SUPERVISION AND ADAPTIVE SYSTEMS
Academic year and teacher
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- Versione italiana
- Academic year
- 2022/2023
- Teacher
- SILVIO SIMANI
- Credits
- 6
- Didactic period
- Primo Semestre
- SSD
- ING-INF/04
Training objectives
- This represents an advanced course of Automatic Nonlinear Control and Supervision techniques for complex systems, and it studies advanced elements of a control and fault diagnosis system from the dynamic point of view, by considering nonlinear dynamic processes from their input-state-output and input-output points of view.
This course thus aims at presenting those control and supervision methodologies currently required and expected by modern industries and practical application activities.
The main goal of the course consists of providing advanced topics and tools for the study, supervision, fault diagnosis and control of complex dynamic systems, as well as their interconnections under proper design constrains imposed by cost, speed, computational cost, robustness, reliability, sustainability, and power consumption.
The main acquired knowledge will be:
- basic nonlinear control and supervision techniques for complex processes from a dynamic point of view, considering the information from its input to its output variables;
- knowledge related to the analysis of nonlinear dynamic systems in steady and transient states and their advanced simulation tools;
- knowledge of nonlinear blocks and control techniques;
- knowledge of the tools to tackle the study of complex systems and of their interconnections under the constrains imposed by performances in terms of cost, speed, computational cost, robustness and power consumption;
- knowledge of the nonlinear mathematical tools for the analysis of nonlinear dynamic systems;
- basic knowledge of nonlinear dynamic system software simulation tools;
- fundamentals about neural networks, fuzzy logic and systems, nonlinear methods for control, adaptive control, supervision and fault diagnosis, basics of optimization methods and tools, nonlinear prediction and filtering tools;
- fundamentals of simulation and control for nonlinear dynamic systems.
The basic acquired skills (that are the capacity of applying the acquired knowledge) will be:
- analysis of the behaviour of nonlinear systems in steady and dynamic conditions;
- design of nonlinear dynamic supervision and controller for a given nonlinear dynamic system in order to meet proper transient and steady state constraints;
- design the most suitable supervision and control solutions by means of fuzzy systems, neural networks, adaptive systems, nonlinear filters, adaptive schemes, and purely nonlinear prototypes;
- identification of the most suitable nonlinear elements, as well as the most suitable parameters for a specific control design and its application;
- use of simulation numerical programs to analyse nonlinear systems. Prerequisites
- 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. Course programme
- The course consists of 60 hours of teaching activities divided in frontal lectures (45 hours) and guided tutorial in the computer laboratories (15 hours).
In more detail, the following topics will be analysed and investigated:
- Introduction. Adaptive, intelligent, autonomous, distributed, embedded and cyber-physical systems. Key tools and multidisciplinary techniques for the comprehension, analysis, representation and synthesis of complex physical phenomena. Introduction to adaptive systems and adaptation theory.
- Basics of optimization tools. Constrained and constrained optimization; gradient method; Lagrange scheme; stochastic approaches; genetic algorithms.
- Dinamic System Identification. Parametric and nonparametric identification; recursive algorithms for linear system identification; on-line models and mathematical tools for nonlinear dynamic system identification; adaptive identification and control approaches; adaptive PID and classical controllers.
- Fuzzy Logic for Control. Definitions and properties of fuzzy logic; fuzzy model identification; fuzzy logic for control; fuzzy control: automatic learning and adaptation for fuzzy models; adaptive fuzzy control; ANFIS tool – Adaptive Neuro Fuzzy Inference System.
- Neural Networks. Fundamentals and properties. Algorithms for supervised and unsupervised learning, with application to identification and control of dynamic systems; stochastic search algorithms; recurrent neural networks; adaptive neural networks; convolutional neural networks for identification and control; design of the adaptive neural controller by means of Model Reference Adaptive Control (MRAC) principle.
- Supervision, fault diagnosis and “Sustainable” Control. Physical or hardware redundancy principle. Software redundancy. Design of the residual generator; evaluation of the residual function. Fault diagnosis schemes. Fault detection, isolation and identification. Condition monitoring. Active and passive approaches to disturbance compensation. Adaptive supervision. Sustainable and fault tolerant control solutions. Predictive and preventive maintenance.
- Computer Aided Hands-on (computer-aided design). Hands-on with identification of nonlinear dynamic systems, fuzzy logic for control, neural networks, design of supervision and fault diagnosis schemes. Didactic methods
- 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. Learning assessment procedures
- 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 22. The time allowed for this test is 1.5 hours. It is allowed consulting the Matlab and Simulink programme manual only;
- One test (8 open and multiple choice questions) based on all the topics tackled in the class or on the basic concepts of the following course: “Supervision and Adaptive Systems”, 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 1 points out of 8. 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 point 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 the examination can be held in English. Reference texts
- 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:
- Controllo Ottimo e Stima Ottima: "Progetto di sistemi di controllo", M. Tibaldi. - 2. ed. - Pitagora, 1995.
- "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 lecture notes and handouts are provided in English.
