This chapter discusses H2 optimal controllers for multivariable plants. The basic regulator problem has been studied using a time domain approach in the state-space and a frequency domain approach using transfer function matrices and Wiener-Hopf theory.
{"title":"H2 control problems","authors":"K. Hunt, M. Šebek, V. Kučera","doi":"10.1049/PBCE049E_CH2","DOIUrl":"https://doi.org/10.1049/PBCE049E_CH2","url":null,"abstract":"This chapter discusses H2 optimal controllers for multivariable plants. The basic regulator problem has been studied using a time domain approach in the state-space and a frequency domain approach using transfer function matrices and Wiener-Hopf theory.","PeriodicalId":290911,"journal":{"name":"IEE control engineering series","volume":"1 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":"116926008","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}
A consideration of the nature and scope of knowledge based process control (KBPC), leads to the proposal that Artificial Intelligence, Systems Engineering and Information Technology are three core elements of the discipline. Some aspects of all three are described. Systems Engineering methods are based upon whole life whole system considerations which are argued to be fundamental to KBPC. Applying the principles of taxonomy, or classification science, leads to a hierarchical perspective of control instrumentation systems which helps to gain breadth of comprehension of information machines. By first introducing the Systems Engineering approach and the methods of taxonomy, the chapter allows further development of the core theoretical elements in automatic control systems in general but especially in process control. Highlighting of the cardinal elements in the technology of knowledge based process control systems is also allowed using these methods.
{"title":"Holistic approaches in knowledge-based process control","authors":"J. McGhee","doi":"10.1049/PBCE044E_CH1","DOIUrl":"https://doi.org/10.1049/PBCE044E_CH1","url":null,"abstract":"A consideration of the nature and scope of knowledge based process control (KBPC), leads to the proposal that Artificial Intelligence, Systems Engineering and Information Technology are three core elements of the discipline. Some aspects of all three are described. Systems Engineering methods are based upon whole life whole system considerations which are argued to be fundamental to KBPC. Applying the principles of taxonomy, or classification science, leads to a hierarchical perspective of control instrumentation systems which helps to gain breadth of comprehension of information machines. By first introducing the Systems Engineering approach and the methods of taxonomy, the chapter allows further development of the core theoretical elements in automatic control systems in general but especially in process control. Highlighting of the cardinal elements in the technology of knowledge based process control systems is also allowed using these methods.","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":"124139702","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 how object-oriented design (OOD) was used to produce a high-level design in conjunction with a top-down stepwise refinement methodology. It also describes how OOD was used recursively through several layers of recursion on the design of a large, complex software system.
{"title":"An OOD methodology for shop floor control systems","authors":"N. Stanley","doi":"10.1049/PBCE041E_CH11","DOIUrl":"https://doi.org/10.1049/PBCE041E_CH11","url":null,"abstract":"This chapter describes how object-oriented design (OOD) was used to produce a high-level design in conjunction with a top-down stepwise refinement methodology. It also describes how OOD was used recursively through several layers of recursion on the design of a large, complex software system.","PeriodicalId":290911,"journal":{"name":"IEE control engineering series","volume":"37 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":"125082374","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 discusses eigenstructure assignment in linear systems by state feedback. Assignment of invariant factors in linear systems has been intensively studied in control theory for more than two decades since it is of great importance in many areas of this theory. For instance, such classical tasks as linear quadratic control and deadbeat control lead to specific requirements for poles placement of closed-loop systems.
{"title":"Eigenstructure assignment in linear systems by state feedback","authors":"P. Zagalák, V. Kučera, J. Loiseau","doi":"10.1049/PBCE049E_CH8","DOIUrl":"https://doi.org/10.1049/PBCE049E_CH8","url":null,"abstract":"This chapter discusses eigenstructure assignment in linear systems by state feedback. Assignment of invariant factors in linear systems has been intensively studied in control theory for more than two decades since it is of great importance in many areas of this theory. For instance, such classical tasks as linear quadratic control and deadbeat control lead to specific requirements for poles placement of closed-loop systems.","PeriodicalId":290911,"journal":{"name":"IEE control engineering series","volume":"138 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":"121532408","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 survey of DSP architectures shows how difficult it would be to recommend one processor for all tasks. If economic considerations were included, a choice would become even more difficult. However, increasingly powerful general purpose digital signal processors are being supplemented not only by low cost versions, but by specialised architectures. The optimum processing environment is certainly worth looking for.
{"title":"Review of architectures","authors":"R. Chance","doi":"10.1049/PBCE042E_CH17","DOIUrl":"https://doi.org/10.1049/PBCE042E_CH17","url":null,"abstract":"This survey of DSP architectures shows how difficult it would be to recommend one processor for all tasks. If economic considerations were included, a choice would become even more difficult. However, increasingly powerful general purpose digital signal processors are being supplemented not only by low cost versions, but by specialised architectures. The optimum processing environment is certainly worth looking for.","PeriodicalId":290911,"journal":{"name":"IEE control engineering series","volume":"83 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":"121891729","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}
Classical design of digital controllers involves the use of frequency domain methods and the root locus. Control is restricted to single-input, single-output systems. The aim of this chapter is to introduce the state space or modern approach to design. Although single-input, single-output systems will mainly be considered, modern control design can readily be extended to cover systems with several inputs and outputs. The design of state feedback controllers is presented after a brief introduction to state space models including the key ideas of controllability and observability. The selection of feedback gains in order to achieve a desired set of closed-loop poles will be familiar to engineers versed with classical design methods. The implementation of state feedback control laws assumes that all the state variables are known. In practice, the state vector is estimated from the measurements or plant outputs using an observer. Observer design is therefore treated next prior to an analysis of the complete control-estimator system.
{"title":"State space control","authors":"G. Irwin","doi":"10.1049/PBCE042E_CH11","DOIUrl":"https://doi.org/10.1049/PBCE042E_CH11","url":null,"abstract":"Classical design of digital controllers involves the use of frequency domain methods and the root locus. Control is restricted to single-input, single-output systems. The aim of this chapter is to introduce the state space or modern approach to design. Although single-input, single-output systems will mainly be considered, modern control design can readily be extended to cover systems with several inputs and outputs. The design of state feedback controllers is presented after a brief introduction to state space models including the key ideas of controllability and observability. The selection of feedback gains in order to achieve a desired set of closed-loop poles will be familiar to engineers versed with classical design methods. The implementation of state feedback control laws assumes that all the state variables are known. In practice, the state vector is estimated from the measurements or plant outputs using an observer. Observer design is therefore treated next prior to an analysis of the complete control-estimator system.","PeriodicalId":290911,"journal":{"name":"IEE control engineering series","volume":"65 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":"123614092","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}
P. Chung, R. Aylett, D. Bental, R. Inder, T. Lydiard
With the advent of artificial intelligence (AI) techniques and with the increased interest in applying the new technology to a wide variety of problems, there is a proliferation of software tools marketed for developing knowledge based systems. There are many factors that influence the selection of a tool for a particular project. For example, machine availability, supplier credibility, etc. These factors, though important, are not considered in this chapter. The primary concern of this chapter is to look at the AI aspects of tools and see how they influence tool selection. The objective is, therefore, threefold. First, it describes the AI features that are found in KBS tools. Second, it considers the problem of mapping application characteristics to these tool features. Third, it describes three representative tools that implement some of these features.
{"title":"Overview of artificial intelligence tools","authors":"P. Chung, R. Aylett, D. Bental, R. Inder, T. Lydiard","doi":"10.1049/PBCE044E_CH9","DOIUrl":"https://doi.org/10.1049/PBCE044E_CH9","url":null,"abstract":"With the advent of artificial intelligence (AI) techniques and with the increased interest in applying the new technology to a wide variety of problems, there is a proliferation of software tools marketed for developing knowledge based systems. There are many factors that influence the selection of a tool for a particular project. For example, machine availability, supplier credibility, etc. These factors, though important, are not considered in this chapter. The primary concern of this chapter is to look at the AI aspects of tools and see how they influence tool selection. The objective is, therefore, threefold. First, it describes the AI features that are found in KBS tools. Second, it considers the problem of mapping application characteristics to these tool features. Third, it describes three representative tools that implement some of these features.","PeriodicalId":290911,"journal":{"name":"IEE control engineering series","volume":"110 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":"124036480","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 overview is given of a number of computer-controlled drug administration schemes in clinical medicine. These range from systems which have been used on many patients in intensive care to experimental schemes which require further clinical evaluation. The control algorithms used vary from simple PI controllers to multi-mode adaptive techniques. Measurement of relevant clinical variables is often a major problem, and the use of extended Kalman filtering is described for the estimation of unmeasurable states. Recent developments in expert control are also described.
{"title":"Computer control for patient care","authors":"D. Linkens","doi":"10.1049/PBCE041E_CH13","DOIUrl":"https://doi.org/10.1049/PBCE041E_CH13","url":null,"abstract":"An overview is given of a number of computer-controlled drug administration schemes in clinical medicine. These range from systems which have been used on many patients in intensive care to experimental schemes which require further clinical evaluation. The control algorithms used vary from simple PI controllers to multi-mode adaptive techniques. Measurement of relevant clinical variables is often a major problem, and the use of extended Kalman filtering is described for the estimation of unmeasurable states. Recent developments in expert control are also described.","PeriodicalId":290911,"journal":{"name":"IEE control engineering series","volume":"15 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":"123649515","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}
A discrete signal is one which is defined only at isolated discrete points in time, its value at other times being either unknown or assumed to be zero. A discrete signal thus contains only discontinuities and is best described analytically as a set of impulses. Most discrete signals are defined at equally spaced points in time but examples exist of unequal spacing which can successfully be processed. This book considers only equally spaced discrete signals. The amplitude of a discrete signal is normally considered to be an infinitely resolved function as with a continuous signal but the digital signal is a discrete signal which is an important exception to this and is classified separately.
{"title":"Discrete signals and systems","authors":"N. Jones","doi":"10.1049/PBCE042E_ch3","DOIUrl":"https://doi.org/10.1049/PBCE042E_ch3","url":null,"abstract":"A discrete signal is one which is defined only at isolated discrete points in time, its value at other times being either unknown or assumed to be zero. A discrete signal thus contains only discontinuities and is best described analytically as a set of impulses. Most discrete signals are defined at equally spaced points in time but examples exist of unequal spacing which can successfully be processed. This book considers only equally spaced discrete signals. The amplitude of a discrete signal is normally considered to be an infinitely resolved function as with a continuous signal but the digital signal is a discrete signal which is an important exception to this and is classified separately.","PeriodicalId":290911,"journal":{"name":"IEE control engineering series","volume":"68 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":"128337786","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 chapter we study architectures, algorithms, and applications for parallel processing in real-time control. The nature of advances in VLSI technology have resulted in increased computing power generally being made more available through parallel processing architectures of different types rather than increased clock rate in uniprocessor systems. Despite the development of faster processors, the real attraction of parallel processing to system designers is its scalability to meet increasing demands. There is a plethora of control engineering application areas.
{"title":"Parallel processing architecture for real-time control","authors":"P. Fleming","doi":"10.1049/PBCE044E_ch8","DOIUrl":"https://doi.org/10.1049/PBCE044E_ch8","url":null,"abstract":"In this chapter we study architectures, algorithms, and applications for parallel processing in real-time control. The nature of advances in VLSI technology have resulted in increased computing power generally being made more available through parallel processing architectures of different types rather than increased clock rate in uniprocessor systems. Despite the development of faster processors, the real attraction of parallel processing to system designers is its scalability to meet increasing demands. There is a plethora of control engineering application areas.","PeriodicalId":290911,"journal":{"name":"IEE control engineering series","volume":"1 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":"128292406","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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