Sistemi di Automazione
Automation Systems Silvia Maria Zanoli
Basic definitions and concepts of Discrete Event Systems - Basic concepts of Petri Nets
KNOWLEDGE AND UNDERSTANDING:
The course aims to illustrate to students the advanced methods of modeling and control of an Automated Production System (a discrete event system, complex, distributed, random) in order to predict / evaluate its performance under real, and therefore uncertain, operating conditions. Models introduced to this end are Markov chains, Stochastic Petri Nets (SPN) and Generalized SPN (GSPN). In addition, students will learn the most important techniques and advanced control architectures in the industry, highlighting the implementation issues. CAPACITY TO APPLY KNOWLEDGE AND UNDERSTANDING:
Students will be able to model a discrete event timed system from examples of real systems, motivating any modeling choices made, and to propose appropriate criteria for evaluating the performance of the tested system. In the application of advanced control techniques it is required to solve the main implementation problems. These capabilities will originate through a series of vocational skills, such as: 1. the ability to critically analyze the results of modeling and / or design; 2. the ability to organize work into subtasks and the coordination of individual activities, working in a team with other elements involved in the problem resolution.TRANSVERSAL SKILLS:
Participation in working groups for the development of an adequate modeling of an advanced production system and the drafting of a final report for the analysis of performance, will help improve the degree of independence of judgment in general, together with the ability of communication which derives from group activities; in addition the students learning autonomy and his/her capability of drawing conclusions will be stimulated; finally, the student will improve his/he skill on writing of technical reports.
Concepts of production systems and production processes. Automation production systems and their classification. Production equipment. Process and manufacturing productions automation. Principal performance indexes. DCS systems.
Discrete Event System Modelling and Analisys:
- Time in Petri Nets, Petri Nets with priority, PN states, Enabling of transitions;
- Brief notes on Markov Process and on generalyzed Markov processes (GSMP). Continuous time and discrete time Markov Chains
- Stocastic Petri Nets and Generalized Petri Net and their relationship with Markov Chain. Modeling and analisys of industrial cases.
Use of modeling SW for GSPN.
Students, organized into working groups, will be required to propose examples of real systems to refine their modeling capabilities of timed discrete event systems. Such examples will be discussed in class with the teacher.
As regards the "time-driven" control will be described:
- Advanced control architectures based on industrial PID, highlighting the implementation issues
Development of the examination
LEARNING EVALUATION METHODS
The assessment of students' level of learning is performed by means of two tests to evaluate the theoretical skills (written and oral test) and a practical test of modeling and analysis of the performance of a system by means of timed discrete event stochastic models. A written report of the practice test is required. Students that in the practical test have demonstrated sufficient expertise and clarity and precision in the report are exempted from the written test.
The written and practical test are preparatory to the oral test. In the event of a negative outcome for the oral exam, the student must repeat the written test.
LEARNING EVALUATION CRITERIA
The evaluation the learning takes into account the results of verification tests / learning measurements and skills acquired and the ability to overcome any deficiency encountered by the results of the tests.
LEARNING MEASUREMENT CRITERIA
The measure of learning through written test is intended to assess the modeling skills on stochastic dynamic systems and the knowledge on control techniques with advanced industrial controllers.To perform the written test, if required, a time limit is given. The measure of learning through oral test is designed to test the comprehension of the topics covered in the course deepening practical applications. The measure of learning through the design activity is designed to test the ability of modeling of stochastic timed discrete event systems and the use of the tools of analysis and synthesis of such systems. The written test, if required, and the practical are in preparation for the oral exam. The tests are carried out of thirty.
FINAL MARK ALLOCATION CRITERIA
In order to pass the exam with the minimum score,equal to eighteen, the student must have sufficient knowledge of all the topics of the course. Additional points will be awarded by demonstrating in-depth knowledge of the content of the course in the written and oral tests together with good autonomy in setting and solving proposed problems. The "lode" is given to students who, having done all the tests correctly and completetly, have demonstrated a particular brilliance in the oral and in the preparation of written assignments and in the design activity
-Lecture notes (scaricabili dal sito moodle del corso)
- Ajmone Marsan M. et alii: Modelling with Generalised Stochastic Petri Nets John Wiley, 1994
For further readings the following texts are recommended:
- Carlucci D., Menga G. Teoria dei Sistemi ad eventi discreti .UTET, Torino (1998),Collana UTET università
- Cassandras,C.G., Lafortune S. Introduction to Discrete Event Systema (Cap 8), Kluwer Academic Pub., 2008.
- GianAntonio Magnani, Tecnologie dei sistemi di controllo, McGraw-Hill
- Ingegneria Informatica e dell'Automazione (Corso di Laurea Magistrale (DM 270/04))