This chapter shows how a broad class of uncertain mechanical systems (including robots) can be forced to behave like a chosen and therefore perfectly known reference model. This model can be used to calculate off-line some particular trajectories or to design some model based control schemes. The presented results are well suited for applications where the robot has to repeatedly perform certain tasks, e.g. in assembly operations with very high precision requirements. A nonlinear reference model and a simple regulator with variable structure control is used. The advantages of using a nonlinear reference model are twofold.
{"title":"Variable structure controllers for robots","authors":"L. Guzzella, H. Geering","doi":"10.1049/PBCE040E_CH7","DOIUrl":"https://doi.org/10.1049/PBCE040E_CH7","url":null,"abstract":"This chapter shows how a broad class of uncertain mechanical systems (including robots) can be forced to behave like a chosen and therefore perfectly known reference model. This model can be used to calculate off-line some particular trajectories or to design some model based control schemes. The presented results are well suited for applications where the robot has to repeatedly perform certain tasks, e.g. in assembly operations with very high precision requirements. A nonlinear reference model and a simple regulator with variable structure control is used. The advantages of using a nonlinear reference model are twofold.","PeriodicalId":290911,"journal":{"name":"IEE control engineering series","volume":"7 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":"131499464","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 presents the modelling, simulation and control of direct-fired liquid-gas vaporizers. The British Gas Isle of Grain Storage Facility was built to meet the requirement for a large volume peak shave storage facility to serve the south east of England. Liquefied natural gas (LNG) was chosen as the most suitable means of providing this storage. The installation consists of four 21,000-tonne LNG storage tanks, two liquefaction plants and a vaporisation/export system. LNG is stored at a temperature of -160°C and a pressure of 100 mbar, and for export into the National Transmission System the gas temperature must be raised to ambient and the pressure to grid conditions, i.e. between 40 and 70 bar.
{"title":"Modelling, simulation and control of direct fired liquid gas vaporisers","authors":"A. Lees","doi":"10.1049/PBCE041E_CH12","DOIUrl":"https://doi.org/10.1049/PBCE041E_CH12","url":null,"abstract":"This chapter presents the modelling, simulation and control of direct-fired liquid-gas vaporizers. The British Gas Isle of Grain Storage Facility was built to meet the requirement for a large volume peak shave storage facility to serve the south east of England. Liquefied natural gas (LNG) was chosen as the most suitable means of providing this storage. The installation consists of four 21,000-tonne LNG storage tanks, two liquefaction plants and a vaporisation/export system. LNG is stored at a temperature of -160°C and a pressure of 100 mbar, and for export into the National Transmission System the gas temperature must be raised to ambient and the pressure to grid conditions, i.e. between 40 and 70 bar.","PeriodicalId":290911,"journal":{"name":"IEE control engineering series","volume":"22 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":"127805889","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 presents parallel processing for computer control. The use of general purpose microprocessors in direct digital control is very beneficial, so adopted in many wide and varied applications. These early microprocessors were rather primitive; they were 8-bit machines working with clock rates of the order of 1 MHz and possessed little software support usually the programming had to be performed in the assembly language. The situation is somewhat better now. The capabilities of electronic chip manufacturers have evolved to the stage where very large scale integration (VLSI) is possible and 32-bit microprocessors working at 25 MHz are commonly available; such modern microprocessors are powerful computing devices, which are able to perform many millions of instructions every second. In addition, they can also have on-chip floating-point support and are able to be programmed in standard high-level languages.
{"title":"Parallel processing for computer control","authors":"G. Virk","doi":"10.1049/PBCE041E_CH7","DOIUrl":"https://doi.org/10.1049/PBCE041E_CH7","url":null,"abstract":"This chapter presents parallel processing for computer control. The use of general purpose microprocessors in direct digital control is very beneficial, so adopted in many wide and varied applications. These early microprocessors were rather primitive; they were 8-bit machines working with clock rates of the order of 1 MHz and possessed little software support usually the programming had to be performed in the assembly language. The situation is somewhat better now. The capabilities of electronic chip manufacturers have evolved to the stage where very large scale integration (VLSI) is possible and 32-bit microprocessors working at 25 MHz are commonly available; such modern microprocessors are powerful computing devices, which are able to perform many millions of instructions every second. In addition, they can also have on-chip floating-point support and are able to be programmed in standard high-level languages.","PeriodicalId":290911,"journal":{"name":"IEE control engineering series","volume":"159 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":"133447866","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 sliding mode control theory the major attention has been paid to finite dimensional systems described by ordinary differential equations. However, mathematical models of a wide range of processes in modern technology are partial differential equations, integro-differential equations and equations with delays. Attempts at theoretical generalisations and applications of sliding modes to control of infinite-dimensional plants show that control scientists and engineers are faced the challenge of increased complexity. Practically all the concepts of discontinuous control theory should be completely revised. Even the basic concepts discontinuity surface, sliding mode, component-wise design should be clarified or reintroduced. In the examples in this chapter we have touched upon infinite-dimensional systems mainly with distributed control.
{"title":"Control of infinite-dimensional plants","authors":"V. Utkin","doi":"10.1049/PBCE040E_CH17","DOIUrl":"https://doi.org/10.1049/PBCE040E_CH17","url":null,"abstract":"In sliding mode control theory the major attention has been paid to finite dimensional systems described by ordinary differential equations. However, mathematical models of a wide range of processes in modern technology are partial differential equations, integro-differential equations and equations with delays. Attempts at theoretical generalisations and applications of sliding modes to control of infinite-dimensional plants show that control scientists and engineers are faced the challenge of increased complexity. Practically all the concepts of discontinuous control theory should be completely revised. Even the basic concepts discontinuity surface, sliding mode, component-wise design should be clarified or reintroduced. In the examples in this chapter we have touched upon infinite-dimensional systems mainly with distributed control.","PeriodicalId":290911,"journal":{"name":"IEE control engineering series","volume":"301 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":"114478021","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}
M. Willis, C. Massimo, G. Montague, M. Tham, A. Morris
Artificial neural networks are made up of highly inter-connected layers of simple 'neuron' like nodes. The neurons act as nonlinear processing elements within the network. An attractive property of artificial neural networks is that given the appropriate network topology, they are capable of characterising nonlinear functional relationships. Furthermore, the structure of the resulting neural network based process model may be considered generic, in the sense that little prior process knowledge is required in its determination. The methodology therefore provides a cost efficient and reliable process modelling technique.
{"title":"Solving process engineering problems using artificial neural networks","authors":"M. Willis, C. Massimo, G. Montague, M. Tham, A. Morris","doi":"10.1049/PBCE044E_CH7","DOIUrl":"https://doi.org/10.1049/PBCE044E_CH7","url":null,"abstract":"Artificial neural networks are made up of highly inter-connected layers of simple 'neuron' like nodes. The neurons act as nonlinear processing elements within the network. An attractive property of artificial neural networks is that given the appropriate network topology, they are capable of characterising nonlinear functional relationships. Furthermore, the structure of the resulting neural network based process model may be considered generic, in the sense that little prior process knowledge is required in its determination. The methodology therefore provides a cost efficient and reliable process modelling technique.","PeriodicalId":290911,"journal":{"name":"IEE control engineering series","volume":"726 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":"116132632","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}
When McCulloch and Pitts first studied artificial neural networks (ANNs), their neuron model consisted of binary signals contributing to a sum which was then thresholded to produce the output of the neuron. This model quickly evolved to the well known 'function of weighted sum of inputs' model.The definition 'an interconnected system of parameterised functions' covers many types of ANNs and neuron models.The functions are respectively, 'output a function of the weighted sum of inputs', and 'output a function of the contents of the addressed memory location'.
{"title":"Digital neural networks","authors":"A. Redgers, I. Aleksander","doi":"10.1049/PBCE053E_CH2","DOIUrl":"https://doi.org/10.1049/PBCE053E_CH2","url":null,"abstract":"When McCulloch and Pitts first studied artificial neural networks (ANNs), their neuron model consisted of binary signals contributing to a sum which was then thresholded to produce the output of the neuron. This model quickly evolved to the well known 'function of weighted sum of inputs' model.The definition 'an interconnected system of parameterised functions' covers many types of ANNs and neuron models.The functions are respectively, 'output a function of the weighted sum of inputs', and 'output a function of the contents of the addressed memory location'.","PeriodicalId":290911,"journal":{"name":"IEE control engineering series","volume":"27 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":"124968054","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}
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}
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}
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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