34437 - AUTOMAZIONE DEI PROCESSI INDUSTRIALI M (L-Z)

Learning outcomes

The course aims to provide the conceptual, methodological, and practical bases that allow to analyze and design automatic control systems of industrial plants and business processes. At the end of the course, the student is able to model, analyze and setup a controller for dynamic systems with discrete events, in the area of industrial automation. Some significant cases of industrial processes and business process will be analyzed. Finally, optimization algorithms, optimal control, and Machine Learning algorithms and their application to industrial process automation are also presented.

 

Course contents

The course addresses the main issues of industrial automation systems from both a technological and methodological perspective. It first introduces the fundamentals of dynamic system modeling for industrial automation applications, together with the tools for the analysis of continuous-time and discrete-time linear dynamic systems. The main feedback control and optimal control techniques are then presented, with particular emphasis on Linear Quadratic (LQ) control and Model Predictive Control (MPC).

Particular attention is devoted to the application of modeling and control methodologies to systems representative of industrial automation and management engineering, including electric motor motion control, inventory and warehouse flow control, and the control of dynamic marketing models.

The course also provides an overview of the technologies most widely used in industrial automation systems, with particular reference to the modeling of discrete-event systems using Petri nets and to Programmable Logic Controllers (PLCs).

The main topics covered in the course are:

• Fundamentals of dynamic system modeling for industrial automation applications;

• Continuous-time linear dynamic systems;

• Discrete-time linear dynamic systems;

• Feedback control of continuous-time and discrete-time dynamic systems;

• Linear Quadratic (LQ) optimal control;

• Model Predictive Control (MPC);

• Application of modeling and control techniques to electric motor motion control;

• Applications of optimal control to systems of interest in management engineering, including inventory and warehouse flow control and the control of dynamic marketing models;

• Modeling and control of an assembly line using Petri nets;

• Introduction to Programmable Logic Controllers (PLCs).

Readings/Bibliography

• Official course documentation supplied by the instructor

• Gerasimos Rigatos, State-Space Approaches for Modelling and Control in Financial Engineering (Systems theory and machine learning methods), Editore:Springer, 2017

• KLS Sharma, Overview of Industrial Process Automation, second edition. Elsevier LtD, 2017

• John O. Moody, Panos J. Antsaklis, Supervisory Control of Discrete Event Systems using Petri Nets, Editore: Kluwer Academic Publishers, ISBN: 0-7923-8199-8

• C. Bonivento, L. Gentili, A. Paoli, Sistemi di automazione industriale, Editore: McGraw-Hill, Anno edizione: 2011, ISBN: 88-386-6693-3

• P. Chiacchio, F. Basile, Tecnologie informatiche per l'automazione, Editore: McGraw-Hill, Anno edizione: 2004, ISBN: 88-386-6147-2
  

• Luca Ferrarini, Automazione Industriale: Controllo Logico con Reti di Petri, Editore: Pitagora Editrice, Anno edizione: 2001, ISBN: 88-371-1296-3

• Luca Ferrarini, Luigi Piroddi, Esercizi di Controllo Logico con Reti di Petri, Editore: Pitagora Editrice, Anno edizione: 2002, ISBN: 88-371-1340-4

• Pedro Larrañaga et al, Industrial Applications of Machine Learning. Editore: Chapman & Hall/CRC Data Mining and Knowledge Series

Teaching methods

In-person lectures.
Matlab/Simulink software
Excel
Generative AI tools applied to teaching

Assessment methods

COMPLETE WRITTEN EXAM: compulsory exercises and theory questions
OPTIONAL ORAL EXAM upon the student’s request
Please refer to the course page on virtuale.unibo.it for a detailed description of the above-mentioned assessment methods.

PROJECT: as an alternative to the written exam, students may complete an optional project report on a topic agreed upon with the student.

 

IMPORTANT NOTE FOR STUDENTS WITH SPECIFIC LEARNING DISABILITIES OR OTHER DISABILITIES

 

Students with specific learning disabilities or temporary or permanent disabilities are advised to contact the relevant University office well in advance (https://site.unibo.it/studenti-con-disabilita-e-dsa/en ): the office will propose any appropriate accommodations to the students concerned. These accommodations must in any case be submitted to the lecturer for approval at least 15 days in advance. The lecturer will assess their suitability, also in relation to the learning objectives of the course.

IMPORTANT NOTE 2

 

Examination in a controlled environment with prohibition of AI use

 

The use of AI tools during the examination is prohibited in any form, including chatbots, text generators, advanced translation tools, content summarisation tools, and tools integrated into electronic devices.

Any use of AI is considered a violation of the principles of honesty and fairness set out in the Policy and may result in the examination being invalidated by the lecturer, who remains responsible for assessing the validity of the examination.

This examination format applies to assessments intended to evaluate individual skills that cannot be delegated to AI, such as:
• critical understanding;
• logical argumentation skills;
• independent problem-solving;
• application of fundamental knowledge;
• personal reconstruction of procedures, methodological steps, or concepts.

As a preventive measure against possible misconduct, the following is required:

• electronic devices must be handed in or placed in a controlled airplane/offline mode;
• only materials provided by the lecturer may be used (examination sheets and examination text/formula sheet); no notes or books are permitted.

AI can be a useful tool to support individual study through further exploration, summaries, and self-assessment activities. However, with regard to the assessment of learning, the use of AI during the in-person examination is prohibited. Any use constitutes a violation of academic integrity.

Teaching tools

Computer in aula, eventuale laboratorio didattico, lavagna, proiettore, strumenti di AI

Office hours

See the website of Paolo Castaldi