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Lecturer(s)
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Hlava Jaroslav, doc. Dr. Ing.
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Course content
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Topics of the lectures: 1. Z transform, difference equations, transfer functions of discrete time systems 2. Discrete time state space models, stability of linear discrete time systems 3. Discrete time equivalents of continuous systems 4. Digital approximation of analogue controllers, selection of sampling period 5. Pole placement design of digital controllers 6. Design of controllers with finite control time, principles of algebraic methods 7. Main ideas of Model predictive control (MPC) 8. MPC based on truncated step response model (Dynamic Matrix Control DMC) - SISO case 9. Extension of DMC to MIMO case, feedforward compensation of disturbances 10. DMC based on full numerical optimization vs simplified suboptimal implementations 11. MPC based on state space models and relationship to DMC 12. Use of state observers and Kalman filters in MPC based on state space models 13. Modifications and variants of MPC cost function, feasibility of MPC, soft constraints 14. Industrial implementations of MPC, examples of MPC applications Topics of seminars and laboratories: 1. Using mathematical formalisms of digital control 2. Using stability criteria for discrete time systems 3. Discretization of continuous systems 4. Control lops with digital controllers approximating analogue controllers 5. Effect of sampling on control loop stability, different control performance obtained with different methods for controller discretization 6. Mathematical formalisms of digital control, solution of Diophantine equations 7. Design and simulation of digital controllers based on pole placement 8. Design and simulation - algebraic methods 9. Design and simulation using unconstrained DMC 10. Constraints in DMC, numerical methods of quadratic programming 11. Application of DMC to a larger industrial case study 12. Design of MPC controller based on state space model 13. Comparison of results obtained using MPC Toolbox for Matlab vs own implementation 14. Application of MPC to a larger industrial case study
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Learning activities and teaching methods
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Monological explanation (lecture, presentation,briefing), Laboratory work
- Class attendance
- 56 hours per semester
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Learning outcomes
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This course is designed to provide the students with a good understanding of the digital control systems design and implementation. It starts with basic mathematical formalisms used for the description of discrete time systems and discrete modelling of continuous dynamic systems. It continues with classical approaches to the design of digital controllers: approximation of analogue controllers, pole placement controllers, algebraic methods for control synthesis. The main focus of this course is on model predictive control (MPC).
Students that have completed this course are able to design digital controllers for most of the control applications. They are able to select the most suitable control approach and to implement it in a given application. The range of control methods the students are able to use extends from simple digital PID to advanced multivariable model based algorithms.
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Prerequisites
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Condition of registration: none
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Assessment methods and criteria
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Combined examination, Oral exam, Written exam
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Recommended literature
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Albertos, P., Sala, A. Multivariable Control Systems. Springer Verlag,, 2004. ISBN 1852337389.
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Franklin, G., F. & Powell, J., D. & Workman, M., L. Digital Control of Dynamic Systems.. Addison - Wesley, 1998.
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Hanuš, B. - Olehla, M. - Modrlák, O. (2000) Číslicová regulace technologických procesů. Algoritmy, matematicko-fyzikální analýza, identifikace, adaptace. Brno: VUTIUM.
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Ljung, L.:. System Identification. Theory for the User. Prentice - Hall, Inc. Upper Saddle River. New Jersey, 1999.
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Rossiter, J.A. (2003), Model-based predictive control, A Practical Approach, CRC Press.
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Šulc, B. & Vítečková, M. (2004), Teorie a praxe návrhu regulačních obvodů, Praha: Vydavatelství ČVUT.
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