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1041422 - PROCESS AUTOMATION (Master in Control Engineering, year I) - Number of ECTS credits: 6
Language of instruction: English
Comunications
Comunications
[Monday 23 March 2024] No lessons (election day)
[Monday 23 February 2024] First lessons, a.y, 2025-2026
Course material and lesson diary
Course material and lesson diary
The course material and the lesson diary can be found on the Moodle platform of Sapienza (https://elearning.uniroma1.it/) within the course "Process Automation"
Course schedule
Course schedule
Monday, 11:00-13:00, Room A6, DIAG, via Ariosto 25
Thursday, 11:00-14:00, Room A7, DIAG, via Ariosto 25
Office hour
Office hour
Refer to the Teaching page
Exams
Exams
June
July
September
October (for students enrolled on supplementary years of studies)
January
February
March/April (for students enrolled on supplementary years of studies)
Course objectives
The course aims at providing basic concepts and methodologies related to the most used control methodology in the framework of process control, and at applying them to suitably modelled industrial process examples.
Expected learning results
The students will be able to manage basic Internal Model Control and Model Predictive Control methodologies with specific reference to process control problems.
Course program
Process Control Overview. Significance of process control. Objectives. Levels of Process Control. Process Dynamics and Mathematical Models. Regulatory Control. Control System Design. Multivariable Control. Batch Process Automation. Automation and Process Safety.
Dynamic models for process control.
Classic process control. Internal Model Control (IMC). PID tuning with IMC. Time-delay systems. Delay margin. Smith Predictor. Robustness to time delay mismatches.
Introduction to Model Predictive Control. The Model Predictive Control (MPC) principle. Relevance of MPC in current industrial process automation. Basic notions about Linear Programming and Dynamic Programming.
Model Predictive Controllers. MPC elements: prediction model, objective function, control law. MPC algorithms: Dynamic Matrix Control, Model Algorithmic Control, Predictive Functional Control. State space formulation.
Stability of Model Predictive Control.
Prerequisites
Basics of system and control theory.
Reference texts:
Dale E. Seborg, Thomas F. Edgar, Duncan A. Mellichamp, "Process Dynamics and Control", Wiley, 2nd ed., 2003, 736 p., ISBN 978-0471000778.
Rivera, Daniel E. "Internal model control: a comprehensive view." Arizona State University (1999).
Eduardo F. Camacho, Carlos Bordons Alba, “Model Predictive Control”, Series: Advanced Textbooks in Control and Signal Processing, XXII, 2nd ed. 2004, 405 p., ISBN 978-0-85729-398-5.
Slides and notes by A. Pietrabissa (https://elearning.uniroma1.it/).
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