Intelligent control
Objectives
Study of inteligent control techniques, both theoreticaly and applied. Starting by introducing concepts on linear dynamic systems identification and adaptive pole placement control, followed by the use of artifitial neural networks and fuzzy logics as means to approximate nonlinear dynamics. The core of this course is the development of neural and fuzzy controllers.
General characterization
Code
10993
Credits
6.0
Responsible teacher
Luís Filipe Figueira Brito Palma
Hours
Weekly - 4
Total - 62
Teaching language
Português
Prerequisites
Preferably students should attend the Control Theory and Computer Control subjects, or equivalent.
Bibliography
Main references
- Identification and Adaptive Control, Paulo Gil, 2002 (in portuguese)
Additional references
- Kalman Filter, L. Brito Palma, 2019.
- System Identification, Lennart Ljung, 1987
- System Identification and Control Design, I. Landau, 1990
- Neural Network Design, M. Hagan, 1996
- Neuro-Fuzzy and Soft Computing, Jang, Sun e Mizutani, 1995
Teaching method
Theoretical-practical classes and practical laboratory classes.
Evaluation method
The course assessment comprises two components: Lab. work ((TG1.1 + TG1.2 + TG1.3)) + two quizzes.
The CI grade is given by:
NP = (TG1.1 + TG1.2 + TG1.3)/3
Grade Normal= NP*0.4/3 + (MT 1 + MT 2)*0.3;
Grade Exam= NP*0.4/3 + Exam*0.6.
Required a minimum attendance of 67% in practical classes and minimum of 9.5 out of 20 in all components.
Subject matter
Linear Systems Identification: Problem; The identification process; Linear time invariant models; Parameter estimation: least squares; Model validation; RLS.
Adaptive Control: Functional models; Pole placement.
Artificial Neural Networks: The neuron; Activation functions; Proactive multi-layer networks; Approximation properties; Supervised training in multi-layer networks ; Generalization and validation; Neural control architectures.
Kalman filter.
Fuzzy Control: Fuzzy systems fundamentals; Defuzzification of variables; Inference with linguistic variables; Defuzzification of linguistic variables; Fuzzy control design.