- Department of Instrumentation and Control Engineering
The goal of this course is to explain in more detail the basic knowledge of automatic control theory - identification, modelling, control and simulation. The course also includes practical training on control systems with Matlab/Simulink software which is a common platform of control engineers.
Analytical methods of system identification;
Basic experimental methods of system identification by using the development of empirical dynamic models from step response data;
Process control and block diagrams;
Dynamic versus steady-state models;
Transfer functions (continuous/discrete time systems);
Minimum/ non-minimum phase transfer function;
Frequency response analysis;
Sampling and discretization for linear systems;
Stability of linear feedback systems;
Feedback and feedforward control;
Internal model control;
Tuning multiloop systems;
Decoupling control Systems;
- Syllabus of lectures:
1.-2. Methods of system identification, black/white/gray box approach, analytical methods of system identification (modelling of mechanical, electro-technical, hydraulic and thermo-mechanic systems).
3.-4. Experimental methods of system identification. Types of input signals. Nonparametric methods. Step response. Model parameterization by using the development of empirical dynamic models from step-response data.
5.-6. Process control and block diagrams, dynamic versus steady-state models,
transfer functions (continuous/discrete time systems).
7. Frequency response analysis using the Bode and the Nyquist diagrams.
8. Sampling and discretization for linear systems
9. Stability of linear feedback systems and the stability criteria (Routh, Nyquist, Bode, Michailov); The Möbius bilinear transform; Minimum/non-minimum phase transfer functions.
10. Controller design (Ziegler and Nichols method, direct synthesis method, frequency response techniques, method based on integral error criteria, trial and error tuning, &#197;sträm and Hägglund method).
11. Advanced Control Techniques (internal model control and time-delay compensation using the Smith predictor technique).
12.-13. Control of MIMO systems (interactions, pairing of controlled and manipulated variables, tuning multiloop control systems and decoupling).
14. Supervisory control (basic requirements, implementation and applications)
- Syllabus of tutorials:
1.-2. Analytical identification of technological processes. For examples a model of mechanical system, an electrical circuit and a hydraulic system.
3.-4. Experimental identification with work on development of empirical dynamic models from step response data, simulation and verification with Matlab software).
5. Tasks on steady characteristics and mathematical descriptions of systems
6. Transfer function computation of complex systems, step/ impulse responses and computer modelling of continuous/discrete systems,
7. Tasks on frequency domain analysis.
8. Tasks on sampling and discretization of linear systems.
9. Lab work on stability and minimum/non-minimum phase systems with Matlab software.
10.-11. Lab work on controller design methods and simulation with Matlab/Simulink software.
12. Examples of advanced control techniques (internal model control, time-delay compensation using the Smith predictor technique) and simulation of controlled circuits with Matlab/Simulink software.
13. Example of control of MIMO systems
14. Assessment and consultati
- Study Objective:
The goal of this study is to explain the essentials of process control and to provide the fundamental concepts and practical tools needed by control engineers.
- Study materials:
Zítek, P. - Hofreiter, M. - Hlava, J.: „Automatické řízení“, 2006, ČVUT
Hofreiter, M. a kolektiv : „ Příklady a návody z automatického řízení“, 2006, ČVUT
Hofreiter, M.: „Identifikace systémů I“, 2009, ČVUT
Hofreiter, M.: „Demonstrované aktuální nástroje návrhu automatického řízení pro praxi“, 2009, DIMART
- Further information:
- No time-table has been prepared for this course
- The course is a part of the following study plans: