Intelligent Control Systems

Intelligent Control Systems. 20 Hours Course for the National Ph.D. Program in Autonomous Systems - DAUSY

Google Meet Link (remote connection for online lectures): meet.google.com/cid-xmdz-bez

Course Enrollment

Please use the following link for sending your contact details to the instructor and enrol in the course (Prof. Silvio Simani, Department of Engineering, University of Ferrara. Email: silvio.simani@unife.it). In case of problems with the link, don't hesitate to get in touch with the instructor directly by email.

Required Background

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.

Main Goals

This represents an advanced course oriented to the design of intelligent (data-driven and model-free) control solutions for complex (nonlinear dynamic) systems, and it proposes advanced elements for the design of complex control schemes from the dynamic behaviour, by considering nonlinear dynamic processes from their input-output point of view. This course thus aims at presenting those control 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 analysis, simulation, and design of advanced control methodologies for complex dynamic systems, as well as their interconnections under proper design constraints imposed by cost, speed, computational cost, robustness, reliability, sustainability, and power consumption.

Objectives and Knowledge

Basic nonlinear control and identification 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 system identification techniques;
Knowledge of the tools to tackle the study of complex systems and their interconnections under the constraints imposed by performances in terms of cost, speed, computational cost, and robustness;
Knowledge of the nonlinear mathematical tools for the analysis of nonlinear dynamic systems;
Fundamentals about system identification, neural networks for control, nonlinear methods for control, adaptive and learning algorithms, basics of optimization methods and tools, nonlinear prediction and filtering tools;
Fundamentals of control for nonlinear dynamic systems.

Main Skills

Analysis of the behaviour of nonlinear systems in steady and dynamic conditions;
Design of nonlinear dynamic and adaptive controllers for a given nonlinear dynamic system to meet proper transient and steady-state constraints;
Design the most suitable system identification algorithm for the design of intelligent control solutions using neural networks, adaptive systems, nonlinear filters, adaptive schemes, and purely nonlinear prototypes;
Identification of the most suitable process model, as well as the most suitable parameters for a specific control design and its application, given its model prototype;

General Programme

Introduction. Adaptive, intelligent, autonomous, distributed, embedded and cyber-physical systems. Key tools and multidisciplinary techniques for comprehending, analysing, representing and synthesising complex physical phenomena. Introduction to adaptive systems and adaptation theory for control.
Basics of optimisation tools. Constrained and constrained optimisation; gradient method; stochastic approaches.
Dynamic system identification. Parametric and nonparametric identification; recursive algorithms for linear system identification; online models and mathematical tools for nonlinear dynamic system identification; adaptive identification and control approaches; adaptive PID and classical controllers.
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 employing the Model Reference Adaptive Control (MRAC) principle.

Course Material: Lecture Notes, Slides and Recorded Lectures

Lecture 0. Course Introduction: Discussion of the course syllabus and teaching material. Lecture 0 recorded movie (YouTube Channel).
Lecture 1. Introduction - General introduction to modelling and system identification. Lecture 1. (Lesson Slides PDF Format, 3.9MB). Lecture 1 recorded movie (YouTube Channel).
Lecture 2. Non-recursive (off-line) system identification methods. Lecture 2. (Lesson Slides PDF Format, 2.5MB). Lecture 2 - Least Squares and Regression Models (part 1) recorded movie (YouTube Channel); Lecture 2 - PEM and IMV Estimation Schemes (part 2) recorded movie (YouTube Channel).
Lecture 3. Recursive (on-line) methods. Lecture 3. (Lesson Slides PDF Format, 1.7MB). Lecture 3 recorded movie (YouTube Channel).
Lecture 4.1. The System Identification Toolbox by MathWorks. Tutorial from MathWorks Website. Lecture 4.1 recorded movie (YouTube Channel).
Lecture 4.2. Recursive (on-line) methods: two application examples in Matlab. Matlab script file 1 for Lecture 3; Matlab script file 2 for Lecture 3; Lecture 4.2 recorded movie (YouTube Channel).
Lecture 5. Remarks on System Identification and Data Analisys. Lecture 5. (Lesson Slides PDF Format, 2.8MB). Lecture 5 recorded movie (YouTube Channel).
Lecture 6. Control Refresher. Lecture 6. (Lesson Slides PDF Format, 232kB). Lecture 6. (Lesson Slides PDF Format, 2.8MB). Lecture 6 recorded movie (YouTube Channel).
Lecture 6.1. Example of a Simple Adaptive PID Controller. Compressed folder with files used in the simulations (.zip file); Design of a Simple Adaptive PID Controller Example recorded movie (YouTube Channel).
Lecture 7. Fundamentals of Model-Based Control. Lecture 7. (Lesson Slides PDF Format, 240kB); Lecture 7 - Overview of Model-Based and Data-Driven Control Techniques recorded movie (YouTube Channel).
Lecture 8. Introduction to Artificial (Shallow) Neural Networks for Control. Lecture 8. (Lesson Slides PDF Format, 1.2MB); Lecture 8 - Introduction to Machine Learning and Neural Networks (part 1) recorded movie (YouTube Channel); Lecture 8 - Neural Network Training (part 2) recorded movie (YouTube Channel).
Lecture 9. Artificial Neural Networks for Control. Lecture 9. (Lesson Slides PDF Format, 4.5MB); Lecture 9 - Neural Network Remarks recorded movie (YouTube Channel).
Lecture 9.1. Neural network training example in Matlab.
Lecture 9.2. Back-propagation algorithm implementation example; Neural network training example in Matlab; training data file.
Lecture 10. Intelligent Models for Control: Neural Design Example. Lecture 10. (Lesson Slides PDF Format, 2.8MB); Lecture 10 - Notes on the design of (shallow) neural networks for control recorded movie (YouTube Channel).
Lecture 10.1. Neural Network Example for the Estimation of a Static Nonlinear Function. Data generation in Simulink for neural network training; Neural network training Matlab script file (feedforward MLP and Back-Propagation); Simulink file for neural network validation (NN to be included); Matlab script for result comparison; Simulink file for result validation (NN included); Neural network training for the approximation of a static nonlinear function recorded movie (YouTube Channel).
Lecture 10.2. Example of Neural Network for Dynamic Process Modelling: Chemical Reactor Case Study. Compressed folder with files used in the simulations (.zip file); Neural network for the approximation of a dynamic nonlinear process recorded movie (YouTube Channel).

Examination Method

If the rules of the PhD course of the candidate require an official examination, a Google Form will be sent to the candidates with several multiple choice questions (quizzes) where the candidate should show the acquired knowledge and skills regarding the implementation and the analysis of the advanced control strategies addressed in the course. It will aim at understanding if the student has acquired the skills of the course. An attendance certificate will be also issued to the student, upon request.

References: Fundamentals Monographs and Textbooks on FDI

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.
Robust and Adaptive Control with Aerospace Applications, E. Lavretsky, K. A. Wise, Springer-Verlag, London, 2013
Adaptive Control. Second Edition, K. J. Astrom,, B. Wittenmark, Addison Wesley, 1995
System Identification, T. Soderstrom, P. Stoica, Prentice Hall International, Cambridge, 1989
Adaptive Control. Algorithms, Analysis and Applications, I. D. Landau, R. Lozano, M. M'Saad, A. Karimi, Springer, London, 2011
Stable Adaptive Systems, K. S. Narendra, A. M. Annaswamy, Dover Publications, New York, 2005
Robust Adaptive Control, P. Ioannou, J. Sun, Dover Publications, New York, 2012
Adaptive Control Tutorial, P. Ioannou, B. Fidan, Advances in Design and Control, SIAM, Philadelphia 2006

Matlab/Simulink Basic Tools and Docs

K. Sigmon, "Matlab Primer". University of Florida, Florida, Second Edition ed., 1993. (PDF format file).
"Matlab Tutorial", Getting Started with MATLAB. (PDF format file).
Simulink Main Features, "SIMULINK. Dynamic System Simulation for MATLAB". (PDF format file).
Simulink for Dynamic System Modelling. Dynamic System Simulation for MATLAB and SIMULINK. (PDF format file).
Simulink Basic Features. SIMULINK for Beginners. (PDF format file).

Simani Home Page

Dipartimento di Ingegneria