Academic Year 2021/2022

  • Docente: Paolo Castaldi
  • Credits: 6
  • SSD: ING-INF/04
  • Language: Italian
  • Teaching Mode: Traditional lectures
  • Campus: Forli
  • Corso: Second cycle degree programme (LM) in Mechanical Engineering (cod. 8771)

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 processes. The course, therefore, includes the deepening of the techniques of modelling, analysis and control of dynamic systems with discrete events, essential for the study of industrial automation problems. In addition, some significant cases of industrial processes and plants will be analyzed. Finally, the most advanced techniques based on Machine Learning and Neural Networks, characterizing Industry 4.0, of Condition Monitoring and Predictive Maintenance are illustrated, as well as the discrete-time techniques of Digital Control for the (auto)tuning of Industrial DIGITAL PID regulators

Course contents



  • From Industrial Revolution to Industry 4.0
  • Classification of plants, processes and control systems
  • Types and main components of production lines
  • Supervision, control, monitoring
  • Production plants and their automation problems
  • Computer Integrated Manufactoring (CIM)
  • Pyramidal Model of a CIM system: field, control, supervision, planning and Management 
  • Field, procedural and coordination control




Modelling of discrete events dynamic systems by Petri Nets

  • Discrete event dynamic systems: definitions and properties
  • Petri net modelling of dicrete event systems: places, tranitions, marking, Petri Net graph
  • Dynamic of Petri nets
  • Simulation of Petri Net by WoPed and PIPE 2.0 software
  • Modelling of Industrial Processes by Petri Nets:
  1. Model of producer/consumer system
  2. Model of a client/server system
  3. Model of robot based production
  4. Implementation and Simulation of examples above mentioned in WoPeD and PIPE 2.0


Analysis and Control of Petri nets


  • Analysis of discrete event dynamic systems with Petri nets
  • Vividness, limitedness, reversibility
  • Reachability and coverage tree and graph
  • Example of supervision and control
  1. Modelling and control of industrial handling system based on autonomous vehicle
  • Introduction to the implementation of Petri nets in code




Digital Control Systems

  • Discrete-time PID industrial regulators
  • Z-transform design methods
  • Real time control and multi-loop control
  • Adaptive DIGITAL PIDs


Machine Learning for Condition Monitoring and Predictive Maintenance of Industrial Plants

  • Application of Machine Learning to Industrial Automation
  • Condition Monitoring (CM) and IoT Predictive Maintenance as part of the Smart Factory
  • Sensorization to obtain Big Data of the systems for the CM the PM: measurement in real time mechanical, electronic, electrical data, data on wear, overheating and consumption.
  • Application to Big Data of Machine Learning algorithms for CM and MPI: Fault Detection and Isolation, Remaining Useful Life (RUL) prediction
  • Fault tolerant Control: mitigation of fault effects


Application of Machine Vision to Industrial Systems

  • Machine Vision and“Imaging Transformations
  • Multi Cameras-Based Visual Servoing for Industrial Robots
  • Distributed Filtering for Sensorless Control


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

Teaching methods

Frontal Lessons, laboratory, industry didactic visits.

Assessment methods

Oral Colloquium. Optional project on a topic agreed with the student.

The oral colloquiom could be taken also on-line by TEAMS or ZOOM platform.

Teaching tools

Computer, laboratory, didactic visit to local industry

Office hours

See the website of Paolo Castaldi


Decent work and economic growth Industry, innovation and infrastructure Responsible consumption and production

This teaching activity contributes to the achievement of the Sustainable Development Goals of the UN 2030 Agenda.