ID:
142957
Tipo Insegnamento:
Opzionale
Durata (ore):
60
CFU:
6
SSD:
AUTOMATICA
Url:
INGEGNERIA INFORMATICA E DELL’AUTOMAZIONE/Percorso Comune Anno: 2
Anno:
2024
Dati Generali
Periodo di attività
Primo Semestre (19/09/2024 - 17/12/2024)
Syllabus
Obiettivi Formativi
This course is designed to provide Robotics and Automation engineers with the necessary adaptive control and data-driven approaches that are now in demand and anticipated. This course focuses on the study of nonlinear and adaptive control techniques specifically designed for complex systems.
The primary information gained will include:
- Fundamental understanding of nonlinear control and identification techniques for complex dynamic processes.
- Proficiency in analysing nonlinear dynamic systems in steady and transient states, using advanced simulation tools.
- Expertise in nonlinear control and system identification techniques.
- Thorough knowledge of nonlinear mathematical tools for analysing and controlling nonlinear dynamic systems.
- Familiarity with software simulation tools for nonlinear dynamic systems.
- Comprehensive understanding of system identification, neural networks, and fuzzy logic for control, as well as nonlinear methods for control, adaptive and learning algorithms, and nonlinear prediction and filtering tools.
- Strong foundation in simulation and control for nonlinear dynamic systems.
The fundamental acquired skills, which refer to the ability to effectively use the information that has been obtained, will include:
- Analysing the behaviour of nonlinear systems under steady and dynamic conditions;
- Developing nonlinear dynamic and adaptive controllers for a specific nonlinear dynamic system to satisfy appropriate transient and steady-state limitations;
- Selecting the most appropriate system identification algorithm for designing intelligent control solutions that utilise fuzzy systems, neural networks, adaptive systems, nonlinear filters, adaptive schemes, and purely nonlinear prototypes;
- Utilising simulation numerical programmes to evaluate nonlinear systems.
The primary information gained will include:
- Fundamental understanding of nonlinear control and identification techniques for complex dynamic processes.
- Proficiency in analysing nonlinear dynamic systems in steady and transient states, using advanced simulation tools.
- Expertise in nonlinear control and system identification techniques.
- Thorough knowledge of nonlinear mathematical tools for analysing and controlling nonlinear dynamic systems.
- Familiarity with software simulation tools for nonlinear dynamic systems.
- Comprehensive understanding of system identification, neural networks, and fuzzy logic for control, as well as nonlinear methods for control, adaptive and learning algorithms, and nonlinear prediction and filtering tools.
- Strong foundation in simulation and control for nonlinear dynamic systems.
The fundamental acquired skills, which refer to the ability to effectively use the information that has been obtained, will include:
- Analysing the behaviour of nonlinear systems under steady and dynamic conditions;
- Developing nonlinear dynamic and adaptive controllers for a specific nonlinear dynamic system to satisfy appropriate transient and steady-state limitations;
- Selecting the most appropriate system identification algorithm for designing intelligent control solutions that utilise fuzzy systems, neural networks, adaptive systems, nonlinear filters, adaptive schemes, and purely nonlinear prototypes;
- Utilising simulation numerical programmes to evaluate nonlinear systems.
Prerequisiti
The following concepts and the knowledge provided by the course of “Fundamentals of Automatic Control” or “Automatic Control” are mandatory:
- basic concepts of mathematics, differential and integral computation;
- knowledge of the basic concepts of Physics;
- knowledge of dynamic systems, their behaviour, and their practical application; methods to analyse dynamic systems in steady and transient states;
- knowledge of the frequency tools for the analysis of dynamic systems;
- ability to analyse and design digital systems.
- basic concepts of mathematics, differential and integral computation;
- knowledge of the basic concepts of Physics;
- knowledge of dynamic systems, their behaviour, and their practical application; methods to analyse dynamic systems in steady and transient states;
- knowledge of the frequency tools for the analysis of dynamic systems;
- ability to analyse and design digital systems.
Metodi didattici
The course is organised as follow:
- 45 hours of lectures on all the course’s topics;
- 15 hours of practical exercises in the Informatics Laboratory concerning the analysis and the simulation of nonlinear dynamic systems.
After the guided tutorials the students will have free access to the computer laboratories for additional individual tests and hands-on.
- 45 hours of lectures on all the course’s topics;
- 15 hours of practical exercises in the Informatics Laboratory concerning the analysis and the simulation of nonlinear dynamic systems.
After the guided tutorials the students will have free access to the computer laboratories for additional individual tests and hands-on.
Verifica Apprendimento
The aim of the exam is to verify at which level the learning objectives previously described have been acquired.
The examination is divided in 2 sections that will take place in the same day.
- A project regarding the simulation and the control design for a nonlinear system by using the Matlab and Simulink environments, which aims at understanding if the student has the skills in the analysis and the synthesis of a complex process. To pass this test it is required to get at least 18 points out of 30. The time allowed for this test is 1.5 hours. It is allowed consulting the Matlab and Simulink programme manual only;
- One test (30 multiple choice questions) based on all the topics tackled in the class or on the basic concepts of the course, with the aim of evaluating how deeply the student has studied the subject and how he is able to understand the topics analysed. To pass this test it is required to get at least 18 points out of 30. The time allowed for this test is 0.5 hour. It is not allowed consulting any textbook or using any PC, smart phone, calculator..
The final mark is the sum of the 2 marks. To pass the exam it is necessary to get at least 18 points out of 31. If the first test fails or if the final mark is below 18, it is necessary to repeat all the exam’s sections.
Passing the exam is proof of having acquired the ability to apply knowledge and the required skills defined in the course training objectives.
Note finally that also the examination is held in English.
The examination is divided in 2 sections that will take place in the same day.
- A project regarding the simulation and the control design for a nonlinear system by using the Matlab and Simulink environments, which aims at understanding if the student has the skills in the analysis and the synthesis of a complex process. To pass this test it is required to get at least 18 points out of 30. The time allowed for this test is 1.5 hours. It is allowed consulting the Matlab and Simulink programme manual only;
- One test (30 multiple choice questions) based on all the topics tackled in the class or on the basic concepts of the course, with the aim of evaluating how deeply the student has studied the subject and how he is able to understand the topics analysed. To pass this test it is required to get at least 18 points out of 30. The time allowed for this test is 0.5 hour. It is not allowed consulting any textbook or using any PC, smart phone, calculator..
The final mark is the sum of the 2 marks. To pass the exam it is necessary to get at least 18 points out of 31. If the first test fails or if the final mark is below 18, it is necessary to repeat all the exam’s sections.
Passing the exam is proof of having acquired the ability to apply knowledge and the required skills defined in the course training objectives.
Note finally that also the examination is held in English.
Testi
Teacher’s handouts and slides are only required for the exam and the course preparation. They are available from the personal web page of the teacher: www.silviosimani.it/lessons.html
Specific topics can be further developed in the following textbooks, which are not fundamental for the exam nor the course preparation:
- "Applied nonlinear control", J.J. Slotine, W. Li. - Prentice Hall, 1991.
- "A course in fuzzy systems and control", L.-X. Wang - Prentice Hall, 1997.
- "Neural Networks for Identification, Prediction, and Control", D.T. Pham and X. Liu - Springer Verlag, 1995.
- "Model-based Fault Diagnosis in Dynamic Systems Using Identification Techniques", S. Simani, C. Fantuzzi, R. J. Patton - Springer, 2003.
Note finally that course lectures, its lecture notes, and handouts are provided in English.
Specific topics can be further developed in the following textbooks, which are not fundamental for the exam nor the course preparation:
- "Applied nonlinear control", J.J. Slotine, W. Li. - Prentice Hall, 1991.
- "A course in fuzzy systems and control", L.-X. Wang - Prentice Hall, 1997.
- "Neural Networks for Identification, Prediction, and Control", D.T. Pham and X. Liu - Springer Verlag, 1995.
- "Model-based Fault Diagnosis in Dynamic Systems Using Identification Techniques", S. Simani, C. Fantuzzi, R. J. Patton - Springer, 2003.
Note finally that course lectures, its lecture notes, and handouts are provided in English.
Contenuti
The course is comprised of a total of 60 hours of instructional activities, which are split into 45 hours of frontal lectures and 15 hours of guided tutorials in the computer labs.
Specifically, the following subjects will be thoroughly examined and explored:
- Overview. Systems that are adaptive and intelligent. This text describes the essential tools and diverse strategies used to understand, analyse, depict, and combine complicated physical processes. An overview of adaptive systems and the notion of adaptation.
- System identification of dynamic systems. The topics covered include parametric and nonparametric identification methods, recursive algorithms for linear system identification, mathematical tools for online models and nonlinear dynamic system identification, adaptive identification and control techniques, and adaptive PID and classical controllers.
- Application of Fuzzy Logic in Control Systems. The text discusses the definitions and principles of fuzzy logic, as well as fuzzy model identification. It also explores the use of fuzzy logic in control systems, specifically focusing on automated learning and adaptation for fuzzy models. The concept of adaptive fuzzy control is introduced, along with the ANFIS tool, which stands for Adaptive Neuro Fuzzy Inference System.
- Artificial neural networks. Basic principles and characteristics. This course covers algorithms for supervised and unsupervised learning, specifically focusing on their application in identifying and controlling dynamic systems. It also explores stochastic search algorithms, recurrent neural networks, adaptive neural networks, and convolutional neural networks for identification and control. Additionally, the course delves into the design of adaptive neural controllers using the Model Reference Adaptive Control (MRAC) principle.
- Computer Aided Hands-on refers to the use of computer-aided design. Engaging in the practical exploration of identifying nonlinear dynamic systems, using fuzzy logic for control, implementing neural networks, and designing adaptive control strategies.
Specifically, the following subjects will be thoroughly examined and explored:
- Overview. Systems that are adaptive and intelligent. This text describes the essential tools and diverse strategies used to understand, analyse, depict, and combine complicated physical processes. An overview of adaptive systems and the notion of adaptation.
- System identification of dynamic systems. The topics covered include parametric and nonparametric identification methods, recursive algorithms for linear system identification, mathematical tools for online models and nonlinear dynamic system identification, adaptive identification and control techniques, and adaptive PID and classical controllers.
- Application of Fuzzy Logic in Control Systems. The text discusses the definitions and principles of fuzzy logic, as well as fuzzy model identification. It also explores the use of fuzzy logic in control systems, specifically focusing on automated learning and adaptation for fuzzy models. The concept of adaptive fuzzy control is introduced, along with the ANFIS tool, which stands for Adaptive Neuro Fuzzy Inference System.
- Artificial neural networks. Basic principles and characteristics. This course covers algorithms for supervised and unsupervised learning, specifically focusing on their application in identifying and controlling dynamic systems. It also explores stochastic search algorithms, recurrent neural networks, adaptive neural networks, and convolutional neural networks for identification and control. Additionally, the course delves into the design of adaptive neural controllers using the Model Reference Adaptive Control (MRAC) principle.
- Computer Aided Hands-on refers to the use of computer-aided design. Engaging in the practical exploration of identifying nonlinear dynamic systems, using fuzzy logic for control, implementing neural networks, and designing adaptive control strategies.
Lingua Insegnamento
INGLESE
Altre informazioni
To contact the professor, students can use their email addresses included in this document.
The professor will publish the teaching materials (slides, assignments, etc.) they use throughout the course in the
personal website of the subject.
http://www.silviosimani.it/lessons.html
Additionally, the professor will use the emailing list of the subject to announce key events
and provide relevant information on the subject.
The Google Classrooom code is available from the instructor's personal course webpage:
http://www.silviosimani.it/lessons.html
The professor will publish the teaching materials (slides, assignments, etc.) they use throughout the course in the
personal website of the subject.
http://www.silviosimani.it/lessons.html
Additionally, the professor will use the emailing list of the subject to announce key events
and provide relevant information on the subject.
The Google Classrooom code is available from the instructor's personal course webpage:
http://www.silviosimani.it/lessons.html
Corsi
Corsi
INGEGNERIA INFORMATICA E DELL’AUTOMAZIONE
Laurea Magistrale
2 anni
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