ISS0023 Intelligent Control Systems

Course description

News
Syllabus

Instructors: 

Semester: 

  • Autumn

Year: 

2015

Awarded ECTS points: 

5.0

Description: 

The course gives an overview of :
  • complex systems modeling and control methods and their applications in design of reliable control systems. 
  • artificial intelligence methods (artificial neural networks, fuzzy systems, genetic algorithms) based systems identification and control techniques and their applications in development of intelligent control systems. 
  • artificial intelligence methods based classification and recognition techniques and their applications.
 
Topics include:
  • Nonlinear systems, Principes of nonlinear systems identification and control; 
  • Adaptive control systems; 
  • Artificial neural networks. Structures of artificial neural networks and training algorithms; 
  • Artificial neural networks based identification of nonlinear systems; 
  • Artificial neural networks based control of nonlinear systems; 
  • Self-learning neural networks; 
  • Artificial neural networks based image recognition and pattern classification;
  • Fractional Order Modelling and Control; 
  • Dynamic feedback linearization based control of nonlinear systems; 
  • Genetic algorithms and their applications for identification and control of nonlinear systems.

Policies: 

Lab reports:
6 labs = 6 reports
Each report gives up to 1 point.
Each report has to be presented during 2 weeks after the lab!
Later presented reports (before December 18) – multiplied by coefficient 0.8
After December 18 – coefficient 0.6
5 best report will give up to 5 points.
 
Exam prerequisites:
Course ISS0023 is declared (included into Your semester plan),
Laboratory trainings are performed,
Reports are presented and accepted (written report on each laboratory training).
 
Estimation criterion:
Grades are based on the report of the final small practical project (design of a control system).
 
Exam  - up to 72 hours
Small practical project – design of a control system according to given control criteria;
Simulation of the control system;
Analysis of results and writing a report.
2 tasks – each one gives maximum 5 points.
 
Average of 2 exam tasks and labs = YOUR COURSE GRADE
 
Grades:
„5“ - student's knowledge is excellent, his/her answers are clear and complete, accurate in details,
deliberative and individual. Independent thinking. Student is capable of analyzing the problem and possible solutions, proposing his own solution and prooving its efficiency.
„4“ - student's knowledge is very good, his/her answers are clear and complete, accurate in details,
but less individual views. Student is capable of analyzing the problem, finding a suitable solution proving its efficiency.
„3“ - student's knowledge is good, his/her answers are clear, but there are a few errors in the
discussion and lack of personality and individual point of view. Student is capable of analyzing the problem and applying standard methods for is solution.
„2“ - student's knowledge is satisfactory, but all the answers are not clear and student makes more
errors (but not errors in basic concepts) and lack of personality and individual point of view.
„1“ - Student responses are weak, there are major mistakes in arguments (not knowing the content,
mistakes in concept), and student needs constant help and guidance in formulating responses to
questions. 
Calendar

September 2017

Mon Tue Wed Thu Fri Sat Sun
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11
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Materials

Lectures

Titlesort descending Published Short description Files
Adaptive control 15.09.2015 Slides - part 1 adaptive_control_1_2015.pdf adaptive_control_1_2015.ppt
Adaptive control 15.09.2015 Slides - part 2 adaptive_control_2_2015.pdf adaptive_control_2_2015.ppt
Fractional-order Modeling and Control 26.11.2015 Slides for the Fractional-order Modeling and Control class ISS0023_FracCalc.pdf
Fuzzy Control 05.10.2015 Lecture slides: Introduction to Fuzzy Control Sergei_Astapov_Fuzzy_Control_lecture_slides.pdf
Introduction to artificial neural networks 19.10.2015 Introduction to neural networks, neural network structure and mathematical model - lecture slides NN2015_part1.pdf NN2015_part1.ppt
Lecture 1 02.09.2015 31.08.2015 Introductory lecture ISS0023_introduction_2015.pptx ISS0023_introduction_2015.pdf
NN supervised learning 28.10.2015 Training of NNs - lecture slides NN2015_part2.ppt NN2015_part2.pdf
NN-based identification and control - lecture slides 11.11.2015 Neural Networks based identification and control of nonlinear systems, supevised and unsupervised learning NN2015_part3.ppt NN2015_part3.pdf

Exercises

Titlesort descending Published Short description Files
Adaptive control 31.08.2015 Nonlinear Adaptive Control adaptju1.mdl
Adaptive control 31.08.2015 Model Reference Control aw_dsim2.mdl aw_dsim44.mdl aw2d.m
First steps in MATLAB 31.08.2015 Material for the first Laboratory work "Brief Introduction into MATLAB/Simulink" Matlab.docx Matlab.pdf
Fractional-order Modeling and Control 30.11.2015 Materials for FOMCON: Fractional-order Modeling and Control lab iss0023_fraccalc_lab_2015_2016.pdf fomcon-1.0.1.zip fraccalc_lab.zip
Fuzzy Control Lab 05.10.2015 This submission includes the necessary laboratory work materials and corresponding instructions. Sergei_Astapov_Fuzzy_Control_Lab.pdf fuzzy_lab.zip
Overhead Crane 09.09.2015 Model of an Overhead Crane Overhead_crane2.pdf

Laboratory works

Titlesort descending Published Short description Files
Image recognition lab 17.11.2015 Example: Character recognition CharacterRecognition.zip all_numbers.m
Neural Networks based control 30.10.2015 Example of NN-based control system design in MATLAB/Simulink lab_NN2_2015.pdf NN_control.zip

Literature

Titlesort descending Published Short description Files
E. Rüstern Adaptiivjuhtimine (In Estonian, Eesti keeles) 31.08.2015 Prof. Ennu Rüsterni materjal - ülevaade adaptiivjuhtimidest (Eesti keeles) ISS0022_ülevaade_adaptiivsüsteemidest.pdf ISS0022_ülevaade_adaptiivsüsteemidest_2.pdf
Fuzzy control 31.08.2015 Book by K. Passino FCbook.pdf
Training Feedforward Networks with the Marquardt Algorithm 31.08.2015 Paper by Martin T. Hagan and Mohammad B. Menhaj in IEEE TRANSACTIONS ON NEURAL NETWORKS 00329697.pdf
Assignments
Titlesort descending Published Short description Files
Exam: January 2016 04.01.2016 Please solve at least one task from group A and one task from group B. You have 3 days to prepare a detailed report and send it to eduard.petlenkov@ttu.ee January2016.zip
NN training 20.10.2015 Data for NN nn_data.mat answer.m