Control Engineering

ANTONIO PIETRABISSA

General objectives

The course aims at providing basic concepts and methodologies related to the most widely used control methodologies in the framework of process automation and at applying them in the industrial contexts, suitably modeled as complex and heterogeneous processes that are interconnected through appropriate material transport and communication infrastructures.

Specific objectives

Knowledge and understanding:
The students will learn methodologies for the robust control of linear time-delay systems, Internal Model Control and Model Predictive Control with specific reference to process control problems.

Apply knowledge and understanding:
Students will be able to design robust controllers for process automation equipment, e.g., to achieve robust tuning of PID controllers, and to apply industrial Model Predictive Control algorithms.

Critical and judgment skills:
The student will be able to choose the most suitable control methodology for a specific process control problem starting from its state-space model or from its transfer-function model.

Communication skills:
The course activities allow the student to be able to communicate and discuss the main problems concerning process control and the possible design choices for their solutions in terms of control laws.

Learning ability:
The aim of the course is to make the students aware on how to deal with control problems in the context of process automation.

General objectives

The course aims at providing basic concepts and methodologies related to the most widely used control methodologies in the framework of process automation and at applying them in the industrial contexts, suitably modeled as complex and heterogeneous processes that are interconnected through appropriate material transport and communication infrastructures.

Specific objectives

Knowledge and understanding:
The students will learn methodologies for the robust control of linear time-delay systems, Internal Model Control and Model Predictive Control with specific reference to process control problems.

Apply knowledge and understanding:
Students will be able to design robust controllers for process automation equipment, e.g., to achieve robust tuning of PID controllers, and to apply industrial Model Predictive Control algorithms.

Critical and judgment skills:
The student will be able to choose the most suitable control methodology for a specific process control problem starting from its state-space model or from its transfer-function model.

Communication skills:
The course activities allow the student to be able to communicate and discuss the main problems concerning process control and the possible design choices for their solutions in terms of control laws.

Learning ability:
The aim of the course is to make the students aware on how to deal with control problems in the context of process automation.

Prerequisites:
Basic knowledge of systems theory.
Basic knowledge of control theory.

There are no prerequisite exams.

Introduction to process control. (10h)
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.

Classic process control. (25h)
Internal Model Control (IMC). Robust control. Robust PID tuning with IMC. Time-delay systems. Time margin. Smith Predictor. Robustness to time delay mismatches. Robust PID tuning in presence of delays with IMC.

Model Predictive Control. (25h)
Introduction to Model Predictive Control. The Model Predictive Control (MPC) principle. Relevance of MPC in current industrial process automation. Basic notions about Quadratic 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. MPC and Optimal Control.

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 lecture notes by A. Pietrabissa available via the website.

Reference bibliography:

Process control
T. F. Edgar, J. Hahn, “Process Automation”, in Handbook of Automation, 2009
D.E. Seborg et al., Process Dynamics and Control (3rd ed.), 2009

Classic process control
Braatz, R. D. (1995). Internal model control. In The Control Handbook (W. S. Levine, ed.) CRC Press, pp. 215-224
Rivera, Daniel E. "Internal model control: a comprehensive view." Arizona State University (1999).
Morari, M. & Zafiriou, E. (1989). Robust Process Control. Prentice Hall, Englewood Cliffs, New Jersey

Model Predictive Control
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.
K. Basil and M. Cannon, "Model predictive control", Switzerland: Springer International Publishing, 2016

The course is taught by using the blackboard and/or slides depending on the topic.
If it is not possible to carry out the lessons in the classroom with all the students due to the pandemic, the lessons are carried out in mixed mode (in the classroom with streaming for students who use remotely) or exclusively in streaming if no student can attend. The instructions are indicated on the course page (https://sites.google.com/a/dis.uniroma1.it/antonio-pietrabissa/teaching/process-automation)

Attendance of the course is optional.

The written test consists of one exercise on Internal Model Control, one on Model Predictive Control and in some open-ended questions on the course programme.
Indicatively, each exercise accounts for one third of the evaluation; the other third depends on the questions.
The oral test consists of the written test discussion with possible insights. The final grade will take into account the written grade and the evaluation of the answers to the questions of the oral exam.

Discuss why the Model Predictive Control (MPC) methodology may improve the safety of plants.