Course Contents
The course consists of 64 hours of teaching activities divided into frontal lectures (47 hours) and guided tutorials in the computer laboratories (17 hours). In more detail, the following topics will be analysed and investigated:
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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 for control.
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Basics of optimisation tools. Constrained and unconstrained optimisation; gradient method; stochastic approaches; genetic algorithms.
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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.
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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.
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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.
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Computer-Aided Hands-on (computer-aided design). Hands-on with the identification of nonlinear dynamic systems, fuzzy logic for control, neural networks, and design of adaptive control schemes.