IAUT1. Course code

Course title Course code

Process control I KRP/IAUT1

Type of course

Lecture + Seminary

Level of course

Bc.

Year of study Semester Number of credits Language

0 ZS 4 CZ

Name of lecturer * Cvejn Jan, doc. Ing. Ph.D.

Objective The goal of the subject is to build up a mathematical aparatus, based on Laplace transform, used in analysis and synthesis of control systems and describe elementaty tools for realization of feedback control. The graduate obtains knowledge necessary for analysis and design of simple control systems. The knowledge is necessary for studying subjects in consequence, especially Automation II and Instruments of Automatic Control. The graduate obtains knowledge necessary for analysis and design of simple control systems.

Prerequisities Course contents Automatic process control - introduction. Dynamic systems. Types of mathematical models. Output and state description. Time-invariant systems. Linearization of the model. Steady-state value. Static characteristics. Dead time. Linear stationary one-dimensional systems. Linearity of the solution, general form of the solution. Impulse and step responses. Fourier and Laplace transforms. Basic statements about images. Simplified vocabulary of L-transform. Using L-transform for obtaining time response of linear systems. System transfer function. Standard form of the transfer function - gain, time constants, astatism. Block algebra. Feedback transfer function. Overview of the most common types of linear systems and their properties (static and astatic system of the first order, second-order systems, high-order system with a dead time). Replacement of a high-order system by the first-order system with a dead time. Automatic regulation. Open and closed control loop. Sensor and actuator transfer function. Discontinuous controllers - two-state and three-state ones. PID controller and its variants. Meaning and realization of particular components. Steady-state regulation error. Closed loop stability. Hurwitz and simplified Nyquist criterions of stability. Aplitude and phase margins. Methods of setting up PID controller parameters. Methods not requiring knowledge of the transfer function - ZieglerNichols method, setting up on the basis of the system step and frequency responses. Setting based on minimum damping. Criterions of linear and quadratic control area. Frequency-domain based design.

Teaching methods Monologická (výklad, přednáška, instruktáž), Metody práce s textem (učebnicí, knihou)

Assesment methods Ústní zkouška, Písemná zkouška

Recommended reading * Cvejn, J. Řízení procesů - úvod do problematiky. Elektronický studijní materiál. UPa, 2007.. * Hanuš, B., Balda, M. a kol. Základy technické kybernetiky I, Skriptum VŠST v Liberci, 1989.. * Hlava, J. Prostředky automatického řízení, Skriptum ČVUT v Praze, 2000.. * Komůrka, J., Gemza, E., Hutla, E., Koropecká, H. Technická kybernetika I, Skriptum VŠCHT v Pardubicích, 1979.. * Kotek, Z., Vysoký, P., Zdráhal, Z. Kybernetika. SNTL, Praha 1990.. * Pírko, Z., Veit, J. Laplaceova transformace, SNTL, Praha, 1970.. * Smith, A. C. Principles and Practice of Automatic Process Control. 3rd Edition. John Wiley & Sons, 2006..


Process control II KRP/IAUT2

Type of course

Lecture + Seminary

Level of course

Bc.


0 LS 4 CZ

Name of lecturer * Honc Daniel, Ing. Ph.D.

Objective The subject covers selected themes belonging to the theoretical basics of the process control and it goes out from subject Process control I. Student will learn more complicated control design methods, discrete and logic control.

Prerequisities Course contents Multidimensional control, discrete time control, Z-transform, difference equations solving, discrete transfer function, conversion between continuous-time and discrete time description, discrete time systems stability, discrete PID controller and its realization, logic control, logic function problem description, optimization of logic functions, sequential logic systems, finite automat principle and its using for control applications.

Teaching methods Monologická (výklad, přednáška, instruktáž), Metody samostatných akcí, Nácvik dovedností

Assesment methods Ústní zkouška, Posouzení zadané práce

Recommended reading * Cvejn, J. ., Řízení procesů - úvod do problematiky. Elektronický studijní materiál. UPa, 2007. * Hanuš, B., Balda, M. a kol. Základy technické kybernetiky I, Skriptum VŠST v Liberci, 1989. * Hlava, J. Prostředky automatického řízení, Skriptum ČVUT v Praze, 2000.. * Kotek, Z., Vysoký, P., Zdráhal, Z. Kybernetika. SNTL, Praha 1990.. * Kotyk, J., Hutla, V. Teorie řízení I, Skriptum VSCHT v Pardubicích, 1990..

* Smith, A. C.. Principles and Practice of Automatic Process Control. 3rd Edition. John Wiley & Sons, 2006..


Process control, laboratory practice KRP/IAUTL

Type of course

Seminary

Level of course

Bc.


0 ZS 3 CZ

Name of lecturer * Havlíček Libor, Ing.

Objective Student masters knowledge of measuring principles, measured data processing and acquired information using in a control of simulated and real processes. Student learns basics of process identification and control.

Prerequisities Course contents Principles of planning and leading the experiment. Measuring of actuating and measuring elements characteristics. Measuring of controlled systems static characteristics and step responses. Measured data evaluation. Simulation of control response with PID controller. Control of simple hydraulic, thermal and electrical plants. Laboratory report elaboration.

Teaching methods Demonstrace, Laborování

Assesment methods Rozbor produktů pracovní činnosti studenta, Rozhovor, Systematické pozorování

Recommended reading * Dokumentace k MATLABu - součást instalace. * MATLAB, Simulink, Control System Toolbox. * Dušek F., Honc D. Matlab a Simulink - úvod do používání. [skriptum], Univerzita Pardubice, 2005.


Bachelor Thesis KRP/IBAPE

Type of course

Lesson

Level of course

Bc.

Year of study

0

Semester

LS

Number of credits

10

Language

CZ

Name of lecturer * Dušek František, doc. Ing. CSc.

Objective Bachelor thesis means individual student work under supervisor leading. Student select on them from communication engineering and microcontroller technology themes. Practical special activity.

Prerequisities Course contents Subject must fulfill bachelor thisis submission.

Teaching methods Dialogická (diskuze, rozhovor, brainstorming)

Assesment methods Rozhovor

Recommended reading * Dle cílů a obsahu zadání bakalářské práce..


Bachelor Course KRP/IBASE

Type of course

Lesson

Level of course

Bc.


0 LS 2 CZ


Objective At seminar are students well - informeds about needs rapid - fire on diploma work , about hers content and formal adjustment. Is them directed and progress at searching needed information, at elaboration personal BP and preparation on hers defence. Further are them provided tuition from articles baccalaureate examination. Seminar will give to students instruction on elaboration baccalaureate work technically and recommendation to preparation defences BW.

Prerequisities Course contents At seminar are students well - informeds about needs rapid - fire on diploma work , about hers content and formal adjustment. Is them directed and progress at searching needed information, at elaboration personal BP and preparation on hers defence. Further are them provided tuition from articles baccalaureate examination.

Teaching methods Dialogická (diskuze, rozhovor, brainstorming)


Recommended reading * Dle cílů a obsahu zadání bakalářské práce..


Selected Chapters from Control Theory KRP/IDSAR

Type of course

no contact

Level of course

Ph.D.


0 ZS+LS 0 CZ


Objective The aim is to inform students about advanced methods of control design of linear dynamic continuous systems in discrete-time area. Attention is paid to next areas: adaptive control of single-input single-output systems (Model Reference Control, suboptimal control with on-line identification), stochastic systems, Linear Quadratic (Gaussian) control and state estimation, predictive control of multi-input multi-output systems Students get knowledge about design of adaptive control and MIMO linear systems control.

Prerequisities Course contents Adaptive control of single-input single-output systems. Model Reference Control, Gain Scheduling, suboptimal control with on-line identification. Linear Quadratic (Gaussian) control and state estimation. Control of multi-input multi-output linear systems. Predictive Control based on state space model.

Teaching methods Metody práce s textem (učebnicí, knihou), Metody samostatných akcí


Recommended reading * ASTRÖM, K. J.; WITTENMARK, B. Adaptive Control. Addison-Wesley Publishing, 1995. * CAMACHO, E. F., BORDONS, C. Model Predictive Control. Springer Verlag, 1999.. * HAVLENA, V.; ŠTECHA, J. Moderní teorie řízení. Vydavatelství ČVUT, Praha, 2000. * Maciejowski, J. M. Predictive Control with Constrains. 2002, Pearson Education Ltd., Essex. * OGATA, K. Discrete-Time Control Systems. Prentice Hall, 1995.. * OGATA, K. Modern Control Engineering. Prentice Hall, 1990..


Modern Methods of Identification and Control KRP/IDSIR

Type of course

no contact

Level of course

Ph.D.


0 ZS+LS 0 CZ


Objective The subject introduces into selected modern approaches to dynamic system indentification and synthesis of feedback control. According to individual interest and Ph.D. thesis theme it is possible to focus on a particular topic. Obtaining deeper knowledge of the methods of dynamic system identification and synthesis of control.

Prerequisities Course contents Identification part - Parameter estimation methods: generalized least squares, instrumental variable method. PEM model and maximum likelihood estimate. Asymptotic properties of the estimate. Recursive estimates. Control of LTI systems - Robust stability and performance. Stabilisation using Youla-Kučera parametrisation. Loopshaping. Synthesis of control loop by minimization of H2 and Hinf norms. Design using LMI. Structured uncertainties. Nonlinear control systems - Lyapunov and modern theory of stability. Passive systems. Design of a control system based on theory of exact linearisation.

Teaching methods Metody práce s textem (učebnicí, knihou)

Assesment methods Ústní zkouška

Recommended reading * Doyle J., Francis B., Tannenbaum A. Feedback Control Theory. Macmillan Publishing Co., 1990. * Goodwin C. G., Payne R. L. Dynamic System Identification. Academic Press, New York, 1977. * Isidori A. . Nonlinear Control Systems. Third Edition.. Springer-Verlag, 1995. * Ljung L. System Identification. Theory for the User. Second Edition.. Prentice Hall, 1999. * Marquez H. J. Nonlinear Control Systems. Wiley & Sons, 2003. * Sanchez - Pena R. S., Sznaier R. Robust Systems. Theory and Applications.. Wiley & sons, 1998. * Skogestad S., Poslethwaite I. Multivariable Feedback Control. 2nd edition.. Wiley & Sons, 2005.


Selected Chapters from Math Statistics KRP/IDSMS

Type of course

no contact

Level of course

Ph.D.


0 ZS+LS 0 CZ

Name of lecturer * Javůrek Milan, doc. Ing. CSc.

Objective The application of computer oriented statistical methods in scientific and technical fields enables not only the use of information hidden in data but also the creation of models, optimizations, and possible solutions. It is a multidisciplinary movement on the frontier of the scientific disciplines of statistics and informatics. The goal of multivariate data processing is to classify data according to many variables and to find hidden structure and interrelationship among these variables. The objective is to find a way of condensing the information contained in a number of original variables into a smaller set of variables with a minimum loss of information. The objective is to classify a sample of entities into a small number of mutually exclusive groups based on the similarities among the entities.

Independent creativ knowledge of evoluation of really experimental data.

Prerequisities Course contents Metrology: Introduction to the basics of metrology, statistical estimation parameters position, dispersion and form, calculation uncertainties establish entitlements result. Character range unit data: Data matrix, objects and variables. Types of variables and multidimensional accidental vector. Preliminary treatment multidimensional data: Sorts transformation. Centering and standardization data. Exploratory analysis of multivariate data: Sorts display range multivariate data. Searching of outliers. Statistical testing of multivariate accidental selections: Estimates parameters position and dispersion. Statistic analysis vector mean value, statistic analysis of covarince matrixes. Analysis covariance: Interpretation of covariane matrix. Analysis of correlation matrix. Pair correlation coefficient, partial correlation coefficient, multiple correlation coefficient. Principal components analysis PCA: Characteristics and geometric meaning of the chief component and their reading. Graphic tools PCA. Diagnostics PCA. Factor analysis FA: Principles of method and progress FA. Model of factor analyses and parameter estimate. Estimation of factor score, rotation factors. Statement of a problem FA and graphic tools. Found solving and achieved tightness fitting. Reading results and naming factors. Canonical correlation analysis CCA: Principles of method and progress diagnosed CCA. Test of significance canonical correlation. Found solving and achieved tightness fitting. Discriminant analysis DA: Classification objects. Principles of method, progress DA and range rules. Linear and quadratic discriminating function. Option signs. Adjustment threshold point. Diagram territorial map. Found solving and achieved tightness fitting. Logistic regression LR: Principles of method and progress logistic regression. Estimates of parameters and their statistical significance and reading. Quality evaluation and found solving and achieved tightness fitting.

Cluster analysis CLU: Principles of cluster analyses. Measurement similarity and distance. Fitness standardization data. Criteria for appreciation qualities analysis to the clusters, distance and resemblance objects. Hierarchic sequence analysis. Dendrograms hierarchical clustering. Fuzzy clustering. Clustering method nearest centresmedoids. Tightness fitting under the course of construction clusters. Surveying objects range unit spectrum MDS: Principles of method and progress range unit spectrum. Metric and no metric method MDS. Found solving and achieved tightness fitting. Correspondence analysis CA: Principles of method and progress of correspondence analyses. Found solving and achieved tightness fitting. Reading results.

Teaching methods Monologická (výklad, přednáška, instruktáž), Laborování


Recommended reading * HEBÁK, P. a kol. Vícerozměrné statistické metody (1). Praha: Informatorium, (2004), ISBN 80-7333-025-3.. * HEBÁK, P. a kol. Vícerozměrné statistické metody (3). Praha: Informatorium, (2007), ISBN 978-80-73333-0019.. * MELOUN, M.; MILITKÝ, J. Kompendium statistického zpracování dat. Praha: Academia (2006), ISBN 80-2001396-2.. * MELOUN, M.; MILITKÝ, J. Statistical analysis of experimental data. In press.. * MELOUN, M.; MILITKÝ, J. Statistická analýza experimentálních dat. Praha: Academia (2004), ISBN 80-2001254-0..


Optimization and Optimal Control of Technological KRP/IDSOR

Type of course

no contact

Level of course

Ph.D.


0 ZS+LS 0 CZ


Objective The subject is focused on introduction into basics of deterministic theory of optimal processes and principles of numerical solving extremal tasks with respect to applications in the area of technological processes. Obtaining orientation in the basics of deterministic theory of optimal processes and in principles of numerical solving extremal tasks with respect to applications in the area of technological processes.

Prerequisities Course contents Problems of dynamic optimization in discrete time domain - transformation into static optimization problem, Bellman optimality principle. Variational approach to solving problems in continuous domain, necessary and sufficient conditions of the extreme. HBJ equation. Applications for linear systems, LQR controller. Solving problems with contraints on control and state, Pontrjagin maximum principle. Numerical methods of computation of optimal trajectories. Introduction into modern mathematical theory of optimal processes - basics of differential calculus in functional spaces and their applications for obtaining conditions of optimality.

Teaching methods Metody práce s textem (učebnicí, knihou)


Recommended reading * Alexejev V. M. a kol. . Matematická teorie optimálních procesů. Academia, Praha, 1991. * Bryson A. E., Ho Y.C. Applied Optimal Control . Hemisphere Corp., New York, 1981. * Kirk, D.E. Optimal Control Theory: An Introduction. Dover Publications, 2004. * Stengel, R. Optimal Control and Estimation. Dover Publications, 1994. * Štecha J. Optimální rozhodování a řízení. ČVUT, Praha, 2000.


Continuous Systems Modelling and Simulation KRP/IDSSP

Type of course

no contact

Level of course

Ph.D.


0 ZS+LS 0 CZ


Objective The aim is to inform students about advanced methods of modeling and simulation of continuous systems. A mathematical description formulation of dynamic systems behavior is based primary on first principles application. The mathematical model is in a form of system of ordinary or partial differential and algebraic equations. The way of mathematical model solving is shown in a part dedicated to simulation. The simulation is a math model numerical solving including restrictions, discreteness, boundary conditions and other real conditions which are significant for given system. All computations are made in MATLAB/SIMULINK environment. Students get knowledge about math models building based on first principles method. They will be informed about simulation in MATLAB/SIMULINK environment of nonlinear systems that are described by system of ordinary differential and algebraic equations.

Prerequisities Course contents Math models of static and dynamic behavior of mechanical, heat, hydraulic and electric systems Simulation of dynamic behavior of nonlinear systems describing by ordinary differential and difference equations and containing typical discontinuities



Recommended reading * ČERMÁK,J. ; PETERKA, V. ; ZÁVORKA, J. Dynamika regulovaných soustav v tepelné energetice a chemii. ACADEMIA Praha, 1968.. * DUŠEK, F.; HONC, D. Matlab a Simulink - úvod do používání. Univerzita Pardubice, 2005.. * Noskievič, P. Modelování a identifikace systémů. Montanex a.s., 1999. ISBN 80-7225-030-2. * WOODS, R. L.; LAWRENCE, K. L.:. Modelling and Simulation of Dynamic Systems. Prentice Hall, 1997.. * Zítek, P. Simulace dynamických systémů.. Ediční středisko ČVUT, Praha, 1983.


Method of Artificial Intelligence (Neural Networks) KRP/IDSUI

Type of course

no contact

Level of course

Ph.D.


0 ZS+LS 0 CZ

Name of lecturer * Taufer Ivan, prof. Ing. DrSc.

Objective The goal of the subject is to teach the students modern methods how to model static and dynamical properties of the system. Students will learn neural network paradigm, theoretical background and practical implementation. Student will be able to create artificial neural networks and realize computationally their learning and implementation.

Prerequisities Course contents Introduction. Basic terms and definitions. Historical perspective. Biological neural network. Biological neuron. Artificial neural network. Artificial neuron. Percepton. Neural network learning. Training and testing. Artificial neural network classification. Neural network types. Forward, multilayer neural networks. Forward neural network learning. Backpropagation method. Static properties modelling of the systems. Creation of the training and testing matrix. Fuzzy neural networks. Software for neural networks creation. MATLAB/Neural Network Toolbox. Practical examples.

Teaching methods Monologická (výklad, přednáška, instruktáž), Dialogická (diskuze, rozhovor, brainstorming), Metody samostatných akcí, Laborování

Assesment methods Písemná zkouška, Posouzení zadané práce

Recommended reading * BÍLA, J. Umělá inteligence a neuronové sítě v aplikacích. 115 s. ISBN 80-01-01275-1. Praha : ČVUT, 1995. ISBN 80-01-01275-1. * FAUSETT, L.V. Fundamentals of Neural Network: Architectures, Algorithm and Applications. New Persey : Prentice Hall, 1994. * MAŘÍK, V a kol. Umělá inteligence. Praha : ACADEMIE, 1993. ISBN 80-200-0502-1.. * POKORNÝ, M. Umělá inteligence v modelování a řízení. ISBN 80-901984-4-9. Praha: BEN, 1992. * ŠNOREK, M.; JIŘINA, M. Neuronové sítě a neuropočítače. Praha : ČVUT, 1996. * VONDRÁK, I. Umělá inteligence a neuronové sítě. Ostrava: VŠB - TU Ostrava, 1994.


Selected Chapters from Control Theory KRP/IDZAR

Type of course

no contact

Level of course

Ph.D.


0 ZS+LS 0 CZ


Objective The aim is to inform students about advanced methods of control design of linear dynamic continuous systems in discrete-time area. Attention is paid to next areas: adaptive control of single-input single-output systems (Model Reference Control, suboptimal control with on-line identification), stochastic systems, Linear Quadratic (Gaussian) control and state estimation, predictive control of multi-input multi-output systems Students get knowledge about design of adaptive control and MIMO linear systems control.

Prerequisities Course contents Adaptive control of single-input single-output systems. Model Reference Control, Gain Scheduling, suboptimal control with on-line identification. Linear Quadratic (Gaussian) control and state estimation. Control of multi-input multi-output linear systems. Predictive Control based on state space model.



Recommended reading * ASTRÖM, K. J.; WITTENMARK, B. Adaptive Control. Addison-Wesley Publishing, 1995. * CAMACHO, E. F., BORDONS, C. Model Predictive Control. Springer Verlag, 1999.. * HAVLENA, V.; ŠTECHA, J. Moderní teorie řízení. Vydavatelství ČVUT, Praha, 2000. * Maciejowski, J. M. Predictive Control with Constrains. 2002, Pearson Education Ltd., Essex. * OGATA, K. Discrete-Time Control Systems. Prentice Hall, 1995.. * OGATA, K. Modern Control Engineering. Prentice Hall, 1990..


Software tools for measurement and data processsing KRP/IDZMD

Type of course

no contact

Level of course

null


0 ZS+LS 0 CZ


Objective Evalution of subject within state final examination.

Pass of state final examination

Prerequisities Course contents null

Teaching methods Metody samostatných akcí


Recommended reading * Morris, Alan. Measurement and instrumentation principles. Oxford : Butterworth-Heinemann, 2001. ISBN 07506-5081-8.


Continuous Systems Modelling and Simulation KRP/IDZSP

Type of course

no contact

Level of course

Ph.D.


0 ZS+LS 0 CZ


Objective The aim is to inform students about advanced methods of modeling and simulation of continuous systems. A mathematical description formulation of dynamic systems behavior is based primary on first principles application. The mathematical model is in a form of system of ordinary or partial differential and algebraic equations. The way of mathematical model solving is shown in a part dedicated to simulation. The simulation is a math model numerical solving including restrictions, discreteness, boundary conditions and other real conditions which are significant for given system. All computations are made in MATLAB/SIMULINK environment. Students get knowledge about math models building based on first principles method. They will be informed about simulation in MATLAB/SIMULINK environment of nonlinear systems that are described by system of ordinary differential and algebraic equations.

Prerequisities Course contents Math models of static and dynamic behavior of mechanical, heat, hydraulic and electric systems Simulation of dynamic behavior of nonlinear systems describing by ordinary differential and difference equations and containing typical discontinuities



Recommended reading * ČERMÁK,J. ; PETERKA, V. ; ZÁVORKA, J. Dynamika regulovaných soustav v tepelné energetice a chemii. ACADEMIA Praha, 1968.. * DUŠEK, F.; HONC, D. Matlab a Simulink - úvod do používání. Univerzita Pardubice, 2005.. * Noskievič, P. Modelování a identifikace systémů. Montanex a.s., 1999. ISBN 80-7225-030-2. * WOODS, R. L.; LAWRENCE, K. L.:. Modelling and Simulation of Dynamic Systems. Prentice Hall, 1997.. * Zítek, P. Simulace dynamických systémů.. Ediční středisko ČVUT, Praha, 1983.


Method of Artificial Intelligence (Neural Networks) KRP/IDZUI

Type of course

no contact

Level of course

Ph.D.


0 ZS+LS 0 CZ

Name of lecturer * Taufer Ivan, prof. Ing. DrSc.



Teaching methods Monologická (výklad, přednáška, instruktáž), Dialogická (diskuze, rozhovor, brainstorming), Metody samostatných akcí, Laborování


Recommended reading * BÍLA, J. Umělá inteligence a neuronové sítě v aplikacích. 115 s. ISBN 80-01-01275-1. Praha : ČVUT, 1995. ISBN 80-01-01275-1. * FAUSETT, L.V. Fundamentals of Neural Network: Architectures, Algorithm and Applications. New Persey : Prentice Hall, 1994. * MAŘÍK, V a kol. Umělá inteligence. Praha : ACADEMIE, 1993. ISBN 80-200-0502-1.. * POKORNÝ, M. Umělá inteligence v modelování a řízení. ISBN 80-901984-4-9. Praha: BEN, 1992. * ŠNOREK, M.; JIŘINA, M. Neuronové sítě a neuropočítače. Praha : ČVUT, 1996. * VONDRÁK, I. Umělá inteligence a neuronové sítě. Ostrava: VŠB - TU Ostrava, 1994.


Modeling and simulation of dynamical processes KRP/IMSDS

Type of course

Lecture + Seminary

Level of course

Bc.


0 ZS+LS 3 CZ


Objective The subject is focused on dynamical systems modelling problematic and its using by continuous-time or discrete control design and simulation. Student will learn and practice dynamical processes modelling and simulation possibilities in computational environment MATLAB / Simulink.

Prerequisities Course contents Conception and basic principles, mathematical-physical analysis, experimental identification. Simple models examples - hydraulic, thermal, mechanical, electrical systems. Types of nonlinearities, linearization. Controlled process and controller simulation. Feed-forward control. Feedback control - on/off controller, PID controller. Continuous-time and discrete control. Hybrid systems.



Recommended reading * Dokumentace k MATLABu - součást instalace. * Dušek F., Honc D. Matlab a Simulink - úvod do používání. [skriptum], Univerzita Pardubice, 2005.


Computer SW Modelling KRP/IMSW

Type of course

Lecture + Lesson

Level of course

Bc.


0 LS 4 CZ


Objective The goal of subject is to familiarize with some computational systems (Maple, MathCad, Mathematica) and in the first place with program Matlab. Using of MATLAB/SIMULINK environment. Numerical solving of ordinary differential equations, numerical evaluating of integrals, finding of extreme of functions with more variables, finding of equation roots.

Prerequisities Course contents The basic operations with matrixes, solving tasks of matrix computations, work with polynomials and interpolation. Visualization - drawing of graph function, 2D- and 3D-graphs. Making user's scripts and functions. Solving simple and complex mathematics' task by numerical methods, using functions of the functions, numerical integration and so on. Creation of a model and its start ii SIMULINK, standard libraries, subsystems and mask subsystems, user libraries. Solving of the simple models using SIMULINK, data input and output.


Assesment methods Písemná zkouška

Recommended reading * Bartko, Miller. Matlab I - algoritmizaci a riešenie úloh, ISBN: 80-969310-0-8. * dostupné publikace. http://www.humusoft.cz/matlab/knihy.htm. * Dušek,F. Matlab a Simulink, úvod do používání, FChT UPCE, Pardubice 2000.. * Kozák, Kajan. Matlab - Simulink, STU Bratislava, ISBN: 80-227-1213-2. * Perůtka. Matlab - Základy pro studenty algoritmizace a informačních technologií, Univerzita Tomáše Bati, Zlín 2005, ISBN: 80-7318-355-2. * Pultarová, Novák. Základy informatiky: Počítačové modelování v Matlabu, ČVUT Stavební fakulta, 2005. * Zaplatílek, Doňar. Matlab při začátečníky. Praha : Technická literatura BEN, 2003. ISBN 80-7300-095-4.


Computer SW Modelling KRP/IMSWE

Type of course

Lecture + Lesson

Level of course

Bc.


0 LS 4 CZ

Name of lecturer * Dušek František, doc. Ing. CSc. * Pola Marek, Ing. * Škrabánek Pavel, Ing.

Objective The goal of subject is to familiarize with some computational systems (Maple, MathCad, Mathematica) and in the first place with program Matlab. Using of MATLAB/SIMULINK environment. Numerical solving of ordinary differential equations, numerical evaluating of integrals, finding of extreme of functions with more variables, finding of equation roots.

Prerequisities Course contents The basic operations with matrixes, solving tasks of matrix computations, work with polynomials and interpolation. Visualization - drawing of graph function, 2D- and 3D-graphs. Making user's scripts and functions. Solving simple and complex mathematics' task by numerical methods, using functions of the functions, numerical integration and so on. Creation of a model and its start ii SIMULINK, standard libraries, subsystems and mask subsystems, user libraries. Solving of the simple models using SIMULINK, data input and output.


Assesment methods Písemná zkouška

Recommended reading * Bartko, Miller. Matlab I - algoritmizaci a riešenie úloh, ISBN: 80-969310-0-8. * Dušek,F. Matlab a Simulink, úvod do používání, FChT UPCE, Pardubice 2000.. * Kozák, Kajan. Matlab - Simulink, STU Bratislava, ISBN: 80-227-1213-2. * Perůtka. Matlab - Základy pro studenty algoritmizace a informačních technologií, Univerzita Tomáše Bati, Zlín 2005, ISBN: 80-7318-355-2. * Pultarová, Novák. Základy informatiky: Počítačové modelování v Matlabu, ČVUT Stavební fakulta, 2005. * Zaplatílek, Doňar. Matlab při začátečníky. Praha : Technická literatura BEN, 2003. ISBN 80-7300-095-4.


Methods of Optimization and Optimal Control KRP/INMOE

Type of course

Lecture + Lesson

Level of course

Mgr.


0 ZS 4 CZ


Objective The goal of the subject is providing introduction into theoretical apparatus of methods of optimization and making an overview of the most important algorithms of searching for the optimum. An emphasis is posed on solving practical problems, which are chosen so that they demonstrate properties of particular methods. In the end of the semester approaches the global optimization problem are discussed. For solving problems utilization of Matlab software is assumed. Obtaining an introduction into theoretical aparatus of optimization methods and providing an overview of the most important algorithms of searching optimum.

Prerequisities Course contents Introduction. Domains of use of optimization. Types of optimization problems. Parametrization. Mathematical apparatus for optimization - linear spaces, linear mappings, quadratic forms, differentiability, Taylor expansion of multiparameter functions. Mathematical formulation of optimization problem, types of extremes. Problems without constraints - necessary and sufficient conditions of the minimum. Minimum of a quadratic function, least-squares problem. Numerical solution with QR and SVD decompositions. Numerical algorithms for solving smooth problems without constraints. Division of the methods. Rate of convergence. Methods not using function model. Methods using direction search. Steepest descent method, Newton metod. Quasi-Newtonon methods and conjugate-gradient methods. Restricted step principle. Levenberg-Marquardt methods. Non-linear least squares problem and solving systems of non-linear equations. Problems with linear constraints of equality type. Linear programming. Elementary problems. Standard LP form. Simplex method.

Basics of theory of optimization with constraints. Necessary and sufficient conditions. Convex problems. Duality. Principles of numerical solving problems with equality-type constraits. Penalty functions, method of extended Lagrangian. Elimination of variables. Lagrange-Newton method. Solving problems with constraints of equality and inequality type. Barrier functions. Active set method. Quadratic programming, Sequential quadratic programming. Projection methods. Interior point methods. Integer optimization problems. Branch and bound method. Approaches to global optimization problem. Methods using local optimization from randomly generated points. Covering methods. Methods of generalized local search. Random search methods. Evolution methods. Introduction into the theory of continuous optimal processes. Formulation of the optimal control problem. Necessary and sufficient conditions of optimality. Control variable contraints. Pontryagin maximum principle.

Teaching methods Monologická (výklad, přednáška, instruktáž), Metody práce s textem (učebnicí, knihou), Metody samostatných akcí

Assesment methods Ústní zkouška, Písemná zkouška, Rozbor produktů pracovní činnosti studenta

Recommended reading * Alexejev, V. M., Tichomirov, V. M., Fomin, S. V. Matematická teorie optimálních procesů, Praha, 1991.. * Bryson, A.E., Ho, Y.C. Applied Optimal Control. Hemisphere Corp., 1981. * Fletcher, R. Practical Methods of Optimization. John Wiley & Sons Ltd., 2nd edition, 1987. * Maňas, M. Optimalizační metody. SNTL, Praha, 1979. * Nocedal, J., Wright, S. J. Numerical optimization. Springer Verlag, 1999. * Stengel, R. Optimal Control and Estimation. Dover Publications, 1994. * Štecha, J. Optimální rozhodování a řízení.Praha, ČVUT, 2000. Praha: ČVUT, 2004. ISBN 80-01-03010-5.


Advanced Control Systems KRP/INPRE

Type of course

Lecture + Lesson

Level of course

Mgr.


0 LS 4 CZ

Name of lecturer * Dušek František, doc. Ing. CSc. * Macháček Jiří, doc. Ing. CSc.

Objective The goal of subject is to familiarize students with modern control systems based on both new hardware (computers, microcontrollers, numerically processing signals circuits) and new algorithms for signal processing. The students will have the newest knowledge in this area and they will be able to continue after entering to practice. Another knowledge and information in Advanced control theory

Prerequisities Course contents Modern control system using state space description, pole placement, principles of robust, adaptive and nonlinear control, theory of estimation. Signal analysis in control systems, covariance function, and spectral behavior. Signal filtration. Adaptive control with on-line identification. Stochastic system control - minimum variance control, linear quadratic control, Kalman's estimator. Control of nonlinear systems - typical nonlinearities in control loops, method of equivalence transfer functions. Control of multi input multi output (MIMO) systems - MIMO controllers, distributed control, autonomous and invariant systems. Principle of robust and predictive control. Logic control and fuzzy control.

Teaching methods Monologická (výklad, přednáška, instruktáž), Dialogická (diskuze, rozhovor, brainstorming), Metody samostatných akcí, Demonstrace


Recommended reading * HAVLENA, V., ŠTĚCHA, J. Moderní teorie řízení. Praha: ČVUT, 2000. ISBN 80-01-02095-9. * Shinners, S. M. Advanced Modern Control System Theory and Design, John Wiley and Sons, Inc., NY, 1998. Wiley, 1998. ISBN 0471-31857-4.


Industrial control systems KRP/INPSE

Type of course

Lecture + Lesson

Level of course

Mgr.


0 ZS 4 CZ


Objective The subject follows Control Systems Theory and brings the students new knowledge in industrial controller design area, using of industrial controllers and data buses. Student will learn industrial control problematic from hardware and software point of view.

Prerequisities Course contents Industrial controllers classification, PLC and industrial control systems. Measurement systems, PC acquisition cards, prototypes development (dSpace). Inustrial busses (Profibus, CAN, Ethernet), distributed I/O. Signals, processing and conversion, transferring, digitalization, SW processing (calibration, filtration, linearization), real-time systems. Sensors principles and non-electric variables measurement. Software support for control design and application (SIMATIC - WinCC, general systems CITEC, WONDEWARE Control Web, PROMOTIC), measurement systems (NI - LabView, dSpace - SIMULINK) Industrial control systems examples (Simatic, Regula, Eurotherm). Compact controller - single chip controllers. Programmable industrial controllers.

Teaching methods Monologická (výklad, přednáška, instruktáž), Metody samostatných akcí, Demonstrace, Nácvik dovedností, Laborování


Recommended reading * Bateson, RN. Introduction to Control System Technology. 5th ed. London: Prentice-Hall International (UK) Limited, 1996. 784 p. ISBN: 0-13-226275-4. * Příručky: SIMULINK, MATLAB. * Ďaďo, S., Kreidl, M. Senzory a měřicí obvody. Vydavatelství ČVUT, Praha 1996. * Horáček, P. Systémy a modely. Skripta ČVUT, Praha 2000. * JOHN, J. Systémy a řízení. Praha : ČVUT, 1998. * Jones, L. D., Chin, A. F. Electronic Instruments and Measurements. 2nd ed. London: Prentice- Hall International (UK) Limited, 1991. ISBN: 0-13-248857-4. * Kuo, B. C. Automatic Control Systems. Prentice-Hall International. ISBN: 0-13-312174-7. * Štecha, J., Horáček, P. Optimální řídicí systémy. Praha, ČVUT 1989.


Control systems theory KRP/INRSE

Type of course

Lecture + Seminary

Level of course

Mgr.


0 ZS 4 CZ


Objective The goal of subject is to give students theoretical basis for the next study of control theory. The knowledge from subject Regulation and automation is exploited here and gained knowledge is necessary in subjects Advance control systems and Methods of optimalization and optimal control Basic of Control theory - the fundamental terms and methods.

Prerequisities Course contents Basic terms from control theory - system, model, variables, control, feedforward and feedback control Model creation based on the first principles. Input/output and state space model (controllable, observable). Integral Laplace transformation - transfer function, properties. Sampling, discrete models and Z-transformation. Experimental identification, model parameters estimation from experimental data. Off-line and on-line estimation based on last square method. Closed control loop, continuous PID regulator. Stability of closed loop and control quality. Continuous PID regulator based on operational amplifier. PID parameters tuning. PSD discrete PID regulator. Controller design by pole placement method.

Teaching methods Monologická (výklad, přednáška, instruktáž), Dialogická (diskuze, rozhovor, brainstorming), Metody samostatných akcí, Demonstrace


Recommended reading * Dušek, F. - Honc, D. MATLAB a SIMULINK - úvod do používání (skriptum). Pardubice, 2005. ISBN 80-7194776-8. * Kolektiv. Automatizace a automatizační technika I až IV. (4 díly). Computer Press, 2000. * Kubík, S., Kotek, Z., Razím, M., Hrušík, J., Branžovský, J. Teorie automatického řízení II. Praha: SNTL, 1982.. * Mikeš, J., Hutla, V. Teória automatického riadenia. Bratislava: ALFA/SNTL, 1986.. * Štecha, J. Optimální rozhodování a řízení.Praha, ČVUT, 2000. Praha: ČVUT, 2004. ISBN 80-01-03010-5.


Statistical analysis of multivariate data KRP/INSAD

Type of course

Lecture + Lesson

Level of course

Mgr.


0 LS 4 CZ


Objective The application of computer oriented statistical methods in scientific and technical fields enables not only the use of information hidden in data but also the creation of models, optimizations, and possible solutions. It is a multidisciplinary movement on the frontier of the scientific disciplines of statistics and informatics. The goal of multivariate data processing is to classify data according to many variables and to find hidden structure and interrelationship among these variables. The objective is to find a way of condensing the information contained in a number of original variables into a smaller set of variables with a minimum loss of information. The objective is to classify a sample of entities into a small number of mutually exclusive groups based on the similarities among the entities. Evaluation of experimental data independently.

Prerequisities Course contents Nature of multivariate data. Exploratory data treatment. Statistical testing of multivariate data. Structure hidden in the data. Principal komponent analysis PCA. Factor analysis FA. Canonical correlation analysis CCA. Discriminant analysis DA. Logistic regression LR. Cluster analysis CLU. Multidimensional data analysis MDA. Correspondence analysis CA.

Teaching methods Monologická (výklad, přednáška, instruktáž), Metody samostatných akcí


Recommended reading * 1. M. Meloun, J. Militký. Kompendium statistického zpracování experimentálních dat. * 1. M. Meloun, J. Militký. Statistické zpracování experimentálních dat. * 3. Meloun M., Militký J., Hill M. Počítačová analýza vícerozměrných dat v příkladech.


Chemometrics I KRP/INSZD

Type of course

Lecture + Lesson

Level of course

Mgr.


0 ZS 5 CZ


Objective The application of computer oriented statistical methods in scientific and technical fields enables not only the use of information hidden in data and the generalization of combined results from different sources, but also the creation of models, optimizations, and possible solutions. It is a multi-disciplinary movement on the frontier of the scientific disciplines of statistics and informatics, which have led to the rise of new fields such as chemometrics, biometrics, psychometrics, econometrics, technometrics and others. The goal of data processing and the level of expertise of the problems solved are always determinant factors, which affect the analysis approach and selection of methods used. Solving practical exercises allows one to better understand the limits and possibilities of various methods and to select through analogy the manner for processing one's own exercises. This is exploratory data analysis to verify the basic assumptions about data with regard to the basic data model statistical method. The construction of statistical models here is more for illustration and the interpretation of results is merely general. The comprehensive statistical data analysis approach is made therefore in the ADSTAT and QC-Expert statistics packages. The diagnosis of interactive data analysis means looking for all relations and peculiarities hidden in data. This is not possible using a standard approach without computer support. Evaluation of experimental data independently.

Prerequisities Course contents Errors in instrumental measurements. Propagation of errors in experimental operations. Exploratory data analysis. Construction and identification of an actual sample distribution. Power- and Box-Cox transformation of data. Assumptions about a sample data. Confirmatory data analysis of univariate data. Small samples analysis. Statistical hypothesis testing. Analysis of variance. Linear regression models. Regression triplet and regression diagnostics. Multivariate regression models. Calibration. Nonlinear regression models. Statistical analysis of nonlinear models. Goodness-of-fit tests. Procedure for building and testing a regression model.



Recommended reading * M. Meloun, J. Militký, Hill M. Počítačová analýza vícerozměrných dat v příkladech. Praha, 2005. * M. Meloun, J. Militký. Kompendium statistického zpracování experimentálních dat. Academia Praha, 2002. ISBN 80-200-1008-4. * M. Meloun, J. Militký. Statistické zpracování experimentálních dat. Academia Praha, 2004. ISBN 80-2001254-0.


Artifical Neural Network KRP/INUNS

Type of course

Lecture + Lesson

Level of course

Mgr.


0 LS 5 CZ

Name of lecturer * Taufer Ivan, prof. Ing. DrSc. * Doležel Petr, Ing.



Teaching methods Monologická (výklad, přednáška, instruktáž), Dialogická (diskuze, rozhovor, brainstorming), Metody samostatných

akcí, Laborování


Recommended reading * BÍLA, J. Umělá inteligence a neuronové sítě v aplikacích. 115 s. ISBN 80-01-01275-1. Praha : ČVUT, 1995. ISBN 80-01-01275-1. * FAUSETT, L.V. Fundamentals of Neural Network: Architectures, Algorithm and Applications. New Persey : Prentice Hall, 1994. * MAŘÍK, V. a kol. Umělá inteligence. ISBN 80-200-0502-1. Praha: ACADEMIE, 1993. ISBN 80-200-0502-1. * NOVÁK, M. a kol. Umělé neuronové sítě. Teorie a aplikace. 382 s.. Praha : C.H.BECK, 1998. * POKORNÝ, M. Umělá inteligence v modelování a řízení. ISBN 80-901984-4-9. Praha: BEN, 1992. * SINČÁK, P.; ANDREJKOVÁ, G. Neurónové siete. Inžiniersky prístup. 1. a 2. diel. 107 s. a 63 s. ISBN 80-8878638-X a ISBN 80-88786-42-8. Košice : elfa s.r.o., 1996. ISBN 80-88786-42-8. * ŠNOREK, M.; JIŘINA, M. Neuronové sítě a neuropočítače. Praha : ČVUT, 1996. * VONDRÁK, I. Umělá inteligence a neuronové sítě. Ostrava: VŠB - TU Ostrava, 1994. * V.-PETR,P. Umělá a výpočetní inteligence. část: Fuzzy množiny. Distančné vzdelávanie v systéme eDOCEOdistanční opora, Fakulta ekonomicko-správní, Univerzita Pardubice, Pardubice, 2004, 62s., ISBN 80 7194-670-2.. ISBN 80-88778-30-1. * ZELINKA, I. Umělá inteligence I.. Brno : VUT, 1998.


Artificial intelligence fundamentals KRP/INZUI

Type of course

Lecture + Lesson

Level of course

Mgr.


0 ZS 5 CZ

Name of lecturer * Taufer Ivan, prof. Ing. DrSc. * Škrabánek Pavel, Ing.

Objective The subjekt "Artificial inteligence" acquaints students with some branches of the artificial inteligence - the problem solution (providing a basic frame and methology of complex problems solution), the other parts are optimalization methods and genetic algorithms. The following part of the subject composes the problems of fuzzy sets, fuzzy logic, and building of fuzzy systems. The last part of the course is dedicated to expert systems. Basic orientation in the artificial inteligence problems. Ability to use optimization methods, genetic algorithms, fuzzy systems building and orientation in expert system probléme.

Prerequisities Course contents Introduction to the problems of the artificial inteligence. Basic concepts and definitions. Historical development. Problem solutions - metodology of the tree graph examination "Blind" examination - informed graph examination Optimalization tasks - problem formulation Genetic algoritms Decomposition of the complex graphs Introduction to fuzzy modelling. Basic concepts and definitions. Historical development. Fuzzy sets Tudory. Fuzzy sets mathematics. Fuzzy uncertainties. Many-valued fuzzy logic. Linguistic variables and their values. Linguistic fuzzy models. Tagaki-Sugeno models. Approximative deduction. Linguistic approximation of functions Implication. Inference algorithms. Fuzzification and defuzzyfication. Fuzzy expert systems. Program instruments for fuzzy systems modelling - MATLAB/Fuzzy Logic Toolbox Real examples of problem solution.


Assesment methods Posouzení zadané práce, Rozhovor

Recommended reading * BERKA, P. Expertní systémy. Praha: VŠE, 1998. ISBN 0-7079-873-4. * KELEMEN. J.; KUBÍK, A.; LENHARČÍK, I.; MIKULECKÝ, P. Tvorba expertních systémů v prostředí CLIPS.

Praha: GRADA Publishing, 1999. ISBN 80-7169-501-7. * KELEMEN, J.; LINDAY, M. Expertné systémy pre prax. Bratislava: SOFA, 1996. * MAŘÍK, V. a kol. Umělá inteligence. Praha: ACADEMIA, 1993. ISBN 80-200-0502-1. * NOVÁK, V. Fuzzy množiny a jejich aplikace. Praha: SNTL, 1992. * NOVÁK, V. Základy fuzzy modelování. Praha: BEN, 2000. ISBN 80-7300-009-1. * POPPER, M. KELEMEN, J. Expertné systémy. Bratislava: ALFA, 1989. ISBN 80-05-000051-0. * VYSOKÝ, P. Fuzzy řízení. Praha: ČVUT, 1996.


Professional Training - Excursion KRP/IOPEX

Type of course

Seminary

Level of course

Bc.


0 LS 2 CZ


Objective This course involves student participation in professional excursions, according to the offerings of the academic department. These credits can be given during the course of Bachelor study just for one excursion in which the student participates actively. The condition for active participation will be specified by the lecturer who will organize the particular concrete excursion. Extension of knowledge in area of Process Control.

Prerequisities Course contents Technical excursion

Teaching methods Pozorování, Demonstrace, Pracovní činnosti


Recommended reading * Literatura není zadávána.


Instruments of automatic control KRP/IPAR

Type of course

Lecture + Seminary

Level of course

Bc.


0 LS 4 CZ

Name of lecturer * Cvejn Jan, doc. Ing. Ph.D. * Havlíček Libor, Ing.

Objective A goal of this subject is to provide an overview of technical instruments and most important methods and principles in automatic control. The subject is more practically focused and connects to the subjects Automation I and Automation II, which are on the contrary focused on building up theoretical basis. The field of sensors is not covered, because it is included in another subject (Sensors and measuring non-electical quantities). The student obtains a view on the field from a practical viewpoint and will be prepared for designing control systems in practice and for studying specialized literature. The graduate obtains a view into the field from a practical viewpoint.

Prerequisities Course contents Electronical analog control loop and its components. Electronical implementation od analog PID controllers, application of operational amplifiers. Parallel and serial structure of PID controller. Analog low-pass filters. Digital control loop. A/D and D/A converters. Implementation of digital PID controller. Digital filters. Aliasing and the scan-period choice. Practical aspects of PID controllers - bumpless switching, windup effect, gain schedueling, split-range control. Discontinuous controllers and their properties. Delaying feedback. The most important actuators. DC, asynchronous and step motors. Power amplifiers, PWM modulation. Properties of control valves. Elements of pneumatic and hydraulic control systems. Instruments for logic control. Programmable logic automata. An overview of languages for logic control in compliance with norm IEC 1131-3 (RLL, SFC, FDB, STL, IL). Serial communication interface. Current loop. Industrial communication data busses - an overview and the most often used types and their basic characteristics.


Assesment methods Ústní zkouška, Písemná zkouška, Rozbor produktů pracovní činnosti studenta

Recommended reading * Balátě, J., Smutný, L. aj. Technické prostředky automatického řízení. SNTL, Praha, 1986. * Hlava J. Prostředky automatického řízení II. Skripta ČVUT FSI, Praha, 2000.. * Martinásková, M., Šmejkal, L. Řízení programovatelnými automaty. Skriptum ČVUT FSI, Praha 1998..

* Smith, C. A., Corripio, A. Principles and Practice of Automatic Process Control. 3rd edition. Wiley & Sons, 2006.. * Zezulka, F. Automatizační prostředky. Skriptum VUT,Brno, 1999..


Control and Automation KRP/IREGE

Type of course

Lecture + Lesson

Level of course

Bc.


0 LS 5 CZ


Objective The subject aim is to make students acquainted with the basic principles of control - structure of control circuit, lineartime-invariant systems, stability analysis, etc. The students acquire theoretical and practise knowledge and skills of control circuit, linear-time-invariant systems, basic types of controllers, stability analysis of systems, basics of synthesis of discrete systems.

Prerequisities Course contents Automatic process control - introduction. Dynamic systems. Types of mathematical models. Output and state description. Time-invariant systems. Linearization of the model. Steady-state value. Static characteristics. Dead time. Linear stationary one-dimensional systems. Linearity of the solution, general form of the solution. Impulse and step responses. Fourier and Laplace transforms. Basic statements about images. Simplified vocabulary of L-transform. Using L-transform for obtaining time response of linear systems. System transfer function. Standard form of the transfer function - gain, time constants, astatism. Block algebra. Feedback transfer function. Overview of the most common types of linear systems and their properties (static and astatic system of the first order, second-order systems, high-order system with a dead time). Replacement of a high-order system by the first-order system with a dead time. Automatic regulation. Open and closed control loop. Sensor and actuator transfer function. Discontinuous controllers - two-state and three-state ones. PID controller and its variants. Meaning and realization of particular components. Steady-state regulation error. Closed loop stability. Hurwitz and simplified Nyquist criterions of stability. Aplitude and phase margins. Methods of setting up PID controller parameters. Methods not requiring knowledge of the transfer function - ZieglerNichols method, setting up on the basis of the system step and frequency responses. Setting based on minimum damping. Criterions of linear and quadratic control area. Frequency-domain based design.

Teaching methods

Monologická (výklad, přednáška, instruktáž), Laborování

Assesment methods Ústní zkouška, Posouzení zadané práce, Analýza výkonu studenta

Recommended reading * Kolektiv. Automatizace a automatizační technika. 1, 2, 3, 4. Praha: Computer Press, 2000.. * Kubík, S., Kotek, Z., Razím, M., Hrušík, J., Branžovský, J. Teorie automatického řízení II. Praha: SNTL, 1982.. * Mikeš, J., Hutla, V. Teória automatického riadenia. Bratislava: ALFA/SNTL, 1986.. * Štecha, J. Optimální rozhodování a řízení.Praha, ČVUT, 2000. Praha: ČVUT, 2004. ISBN 80-01-03010-5.


Practice I KRP/IRPP

Type of course

Seminary

Level of course

null


0 LS 2 CZ


Objective Extebsion of knowletge by form of practical training experience during vacation. Student gets practical skills in the field of process control.

Prerequisities Course contents Practical training experience during vacation.

Teaching methods Pozorování, Demonstrace, Pracovní činnosti


Recommended reading * není.


Word and spreadsheet processors KRP/ITETP

Type of course

Lecture + Lesson

Level of course

Bc.


0 LS 2 CZ

Name of lecturer * Javůrek Milan, doc. Ing. CSc. * Havlíček Libor, Ing. * Honc Daniel, Ing. Ph.D. * Kotyk Josef, doc. Ing. CSc.

Objective Improve in use text editor so, to student was able to separately create paper suitable appropriate norms. Improve in use tabular editor for processing and evaluation experimental data diverse. Student regaining survey and skill at work with text editor and will prove effectively use benefit from tabular editor at to other studio on university.

Prerequisities Course contents Word - Stylies, head and foot, division, tables, drawing, diagrams, screen, synopses, indices, content, turn - ups, cross references, hypertext references. Collective correspondence, macro. Excel - figure, function, references,formatting, printing, graphs and drawing, indices, totalities, sorting, filtration Contingency tables, linking and synopses, sensitivity analysis, scenarios, Solver. Calculations using Excel, processing experimental data (basic statistics, solving nonlinear equations, solving systems linear equations, matrix calculations, regression analysis calculations). Supervequations. Import and export of data.

Teaching methods Monologická (výklad, přednáška, instruktáž), Dialogická (diskuze, rozhovor, brainstorming), Metody práce s textem (učebnicí, knihou), Demonstrace, Projekce, Nácvik dovedností


Recommended reading * Brož, M. Microsoft Office Excel 2007, Podrobná uživatelská příručka, Computer Press, 408 stran.. Computer Press, 2007. ISBN 9788025118221. * Kolektiv autorů. Office 2000 Profesional. Podrobný průvodce začínajícího uživatele.. Praha: Grada Publishing, 2000. * Mansfield, R. Word 97, Grada Publishing s.r.o., edice Profesional, 888 stran.. Praha: Grada Publishing. * Pecinovský, J., Pecinovský, R. Microsoft Word 97. Podrobný průvodce začínajícího uživatele. Grada Publishing s.r.o., 256 stran. * PECINOVSKÝ, J. Word 2007 Grada Publishing s.r.o.,123 stran. Praha: Grada Publishing.


Fundamentals of Information Technologies KRP/IZIT

Type of course

Lecture + Lesson

Level of course

Bc.


0 ZS 5 CZ

Name of lecturer * Javůrek Milan, doc. Ing. CSc. * Doležel Petr, Ing. * Škrabánek Pavel, Ing.

Objective Students obtain knowletge about software of PC, basic information about using of operation system Windows and internet. Active usage PC. Ability disposition documents conformable with norms correct writing. Processing numerical and database exercises

Prerequisities Course contents Fundamental terms from information technologies and information systems. Fundamental structure PCs ., diagram, components, peripheral equipments. Ways of memoriing of data, media, structure organization. Organization entries on media. Programmatic computer feature, types. Work with text editor Word, fundamentals production documents. Production common types documents, text formatting, work with tables, pictures. Taking of styles. Work with tabular editor Excel, his possibilities and characteristics. Processing some mathematical and database exercises. Computer networks behaviour, topology. Internet and his services. Language HTML.

Teaching methods Monologická (výklad, přednáška, instruktáž), Dialogická (diskuze, rozhovor, brainstorming), Metody práce s textem (učebnicí, knihou), Demonstrace, Projekce, Nácvik dovedností, Laborování

Assesment methods Ústní zkouška, Didaktický test

Recommended reading * Uživatelské příručky produktů MS Office. * Renda, M. Český Internet a MS Internet Explorer 5. Podrobný průvodce začínajícího uživatele. Praha: Grada Publishing, 1999. * Slánská, S.; Slánský, L. Základy informační technologie. * Taufer I., Kotyk J., Javůrek M. Jak psát a obahjovat závěrečnou práci, skripta.Univerzita Pardubice,2009.


Fundamentals of Information Technologies KRP/IZITE

Type of course

Lecture + Lesson

Level of course

Bc.


0 ZS 5 CZ

Name of lecturer * Javůrek Milan, doc. Ing. CSc. * Rozsíval Pavel, Ing.

Objective Students obtain knowletge about software of PC, basic information about using of operation system Windows and internet. Active usage PC. Ability disposition documents conformable with norms correct writing. Processing numerical and database exercises.

Prerequisities Course contents Fundamental terms from information technologies and information systems. Fundamental structure PCs ., diagram, components, peripheral equipments. Ways of memoriing of data, media, structure organization. Organization entries on media. Programmatic computer feature, types. Work with text editor Word, fundamentals production documents. Production common types documents, text formatting, work with tables, pictures. Taking of styles. Work with tabular editor Excel, his possibilities and characteristics. Processing some mathematical and database exercises. Computer networks behaviour, topology. Internet and his services. Language HTML.

Teaching methods Monologická (výklad, přednáška, instruktáž), Dialogická (diskuze, rozhovor, brainstorming), Metody práce s textem (učebnicí, knihou), Demonstrace, Projekce, Nácvik dovedností


Recommended reading * Uživatelské příručky produktů MS Office. * Renda, M. Český Internet a MS Internet Explorer 5. Podrobný průvodce začínajícího uživatele. Praha: Grada Publishing, 1999. * Slánská, S.; Slánský, L. Základy informační technologie. * Taufer I., Kotyk J., Javůrek M. Jak psát a obahjovat závěrečnou práci, skripta.Univerzita Pardubice,2009.


Automatic Control KRP/ZAC

Type of course

Lecture + Seminary

Level of course

null


0 LS 4 AN

Name of lecturer * Škrabánek Pavel, Ing. * Honc Daniel, Ing. Ph.D.

Objective Final exam consists of the oral and written part. Written part consists of five principal examples dealing with problem of Laplace trasform and inversion of Laplace transform (ordináty differential equation solution), initial and final value theorems, transfer functions algebra, controller tuning and control system stability. Capability to design control system.

Prerequisities Course contents Introduction - steady state, dynamic behaviour. Mathematical models of engineering systems. Fundamentals laws - material balances - energy balances - equation of motion. Transport equation - equation of state - equilibrium. Process dynamics - classification and definitions. Time domain dynamics - linearization and perturbation variables. Laplace domain dynamics - Laplace transform - Inversion of Laplace transforms. Initial and final value theorems. Transfer functions. Frequency domain dynamics - Nyquist plots - Bode plots - Nichols plots. Feedback control. Control instrumentation - controllers - final control elements - valves. Performance of conventional feedback control system. Controller tuning. Stability.

Teaching methods Monologická (výklad, přednáška, instruktáž)


Recommended reading * DORF, R.C., BISHOP, R.H. Modern Control Systems, Addison - Wesley, Berkeley, 1998. * LUYBEN, W.L. Process Modeling, Simulation and Control for chemical engineers, McGraw-Hill, New York, 1973.


Sensors and Measuring Systems KRP/ZSMS

Type of course

Lecture + Seminary

Level of course

null


0 ZS+LS 4 AN

Name of lecturer * Škrabánek Pavel, Ing.

Objective Final exam consists of the oral part. Every students will answer two question dealing with problems of measurements evaluation and principles of the technological variables measurement. Capability to design measuring system.

Prerequisities Course contents Introduction - Metrology - Measurement - Measurement unit. Symbols and schematic representation of measuring device. Measurement evaluation - measurement errors. Properties of measurements devices - statics and dynamics - accuracy. Measurement of technological variables. Pressure measurements. Temperature measurements. Flow measurements. Level measurements.

Teaching methods Monologická (výklad, přednáška, instruktáž)


Recommended reading * LUYBEN, W.L. Process Modeling, Simulation and Control for chemical engineers, McGraw-Hill, New York, 1973.

IAUT1. Course code

Recommend Documents