simulation tool to several large-scale scale facilities is discussed.<>
{"title":"Simulation: a key component in the development of an integrated computer-based approach to wastewater treatment plant control","authors":"G. Patry, I. Takics","doi":"10.1109/CCA.1994.381373","DOIUrl":"https://doi.org/10.1109/CCA.1994.381373","url":null,"abstract":"This paper describes the basic elements of an integrated computer control system for wastewater treatment plant operation and control. One of the key elements of this approach is the role played by simulation. The dynamic model of the wastewater treatment plant is critical in developing an optimal control strategy. Modelling of large-scale facilities is complicated by the fact that wastewater treatment plants are not only complex in their design but also in their operation. The simulation tool must be able to reflect process and operational details with some accuracy. Results on the application of the simulation tool to several large-scale scale facilities is discussed.<<ETX>>","PeriodicalId":173370,"journal":{"name":"1994 Proceedings of IEEE International Conference on Control and Applications","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116107409","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The paper presents a method for robust product or process design. The product parameters are so chosen that to minimise the performance characteristic's variance while keeping its mean value on a target. Models of the mean value and variance of the performance characteristic in mass production are given. Two examples are considered: choice of parameters of a Wheatstone bridge and of a band-pass filter.<>
{"title":"Computer aided quality improvement","authors":"I. Vuchkov, L. Boyadjieva","doi":"10.1109/CCA.1994.381335","DOIUrl":"https://doi.org/10.1109/CCA.1994.381335","url":null,"abstract":"The paper presents a method for robust product or process design. The product parameters are so chosen that to minimise the performance characteristic's variance while keeping its mean value on a target. Models of the mean value and variance of the performance characteristic in mass production are given. Two examples are considered: choice of parameters of a Wheatstone bridge and of a band-pass filter.<<ETX>>","PeriodicalId":173370,"journal":{"name":"1994 Proceedings of IEEE International Conference on Control and Applications","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121204821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The emergence of artificial neural networks has made it conducive to integrate fuzzy logic controllers and neural models for the development of adaptive fuzzy control systems. In this paper, the authors proposed an adaptive fuzzy-neural control scheme by integrating two neural network models with a basic fuzzy logic controller. Using the backpropagation algorithm the first neural network is trained as a plant emulator and the second neural network is used as a compensator for the basic fuzzy controller to improve its performance on-line. The function of the neural network plant emulator is to provide the correct error signal at the output of the neural fuzzy compensator without the need for any mathematical modeling of the plant. The difficulty of fine-tuning the scale factors and formulating the correct control rules in a basic fuzzy controller may be reduced using the proposed scheme. The scheme is applied to the temperature control of a water bath process. The performance of the adaptive fuzzy-neural controller is compared to the basic fuzzy logic controller and a conventional digital-PI controller under identical conditions of varying complexities in the process. The experimental results show that the adaptive fuzzy-neural control scheme is superior in performance than the other two controllers.<>
{"title":"Adaptive fuzzy-neuro control with application to a water bath process","authors":"M. Khalid, S. Omatu, R. Yusof","doi":"10.1109/CCA.1994.381231","DOIUrl":"https://doi.org/10.1109/CCA.1994.381231","url":null,"abstract":"The emergence of artificial neural networks has made it conducive to integrate fuzzy logic controllers and neural models for the development of adaptive fuzzy control systems. In this paper, the authors proposed an adaptive fuzzy-neural control scheme by integrating two neural network models with a basic fuzzy logic controller. Using the backpropagation algorithm the first neural network is trained as a plant emulator and the second neural network is used as a compensator for the basic fuzzy controller to improve its performance on-line. The function of the neural network plant emulator is to provide the correct error signal at the output of the neural fuzzy compensator without the need for any mathematical modeling of the plant. The difficulty of fine-tuning the scale factors and formulating the correct control rules in a basic fuzzy controller may be reduced using the proposed scheme. The scheme is applied to the temperature control of a water bath process. The performance of the adaptive fuzzy-neural controller is compared to the basic fuzzy logic controller and a conventional digital-PI controller under identical conditions of varying complexities in the process. The experimental results show that the adaptive fuzzy-neural control scheme is superior in performance than the other two controllers.<<ETX>>","PeriodicalId":173370,"journal":{"name":"1994 Proceedings of IEEE International Conference on Control and Applications","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124930256","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Previously very efficient mathematical models for the stepping motor have been developed. The particularity of these models is that they give good results even at high frequencies. Many control algorithms based on the proposed reference models have been developed and tested on stepping motors. The mechanical load is considered constant. The control robustness has never been analysed with this type of control method driving stepping motors. However, the robust control algorithms could permit the use of stepping motors in robotics. In this paper the robust control method for nonlinear systems (stepping motors with variable load) is proposed. The load variations are relatively important and the control robustness is obtained by limiting the global performances. The authors have realised only the first approach to the robust control. Very large nonlinearities of reference model don't permit classical robust control analysis and synthesis, then the control law is obtained by simulation methods. It is also possible to linearize the reference model and to use standard robust control methods but this approach disables the control at high frequencies. The reference model and the maximal torque control method (optimal control for the stepping motor) are presented, leading to definitions of the stepping motor safety range and safety factor. Then, from the safety range definition, a robustness criterion is obtained. This criterion is based on the stator phases commutation positions. Finally the numerical simulation of the stepping motor control with important mechanical load variations is given.<>
{"title":"Nonlinear system robust control-application on a stepping motor","authors":"D. Bertaux, P. Bruniaux, V. Koncar, D. Pinchon","doi":"10.1109/CCA.1994.381225","DOIUrl":"https://doi.org/10.1109/CCA.1994.381225","url":null,"abstract":"Previously very efficient mathematical models for the stepping motor have been developed. The particularity of these models is that they give good results even at high frequencies. Many control algorithms based on the proposed reference models have been developed and tested on stepping motors. The mechanical load is considered constant. The control robustness has never been analysed with this type of control method driving stepping motors. However, the robust control algorithms could permit the use of stepping motors in robotics. In this paper the robust control method for nonlinear systems (stepping motors with variable load) is proposed. The load variations are relatively important and the control robustness is obtained by limiting the global performances. The authors have realised only the first approach to the robust control. Very large nonlinearities of reference model don't permit classical robust control analysis and synthesis, then the control law is obtained by simulation methods. It is also possible to linearize the reference model and to use standard robust control methods but this approach disables the control at high frequencies. The reference model and the maximal torque control method (optimal control for the stepping motor) are presented, leading to definitions of the stepping motor safety range and safety factor. Then, from the safety range definition, a robustness criterion is obtained. This criterion is based on the stator phases commutation positions. Finally the numerical simulation of the stepping motor control with important mechanical load variations is given.<<ETX>>","PeriodicalId":173370,"journal":{"name":"1994 Proceedings of IEEE International Conference on Control and Applications","volume":"514 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125639350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, a robust multivariable two matrix degree of freedom feedback structure is designed for an evaporator example. The evaporator is described by a 2/spl times/2 transfer matrix having uncertain time-delays and gains. The desired tracking properties of the closed loop system are a priori given, and these are to be achieved despite the large parametric uncertainty. The third MIMO quantitative feedback technique (QFT) of Horowitz is used for the design. The obtained results are verified in both frequency and time domains through simulations, and found to be acceptable over the range of uncertainty considered.<>
{"title":"Evaporator control design: a quantitative feedback theory approach","authors":"R. Kundergi, P. Nataraj","doi":"10.1109/CCA.1994.381299","DOIUrl":"https://doi.org/10.1109/CCA.1994.381299","url":null,"abstract":"In this paper, a robust multivariable two matrix degree of freedom feedback structure is designed for an evaporator example. The evaporator is described by a 2/spl times/2 transfer matrix having uncertain time-delays and gains. The desired tracking properties of the closed loop system are a priori given, and these are to be achieved despite the large parametric uncertainty. The third MIMO quantitative feedback technique (QFT) of Horowitz is used for the design. The obtained results are verified in both frequency and time domains through simulations, and found to be acceptable over the range of uncertainty considered.<<ETX>>","PeriodicalId":173370,"journal":{"name":"1994 Proceedings of IEEE International Conference on Control and Applications","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122470532","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The paper describes the application of self-tuning concepts to the problem of extremum control for the in-vehicle calibration of a spark ignition engine. Various implementations of least squares estimation are considered and used in the work. It is shown that improved performance is obtained by combining an angle of peak pressure regulator with the self-tuning extremum controller.<>
{"title":"Self-tuning control applied to the in-vehicle calibration of a spark ignition engine","authors":"R. Dorey, G. Stuart","doi":"10.1109/CCA.1994.381239","DOIUrl":"https://doi.org/10.1109/CCA.1994.381239","url":null,"abstract":"The paper describes the application of self-tuning concepts to the problem of extremum control for the in-vehicle calibration of a spark ignition engine. Various implementations of least squares estimation are considered and used in the work. It is shown that improved performance is obtained by combining an angle of peak pressure regulator with the self-tuning extremum controller.<<ETX>>","PeriodicalId":173370,"journal":{"name":"1994 Proceedings of IEEE International Conference on Control and Applications","volume":"2009 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131526402","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
For good control of activated sludge plants information on the degradation kinetics of readily biodegradable matter (RBM) is necessary, because RBM greatly affects the oxygen consumption of the activated sludge. The concentration of RBM can be expressed by the short-term biochemical oxygen demand (BOD/sub st/). In this paper batch experiments are used in which samples of wastewater and activated sludge are mixed in a lab scale aeration tank while the respiration rate is monitored until all the RBM is oxidized. Since BOD/sub st/ is simply the integral of the respiration rates at successive time instants, BOD/sub st/ is calculated from time series of respiration rates. In this way kinetic models can be obtained from dynamic measurement of respiration rate only. The aim of this paper is to provide and analyse the conditions under which the equivalence of the relationship between BOD/sub st/ and respiration rate obtained from chemostat or batch experiments holds. Furthermore, the problem of obtaining an appropriate model structure with associated model parameters from given respiration rate data is investigated and evaluated for this type of biological systems. Four different classes of kinetic models are identified from several batch experiments. The data sets consist of only respiration rate as a function of time. It is concluded that batch respirometric experiments can be employed for kinetic model identification if sludge concentration and initial substrate concentration are carefully chosen. The occurrence of slowly degradable components hinders the identification of degradation kinetics of the dominant component.<>
{"title":"Identification of wastewater biodegradation kinetics","authors":"H. Spanjers, K. Keesman","doi":"10.1109/CCA.1994.381375","DOIUrl":"https://doi.org/10.1109/CCA.1994.381375","url":null,"abstract":"For good control of activated sludge plants information on the degradation kinetics of readily biodegradable matter (RBM) is necessary, because RBM greatly affects the oxygen consumption of the activated sludge. The concentration of RBM can be expressed by the short-term biochemical oxygen demand (BOD/sub st/). In this paper batch experiments are used in which samples of wastewater and activated sludge are mixed in a lab scale aeration tank while the respiration rate is monitored until all the RBM is oxidized. Since BOD/sub st/ is simply the integral of the respiration rates at successive time instants, BOD/sub st/ is calculated from time series of respiration rates. In this way kinetic models can be obtained from dynamic measurement of respiration rate only. The aim of this paper is to provide and analyse the conditions under which the equivalence of the relationship between BOD/sub st/ and respiration rate obtained from chemostat or batch experiments holds. Furthermore, the problem of obtaining an appropriate model structure with associated model parameters from given respiration rate data is investigated and evaluated for this type of biological systems. Four different classes of kinetic models are identified from several batch experiments. The data sets consist of only respiration rate as a function of time. It is concluded that batch respirometric experiments can be employed for kinetic model identification if sludge concentration and initial substrate concentration are carefully chosen. The occurrence of slowly degradable components hinders the identification of degradation kinetics of the dominant component.<<ETX>>","PeriodicalId":173370,"journal":{"name":"1994 Proceedings of IEEE International Conference on Control and Applications","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133301224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Presents the application of a neural network based model predictive control scheme to the control of pH in a laboratory-scale neutralization reactor. The authors use a feedforward neural network as the nonlinear prediction model in an extended DMC-algorithm to control the pH-value. The training data set for the neural network was obtained from measurements of the inputs and outputs of the real plant operating with a PI controller. Thus, no a priori information about the plant and no special operating conditions of the plant were needed to design the controller. The training algorithm used is a combination of an adaptive backpropagation algorithm which tunes the connection weights with a genetic algorithm to modify the slopes of the activation function of each neuron. This combination turned out to be very robust against getting caught in local minima and it is very insensitive to the initial settings of the weights of the network. Experimental results show that the resulting control algorithm performs much better than the conventional PI controller which was used for the generation of the training data set.<>
{"title":"Neural network based model predictive control of a continuous neutralization reactor","authors":"A. Draeger, H. Ranke, Sebastian Engell","doi":"10.1109/CCA.1994.381408","DOIUrl":"https://doi.org/10.1109/CCA.1994.381408","url":null,"abstract":"Presents the application of a neural network based model predictive control scheme to the control of pH in a laboratory-scale neutralization reactor. The authors use a feedforward neural network as the nonlinear prediction model in an extended DMC-algorithm to control the pH-value. The training data set for the neural network was obtained from measurements of the inputs and outputs of the real plant operating with a PI controller. Thus, no a priori information about the plant and no special operating conditions of the plant were needed to design the controller. The training algorithm used is a combination of an adaptive backpropagation algorithm which tunes the connection weights with a genetic algorithm to modify the slopes of the activation function of each neuron. This combination turned out to be very robust against getting caught in local minima and it is very insensitive to the initial settings of the weights of the network. Experimental results show that the resulting control algorithm performs much better than the conventional PI controller which was used for the generation of the training data set.<<ETX>>","PeriodicalId":173370,"journal":{"name":"1994 Proceedings of IEEE International Conference on Control and Applications","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133441049","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The paper considers the problem of decomposition of multivariable fuzzy control systems. Some definitions, regarding the assignment of variables to subsystems, are given. A decomposition method, decreasing the strength and the number of interactional fuzzy relations among subsystems, is developed. The method is applied to a power system with regard to its load-frequency control, and the results are analysed.<>
{"title":"Decomposed fuzzy control of power systems","authors":"A. Gegov, H.-J. Hern","doi":"10.1109/CCA.1994.381391","DOIUrl":"https://doi.org/10.1109/CCA.1994.381391","url":null,"abstract":"The paper considers the problem of decomposition of multivariable fuzzy control systems. Some definitions, regarding the assignment of variables to subsystems, are given. A decomposition method, decreasing the strength and the number of interactional fuzzy relations among subsystems, is developed. The method is applied to a power system with regard to its load-frequency control, and the results are analysed.<<ETX>>","PeriodicalId":173370,"journal":{"name":"1994 Proceedings of IEEE International Conference on Control and Applications","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131914513","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper presents a method of designing the output feedback gain which place the closed-loop poles of a given interval system inside some region. The output feedback gain can be determined in a very simple way. A numerical example illustrates the proposed procedure.<>
{"title":"Robust pole assignment for interval systems using output feedback","authors":"O. Ismail, B. Bandyopadhyay","doi":"10.1109/CCA.1994.381479","DOIUrl":"https://doi.org/10.1109/CCA.1994.381479","url":null,"abstract":"This paper presents a method of designing the output feedback gain which place the closed-loop poles of a given interval system inside some region. The output feedback gain can be determined in a very simple way. A numerical example illustrates the proposed procedure.<<ETX>>","PeriodicalId":173370,"journal":{"name":"1994 Proceedings of IEEE International Conference on Control and Applications","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134125716","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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