ISS0023 Intelligent Control Systems

Course description

News
  • End of smester and exams | 11.12.2018 18:52 | by Eduard Petlenkov

    There will be no lecture on December 17.

     

    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 4, start at 10:00 ICT-405, deadline for presenting exam reports: January 7, 10:00

    January 7, start at 10:00 ICT-402, deadline for presenting exam reports: January 10, 10:00

    January 15, start at 10:00 ICT-501, deadline for presenting exam reports: January 18, 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@taltech.ee  before the deadline.

     

    To those, who want to have the exam in December before Christmas, please send me an e-mail before December 16.

    I will send the assignments to you personally by December 17 morning. Please send me the reports by December 20 morning.

     

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

Syllabus

Instructors: 

Semester: 

  • Autumn

Year: 

2018

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:

5 labs = 5 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 21) – multiplied by coefficient 0.8
After December 21 – coefficient 0.6
5 eport 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

December 2018

Mon Tue Wed Thu Fri Sat Sun
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Materials

Lectures

Titlesort descending Published Short description Files
Adaptive control 17.09.2018 Slides - part 1 adaptive_control_1_2017.pdf
Adaptive control 17.09.2018 Slides - part 2 adaptive_control_2_2017.pdf
Fractional-order Modeling and Control 19.11.2018 Slides for the fractional-order modeling and control lecture ISS0023_FracCalc.pdf
Introduction to artificial neural networks 17.09.2018 Introduction to neural networks, neural network structure and mathematical model - lecture slides NN2018_part1.pdf
Lecture 1 10.09.2017 10.09.2018 Introductory lecture ISS0023_introduction_2018.pdf
NN supervised learning 12.10.2018 Training of NNs - lecture slides NN2018_part2.pdf
NN-based identification and control - lecture slides 22.10.2018 Neural Networks based identification and control of nonlinear systems, supevised and unsupervised learning NN2018_part3.pdf
NN-structures in control 03.12.2018 Lecture slides on NN-structures, dynamic feedback linearization, NN-ANARX and application of Genetic Algorithms NN_structures2018.pdf Genetic2018.pdf

Exercises

Titlesort descending Published Short description Files
Adaptive control 17.09.2018 Nonlinear Adaptive Control adaptju1.mdl
Adaptive control 17.09.2018 Model Reference Control aw2d.m aw_dsim2.mdl aw_dsim44.mdl
Introduction to Matlab 04.09.2018 Introduction to Matlab 10.09.2018 (Optional) Matlab.pdf

Laboratory works

Titlesort descending Published Short description Files
Dynamic linearization based control 07.12.2018 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 26.11.2018 Lab materials for FOMCON practical work iss0023_fraccalc_lab.pdf fraccalc_lab.zip
Identification of Dynamic Systems using ANNs 15.10.2018 MATLAB files for the lab NN_modelling_lab.zip
Image recognition lab 08.11.2018 Example: Character recognition CharacterRecognition.zip
Introduction to Neural Networks 04.10.2018 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 24.10.2018 Example of NN-based control system design in MATLAB/Simulink NN_control.zip lab_NN2_2018.pdf
Rules 27.08.2018 Rules for preparation of laboratory reports lab_reports_rules.pdf
Assignments
Titlesort descending Published Short description Files
Home work 1 (NN training) 12.10.2018 Solve all the tasks from the attached pdf file. DEADLINE - October 29, 2018 HW1_2018.pdf homework1.mat
Home work 2 (Identification of Dynamic Systems using ANNs) 15.10.2018 Solve all the tasks from the attached pdf file. DEADLINE - November 5, 2018 HW2_2018.pdf
Home work 3 (NN based control) 24.10.2018 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 12, 2018 HW3_2018.pdf nonlinear_system.slx
Home work 4 (pattern recognition) 08.11.2018 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 16% of random noise. REPORT DEADLINE: November 26, 2018 all_numbers.m
Home work 5 (Fractional-order Modeling and Control) 03.12.2018 Present a report on the laboratory tasks according to the given instructions. DEADLINE: December 10 No page limit for this report (NB! only for this report!) iss0023_fraccalc_lab.pdf fraccalc_lab.zip
Home work 6 (NN-ANARX model based control) 07.12.2018 Design a NN-ANARX model based control system for the Jacketed CSTR system. See the task in the attached hw6.pdf file. DEADLINE: January 4, 2019 HW6.pdf