This dictionary contains terms from the fields of automatic control, which includes mathematical modelling, simulation of dynamic systems, automation technology with its corresponding elements, and robotics. It also includes signal processing, information technologies and production technologies.The terminological dictionary is primarily aimed at experts and students who deal with control technology and dynamic systems in both technical and non-technical domains. To be able to use the dictionary, at least basic knowledge in this field is required. In the dictionary users will find concise terminological definitions. A concept may be designated by different terms; therefore, cross-references are used. The aim of the dictionary is to collect and unify – at least to an achievable extent – the terminology in the field of automatic control, dynamic systems and robotics.

This dictionary contains terms from the fields of automatic control, which includes mathematical modelling, simulation of dynamic systems, automation technology with its corresponding elements, and robotics. It also includes signal processing, information technologies and production technologies.The terminological dictionary is primarily aimed at experts and students who deal with control technology and dynamic systems in both technical and non-technical domains. To be able to use the dictionary, at least basic knowledge in this field is required. In the dictionary users will find concise terminological definitions. A concept may be designated by different terms; therefore, cross-references are used. The aim of the dictionary is to collect and unify – at least to an achievable extent – the terminology in the field of automatic control, dynamic systems and robotics.

This dictionary contains terms from the fields of automatic control, which includes mathematical modelling, simulation of dynamic systems, automation technology with its corresponding elements, and robotics. It also includes signal processing, information technologies and production technologies.The terminological dictionary is primarily aimed at experts and students who deal with control technology and dynamic systems in both technical and non-technical domains. To be able to use the dictionary, at least basic knowledge in this field is required. In the dictionary users will find concise terminological definitions. A concept may be designated by different terms; therefore, cross-references are used. The aim of the dictionary is to collect and unify – at least to an achievable extent – the terminology in the field of automatic control, dynamic systems and robotics.

This monograph opens up new horizons for engineers and researchers inacademia and in industry dealing with or interested in new developments in thefield of system identification and control. It emphasizes guidelines forworking solutions and practical advice for their implementation rather than thetheoretical background of Gaussian process (GP) models. The book demonstratesthe potential of this recent development in probabilistic machine-learningmethods and gives the reader an intuitive understanding of the topic. Thecurrent state of the art is treated along with possible future directions forresearch.Systems control design relies on mathematical models and these may bedeveloped from measurement data. This process of system identification, whenbased on GP models, can play an integral part of control design in data-basedcontrol and its description as such is an essential aspect of the text. Thebackground of GP regression is introduced first with system identification andincorporation of prior knowledge then leading into full-blown control. The bookis illustrated by extensive use of examples, line drawings, and graphicalpresentation of computer-simulation results and plant measurements. Theresearch results presented are applied in real-life case studies drawn fromsuccessful applications including:a gas–liquid separatorcontrol;urban-traffic signalmodelling and reconstruction; andprediction of atmosphericozone concentration.A MATLAB® toolbox, for identification and simulation ofdynamic GP models is provided for download.

This monograph opens up new horizons for engineers and researchers inacademia and in industry dealing with or interested in new developments in thefield of system identification and control. It emphasizes guidelines forworking solutions and practical advice for their implementation rather than thetheoretical background of Gaussian process (GP) models. The book demonstratesthe potential of this recent development in probabilistic machine-learningmethods and gives the reader an intuitive understanding of the topic. Thecurrent state of the art is treated along with possible future directions forresearch.

Systems control design relies on mathematical models and these may bedeveloped from measurement data. This process of system identification, whenbased on GP models, can play an integral part of control design in data-basedcontrol and its description as such is an essential aspect of the text. Thebackground of GP regression is introduced first with system identification andincorporation of prior knowledge then leading into full-blown control. The bookis illustrated by extensive use of examples, line drawings, and graphicalpresentation of computer-simulation results and plant measurements. Theresearch results presented are applied in real-life case studies drawn fromsuccessful applications including:

a gas–liquid separator control; urban-traffic signal modelling and reconstruction; and prediction of atmospheric ozone concentration.

A MATLAB® toolbox, for identification and simulation ofdynamic GP models is provided for download.

This dictionary contains terms from the fields of automatic control, which includes mathematical modelling, simulation of dynamic systems, automation technology with its corresponding elements, and robotics. It also includes signal processing, information technologies and production technologies.The terminological dictionary is primarily aimed at experts and students who deal with control technology and dynamic systems in both technical and non-technical domains. To be able to use the dictionary, at least basic knowledge in this field is required. In the dictionary users will find concise terminological definitions. A concept may be designated by different terms; therefore, cross-references are used. The aim of the dictionary is to collect and unify – at least to an achievable extent – the terminology in the field of automatic control, dynamic systems and robotics.

This dictionary contains terms from the fields of automatic control, which includes mathematical modelling, simulation of dynamic systems, automation technology with its corresponding elements, and robotics. It also includes signal processing, information technologies and production technologies.The terminological dictionary is primarily aimed at experts and students who deal with control technology and dynamic systems in both technical and non-technical domains. To be able to use the dictionary, at least basic knowledge in this field is required. In the dictionary users will find concise terminological definitions. A concept may be designated by different terms; therefore, cross-references are used. The aim of the dictionary is to collect and unify – at least to an achievable extent – the terminology in the field of automatic control, dynamic systems and robotics.

This dictionary contains terms from the fields of automatic control, which includes mathematical modelling, simulation of dynamic systems, automation technology with its corresponding elements, and robotics. It also includes signal processing, information technologies and production technologies.The terminological dictionary is primarily aimed at experts and students who deal with control technology and dynamic systems in both technical and non-technical domains. To be able to use the dictionary, at least basic knowledge in this field is required. In the dictionary users will find concise terminological definitions. A concept may be designated by different terms; therefore, cross-references are used. The aim of the dictionary is to collect and unify – at least to an achievable extent – the terminology in the field of automatic control, dynamic systems and robotics.

This monograph opens up new horizons for engineers and researchers inacademia and in industry dealing with or interested in new developments in thefield of system identification and control. It emphasizes guidelines forworking solutions and practical advice for their implementation rather than thetheoretical background of Gaussian process (GP) models. The book demonstratesthe potential of this recent development in probabilistic machine-learningmethods and gives the reader an intuitive understanding of the topic. Thecurrent state of the art is treated along with possible future directions forresearch.Systems control design relies on mathematical models and these may bedeveloped from measurement data. This process of system identification, whenbased on GP models, can play an integral part of control design in data-basedcontrol and its description as such is an essential aspect of the text. Thebackground of GP regression is introduced first with system identification andincorporation of prior knowledge then leading into full-blown control. The bookis illustrated by extensive use of examples, line drawings, and graphicalpresentation of computer-simulation results and plant measurements. Theresearch results presented are applied in real-life case studies drawn fromsuccessful applications including:a gas–liquid separatorcontrol;urban-traffic signalmodelling and reconstruction; andprediction of atmosphericozone concentration.A MATLAB® toolbox, for identification and simulation ofdynamic GP models is provided for download.

This monograph opens up new horizons for engineers and researchers inacademia and in industry dealing with or interested in new developments in thefield of system identification and control. It emphasizes guidelines forworking solutions and practical advice for their implementation rather than thetheoretical background of Gaussian process (GP) models. The book demonstratesthe potential of this recent development in probabilistic machine-learningmethods and gives the reader an intuitive understanding of the topic. Thecurrent state of the art is treated along with possible future directions forresearch.

Systems control design relies on mathematical models and these may bedeveloped from measurement data. This process of system identification, whenbased on GP models, can play an integral part of control design in data-basedcontrol and its description as such is an essential aspect of the text. Thebackground of GP regression is introduced first with system identification andincorporation of prior knowledge then leading into full-blown control. The bookis illustrated by extensive use of examples, line drawings, and graphicalpresentation of computer-simulation results and plant measurements. Theresearch results presented are applied in real-life case studies drawn fromsuccessful applications including:

a gas–liquid separator control; urban-traffic signal modelling and reconstruction; and prediction of atmospheric ozone concentration.

A MATLAB® toolbox, for identification and simulation ofdynamic GP models is provided for download.