ISS0023 Intelligent Control Systems

Course description

News
  • IMPORTANT INFORMATION REGARDING EXAMS AND SUBMISSION OF LAB REPORTS | 07.12.2017 15:59 | by Eduard Petlenkov

    There will be no lab classes on the last week (December 21-22).

     

    The grade for the lab works will be the sum of 5 best reports of 6 (max. 5 points). If you submit 5 reports and decide not to submit one of the reports, all points will be summarized and give the grade for the labs.

    Two more grades will be given for the exam tasks. You will get 2 tasks. Each task will be evaluated separately.

    Final grade will be the average of 3 grades.

     

    EXAMS:

     

    January 9, start at 10:00 - deadline for presenting exam reports: January 12, 10:00

    January 16, start at 10:00 - deadline for presenting exam reports: January 19, 10:00

    January 23, start at 10:00 - deadline for presenting exam reports: January 25, 24:00

     

    If you want to have the exam at another time - please contact me. It is also possible to get exam tasks by e-mail.

    All exam reports are individual and have to be sent by e-mail to eduard.petlenkov@ttu.ee before the deadline.

     

    To those, who want to have the exam in December before Christmas, please send me an e-mail before December 14.

    I will be out of Estonia in the end of semester with limited access to the internet, but exam tasks will be available on the web page under the assignments by December 20 morning. I will send you passwords by e-mail and please send me the reports by December 23 morning.

    I will check these reports after Christmas.

     

    If you have any questions, please contact me by e-mail.

  • Laboratory class on Thursday, October 12 | 10.10.2017 13:57 | by Eduard Petlenkov

    This Thursday, October 12 the laboratory class will start at 13:45 (NOT at 13:00)

Syllabus

Instructors: 

Semester: 

  • Autumn

Year: 

2017

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

January 2018

Mon Tue Wed Thu Fri Sat Sun
1
2
3
4
5
6
7
 
 
 
 
 
 
 
8
9
10
11
12
13
14
 
 
 
 
 
 
 
15
16
17
18
19
20
21
 
 
 
 
 
 
 
22
23
24
25
26
27
28
 
 
 
 
 
 
 
29
30
31
1
2
3
4
 
 
 
 
 
 
 
Materials

Lectures

Titlesort descending Published Short description Files
Adaptive control 05.09.2017 Slides - part 1 adaptive_control_1_2017.ppt adaptive_control_1_2017.pdf
Adaptive control 05.09.2017 Slides - part 2 adaptive_control_2_2017.ppt adaptive_control_2_2017.pdf
Fractional-order Modeling and Control (A. Tepljakov) 04.12.2017 Slides for the fractional-order modeling and control lecture ISS0023_FracCalc.pdf
Fuzzy Logic based Modeling and Control (S. Astapov) 10.10.2017 Lecture slides on fuzzy logic based modeling and control Sergei_Astapov_Fuzzy_Control_lecture_slides.pdf
Introduction to artificial neural networks 21.09.2017 Introduction to neural networks, neural network structure and mathematical model - lecture slides NN2017_part1.ppt NN2017_part1.pdf
Lecture 1 08.09.2017 05.09.2017 Introductory lecture ISS0023_introduction_2017.pptx ISS0023_introduction_2017.pdf
NN supervised learning 20.10.2017 Training of NNs - lecture slides NN2017_part2.pdf
NN-based identification and control - lecture slides 20.10.2017 Neural Networks based identification and control of nonlinear systems, supevised and unsupervised learning NN2017_part3.pdf
NN-structures in control 16.11.2017 Lecture slides on NN-structures, dynamic feedback linearization, NN-ANARX and application of Genetic Algorithms NN_structures2016.pdf Genetic2017.pdf

Exercises

Titlesort descending Published Short description Files
Adaptive control 12.09.2017 Nonlinear Adaptive Control adaptju1.mdl
Adaptive control 12.09.2017 Model Reference Control aw2d.m aw_dsim2.mdl aw_dsim44.mdl

Laboratory works

Titlesort descending Published Short description Files
Dynamic linearization based control 22.11.2017 Example of NN-ANARX model based control - liquid level control of a laboratory multi-tank system NN_ANARX_control.zip
Fractional-order Modeling and Control (A. Tepljakov) 04.12.2017 Laboratory work for Fractional-order Modeling and Control iss0023_fraccalc_lab.pdf fraccalc_lab.zip
Fuzzy Logic Control 11.10.2017 Files and instructions for the fuzz logic laboratory work Sergei_Astapov_Fuzzy_Control_Lab.pdf fuzzy lab.zip
Image recognition lab 08.11.2017 Example: Character recognition CharacterRecognition.zip
Introduction to Neural Networks 27.09.2017 NN training data - two inputs, one output + function used to generate the data (you may use this file to validate the result) nn_data.mat answer.m
Neural Networks based control 25.10.2017 Example of NN-based control system design in MATLAB/Simulink NN_control.zip lab_NN2_2017.ppt lab_NN2_2017.pdf

Literature

Titlesort descending Published Short description Files
E. Rüstern Adaptiivjuhtimine (In Estonian, Eesti keeles) 05.09.2017 Prof. Ennu Rüsterni materjal - ülevaade adaptiivjuhtimidest (Eesti keeles) ISS0022_ülevaade_adaptiivsüsteemidest.pdf ISS0022_ülevaade_adaptiivsüsteemidest_2.pdf
Fuzzy control 05.09.2017 Book by K. Passino FCbook.pdf
Assignments
Titlesort descending Published Short description Files
Exam tasks 07.01.2018 There are two groups of tasks - Group1 (Task1 and Task2) and Group2 (Task3 and Task4). Please solve one task (on your choice) FROM EACH GROUP. Each task will be evaluated separately. If you will solve more, the best results from each group will be counted ExamJanuary2018.zip tasks.pdf
Home work 1 (NN training) 20.10.2017 Solve all the tasks from the attached pdf file. DEADLINE - October 27, 2017 HW1_2017.pdf homework1.mat
Home work 2 (Fuzzy Systems) 20.10.2017 Solve all tasks from the attached file. Explain solutions, analyze results. Present a report. DEADLINE - October 27, 2017 Sergei_Astapov_Fuzzy_Control_Lab.pdf
Home work 3 (NN based control) 25.10.2017 Design Neural Network based nonadaptive and adaptive control systems for the black box nonlinear system given in the file. See HW3.pdf for a detailed task. DEADLINE: November 10, 2017 nonlinear_system.slx HW3.pdf
Home work 4 (pattern recognition) 08.11.2017 Design supervised and unsupervised NN based systems for recognition of numbers given in file all_numbers.m. Proof experimentally that the system correctly recognizes all numbers with 18% of random noise. REPORT DEADLINE: November 24, 2017 all_numbers.m
Home work 5 (NN-ANARX model based control) 22.11.2017 Design a NN-ANARX model based control system for the Jacketed CSTR system. See the task in the attached hw5.pdf file. DEADLINE: December 10 , 2017 HW5.pdf
Home work 6 (Fractional-order Modeling and Control) 07.12.2017 Present a report on the laboratory tasks according to the given instructions. DEADLINE: December 22 iss0023_fraccalc_lab.pdf fraccalc_lab.zip