A systematic approach to the design of VSC systems has been given. Using the hyperstability theory, the stability of the global system has been established and the existence of the sliding modes. Simple control laws can be used and high speed of response is obtainable by forcing the system with maximum allowable signals, e.g. by means of relay or unit-vector laws. A simple way to gain confidence with synthesis is an extensive and accurate simulation before implementing the control system. To this end it is very useful to realise effective CAD packages for variable structure control system design.
{"title":"The hyperstability approach to VSCS design","authors":"A. Balestrino, M. Innocenti","doi":"10.1049/PBCE040E_CH9","DOIUrl":"https://doi.org/10.1049/PBCE040E_CH9","url":null,"abstract":"A systematic approach to the design of VSC systems has been given. Using the hyperstability theory, the stability of the global system has been established and the existence of the sliding modes. Simple control laws can be used and high speed of response is obtainable by forcing the system with maximum allowable signals, e.g. by means of relay or unit-vector laws. A simple way to gain confidence with synthesis is an extensive and accurate simulation before implementing the control system. To this end it is very useful to realise effective CAD packages for variable structure control system design.","PeriodicalId":290911,"journal":{"name":"IEE control engineering series","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121097514","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 chapter gives an introduction to expert systems as they have been and are likely to be applied to the domain of process control. The evolutionary context of expert systems is discussed, so as to pick out those features of process control expert systems which make them different from expert systems in other domains and artificial intelligence in general. This chapter on expert systems for process control has taken an evolutionary view of the field, examining how expert systems have evolved from their roots in artificial intelligence to become something quite distinct from the medical expert systems from which they sprang. However, they are not so separate that there is no more to be learnt from the applications in the medical domain. While the experts in process control and medicine may not discuss the intricacies of their knowledge-based systems, the designers and academics remain in communication.
{"title":"Introduction to knowledge-based systems for process control","authors":"H. Efstathiou","doi":"10.1049/PBCE044E_CH2","DOIUrl":"https://doi.org/10.1049/PBCE044E_CH2","url":null,"abstract":"This chapter gives an introduction to expert systems as they have been and are likely to be applied to the domain of process control. The evolutionary context of expert systems is discussed, so as to pick out those features of process control expert systems which make them different from expert systems in other domains and artificial intelligence in general. This chapter on expert systems for process control has taken an evolutionary view of the field, examining how expert systems have evolved from their roots in artificial intelligence to become something quite distinct from the medical expert systems from which they sprang. However, they are not so separate that there is no more to be learnt from the applications in the medical domain. While the experts in process control and medicine may not discuss the intricacies of their knowledge-based systems, the designers and academics remain in communication.","PeriodicalId":290911,"journal":{"name":"IEE control engineering series","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121537276","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}
Neural networks have become a very fashionable area of research with a range of potential applications that spans AI, engineering and science. All the applications are dependent upon training the network with illustrative examples and this involves adjusting the weights which define the strength of connection between the neurons in the network. This can often be interpreted as a system identification problem with the advantage that many of the ideas and results from estimation theory can be applied to provide insight into the neural network problem irrespective of the specific application.
{"title":"Neural networks and system identification","authors":"S. Billings, S. Chen","doi":"10.1049/PBCE053E_CH11","DOIUrl":"https://doi.org/10.1049/PBCE053E_CH11","url":null,"abstract":"Neural networks have become a very fashionable area of research with a range of potential applications that spans AI, engineering and science. All the applications are dependent upon training the network with illustrative examples and this involves adjusting the weights which define the strength of connection between the neurons in the network. This can often be interpreted as a system identification problem with the advantage that many of the ideas and results from estimation theory can be applied to provide insight into the neural network problem irrespective of the specific application.","PeriodicalId":290911,"journal":{"name":"IEE control engineering series","volume":"128 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132372946","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 chapter describes the application of COGSYS to a small gas processing plant at the British Gas Midlands Research Station, Solihull. COGSYS (for COGnitive SYStem), is a new real-time expert system which is particularly suited to industrial process control. It was specified and developed within a collaborative group of some 35 major companies and has been made commercially available through the formation of a new company, COGSYS Ltd.
{"title":"Application of COGSYS to a small gas-processing plant","authors":"T. Williams","doi":"10.1049/PBCE044E_CH16","DOIUrl":"https://doi.org/10.1049/PBCE044E_CH16","url":null,"abstract":"This chapter describes the application of COGSYS to a small gas processing plant at the British Gas Midlands Research Station, Solihull. COGSYS (for COGnitive SYStem), is a new real-time expert system which is particularly suited to industrial process control. It was specified and developed within a collaborative group of some 35 major companies and has been made commercially available through the formation of a new company, COGSYS Ltd.","PeriodicalId":290911,"journal":{"name":"IEE control engineering series","volume":"190 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131587921","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 computation of H∞ and LQG optimal controllers is considered for process control applications. There are many process control problems where significant uncertanties exist in the system models which therefore require robust control designs. A simple solution for the optimal H∞ robust design problem is considered and the relationship to super-optimal solutions is discussed. For special types of weighted plant model the main H∞ equations to be solved are shown to be decoupled so that the calculations are similar to the scalar case. This situation is shown to arise when a mixed-sensitivity cost-function is selected and the plant has an interaction structure typical of many hot or cold rolling mill gauge control applications. A simplified design procedure is also introduced which further simplifies the calculations of the optimal controller and enables standard eigenvector/eigenvalue algorithms to be employed in solving the equations. The procedures are illustrated using a multivariable metal processing control design example.
{"title":"Design of LQG and H∞ multivariable robust controllers for process control applications","authors":"M. Grimble","doi":"10.1049/PBCE044E_CH18","DOIUrl":"https://doi.org/10.1049/PBCE044E_CH18","url":null,"abstract":"The computation of H∞ and LQG optimal controllers is considered for process control applications. There are many process control problems where significant uncertanties exist in the system models which therefore require robust control designs. A simple solution for the optimal H∞ robust design problem is considered and the relationship to super-optimal solutions is discussed. For special types of weighted plant model the main H∞ equations to be solved are shown to be decoupled so that the calculations are similar to the scalar case. This situation is shown to arise when a mixed-sensitivity cost-function is selected and the plant has an interaction structure typical of many hot or cold rolling mill gauge control applications. A simplified design procedure is also introduced which further simplifies the calculations of the optimal controller and enables standard eigenvector/eigenvalue algorithms to be employed in solving the equations. The procedures are illustrated using a multivariable metal processing control design example.","PeriodicalId":290911,"journal":{"name":"IEE control engineering series","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114159598","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}
An introduction to multivariable control system design has been presented. Due to lack of space, only a brief overview has been given, but the interested reader can consult the references cited for further discussion.
{"title":"Multivariable control system design","authors":"G. Virk","doi":"10.1049/PBCE041E_CH2","DOIUrl":"https://doi.org/10.1049/PBCE041E_CH2","url":null,"abstract":"An introduction to multivariable control system design has been presented. Due to lack of space, only a brief overview has been given, but the interested reader can consult the references cited for further discussion.","PeriodicalId":290911,"journal":{"name":"IEE control engineering series","volume":"55 34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123719258","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}
Fuzzy sets were first introduced by Zadeh as a method of handling 'real-world' classes of objects. Ambiguities abound in these real-world sets, examples given by Zadeh include the 'class of all real numbers which are much greater than 1, and the 'class of tall men'. Examples of these ambiguous sets are easily found in the process control field, where operators may talk about 'very high temperatures' or a 'slight increase in flowrate'. Conventional set theory is clearly inadequate to handle these ambiguous concepts since set members either do, or do not, belong to a set. For example, consider the set 'tall men' a man who is seven feet tall will clearly belong to the set and one who is four feet tall will not, but what about someone who measures five feet ten inches? Zadeh's solution to this problem was to create the fuzzy set, in which members could have a continuous range of membership ranging from zero, or not belonging, to one indicating definite belonging.
{"title":"Basic theory and algorithms for fuzzy sets and logic","authors":"B. Postlethwaite","doi":"10.1049/PBCE044E_CH3","DOIUrl":"https://doi.org/10.1049/PBCE044E_CH3","url":null,"abstract":"Fuzzy sets were first introduced by Zadeh as a method of handling 'real-world' classes of objects. Ambiguities abound in these real-world sets, examples given by Zadeh include the 'class of all real numbers which are much greater than 1, and the 'class of tall men'. Examples of these ambiguous sets are easily found in the process control field, where operators may talk about 'very high temperatures' or a 'slight increase in flowrate'. Conventional set theory is clearly inadequate to handle these ambiguous concepts since set members either do, or do not, belong to a set. For example, consider the set 'tall men' a man who is seven feet tall will clearly belong to the set and one who is four feet tall will not, but what about someone who measures five feet ten inches? Zadeh's solution to this problem was to create the fuzzy set, in which members could have a continuous range of membership ranging from zero, or not belonging, to one indicating definite belonging.","PeriodicalId":290911,"journal":{"name":"IEE control engineering series","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123418676","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 chapter considers the acquisition of skill in dynamical control tasks for which the 'recognise-act' cycle is relatively fast, as in piloting a helicopter. Human pilots commonly receive their initial training on computer simulations. From such trial-and-error learning they acquire cognitive capabilities which they cannot articulate.
{"title":"Cognitive models from subcognitive skills","authors":"D. Michie, Michael Bain, Jean Hayes-Michie","doi":"10.1049/PBCE044E_CH5","DOIUrl":"https://doi.org/10.1049/PBCE044E_CH5","url":null,"abstract":"This chapter considers the acquisition of skill in dynamical control tasks for which the 'recognise-act' cycle is relatively fast, as in piloting a helicopter. Human pilots commonly receive their initial training on computer simulations. From such trial-and-error learning they acquire cognitive capabilities which they cannot articulate.","PeriodicalId":290911,"journal":{"name":"IEE control engineering series","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129084973","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}
With the development of CSAC the control system designer can, for all orders of system, 'fine tune' an initial VSC system design and therefore improve the speed of response of the system whilst in the sliding mode. The CSAC possesses a degree of robustness since it is based upon robust VSC theory. Chatter motion associated with ideal sliding motion in an ideal VSC system is eliminated as the CSAC system is developed from SmVSC, and CSAC is easily implemented. By introducing the CSAC system, the VSC system is now split into three distinct phases: the attainment (hitting) of the sliding mode, the sliding mode itself and then an improvement (adaptation) of the response in that sliding mode, should that be required. There are many degrees of freedom available in prescribing the rate of adaptation in the multivariable CSAC system and this degree greatly increases as the order of the reduced state space of the sliding mode increases. Further investigation into CSAC and its applications should provide additional interesting results.
{"title":"Continuous self-adaptive control using variable structure design techniques","authors":"J. Burton, A. Zinober","doi":"10.1049/PBCE040E_CH15","DOIUrl":"https://doi.org/10.1049/PBCE040E_CH15","url":null,"abstract":"With the development of CSAC the control system designer can, for all orders of system, 'fine tune' an initial VSC system design and therefore improve the speed of response of the system whilst in the sliding mode. The CSAC possesses a degree of robustness since it is based upon robust VSC theory. Chatter motion associated with ideal sliding motion in an ideal VSC system is eliminated as the CSAC system is developed from SmVSC, and CSAC is easily implemented. By introducing the CSAC system, the VSC system is now split into three distinct phases: the attainment (hitting) of the sliding mode, the sliding mode itself and then an improvement (adaptation) of the response in that sliding mode, should that be required. There are many degrees of freedom available in prescribing the rate of adaptation in the multivariable CSAC system and this degree greatly increases as the order of the reduced state space of the sliding mode increases. Further investigation into CSAC and its applications should provide additional interesting results.","PeriodicalId":290911,"journal":{"name":"IEE control engineering series","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127651887","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}
An instrumentation system or a control system does not have to exist at one specific location, nor does a system have to exist in a specific cabinet. Nowadays, the idea of centralised systems is being replaced by the concept of distributed systems. Control engineers long ago discovered the frailty of centralised control rooms and took the advent of the microprocessor to distribute control actions to the point of application. Instrumentation engineers working in the same epoch seized the opportunity to incorporate intelligence into the point of measurement, and computer engineers developed the technique of distributed computing by means of networking. This chapter investigates the means of communication between instruments and controllers using digital computers. It deals first with the ideas of distributed systems and discusses serial interfaces. Later, the modern techniques of local and wide area networking are applied, but not in detail.
{"title":"Real-time computer networking","authors":"G. Barney","doi":"10.1049/PBCE041E_CH6","DOIUrl":"https://doi.org/10.1049/PBCE041E_CH6","url":null,"abstract":"An instrumentation system or a control system does not have to exist at one specific location, nor does a system have to exist in a specific cabinet. Nowadays, the idea of centralised systems is being replaced by the concept of distributed systems. Control engineers long ago discovered the frailty of centralised control rooms and took the advent of the microprocessor to distribute control actions to the point of application. Instrumentation engineers working in the same epoch seized the opportunity to incorporate intelligence into the point of measurement, and computer engineers developed the technique of distributed computing by means of networking. This chapter investigates the means of communication between instruments and controllers using digital computers. It deals first with the ideas of distributed systems and discusses serial interfaces. Later, the modern techniques of local and wide area networking are applied, but not in detail.","PeriodicalId":290911,"journal":{"name":"IEE control engineering series","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117045955","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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