Laboratory of Control Engineering and Automation 2

Course Description

The course is organized in two laboratory blocks. In the first block students get practical experience about non-parametric and parametric identification methods, as well as practical knowledge about parameter and state estimation methods. The methods that are taught in the course Estimation Theory are experimentally tested on laboratory processes. In the second block students get insights in various nonlinear effects present in the systems analyzing them through experimental exercises on laboratory processes. Covered are frequency and time domain methods. Design by feedback linearization method, backsteping and sliding mode will be covered.

General Competencies

This course gives students sufficient practical knowledge to make identification experiments on linear systems, select optimal model structure and to estimate the system model using MATALAB System Identification Toolbox. The students will also get the capability to design and tune Kalman filter based state estimators for both linear and nonlinear systems. Finally, students will get the competence to design simple nonlinear control systems.

Learning Outcomes

  1. apply nonparametric method for estimation of mathematical models of systems
  2. apply parameter estimation methods
  3. apply state estimators of stochastic systems with Gaussian distribution
  4. analyze phase trajectories of nonlinear systems and chaos
  5. analyze self-oscillations in nonlinear control systems
  6. analyze forced oscillations in nonlinear control systems
  7. apply Dither signal for the purpose of system linearization

Forms of Teaching

Lectures

Lectures are held within the courses Nonlinear Control Systems (7.5 hours) and Estimation Theory (7.5 hours).

Laboratory Work

8 laboratory exercises: 4 with the topic of estimation theory, 4 with the topic of nonlinear control systems.

Consultations

Planned with students.

Grading Method

Continuous Assessment Exam
Type Threshold Percent of Grade Threshold Percent of Grade
Laboratory Exercises 0 % 20 % 0 % 20 %
Quizzes 0 % 80 % 0 % 80 %
Comment:

Attendance to all laboratory exercises is obligatory for passing the course.

Week by Week Schedule

  1. -
  2. -
  3. Laboratory exercise 1 (Nonlinear Control Systems): Chua's circuit
  4. Laboratory exercise 8 (Estimation Theory): Nonparametric identification methods
  5. Laboratory exercise 3 (Nonlinear Control Systems): State trajectories of nonlinear systems
  6. Laboratory exercise 4 (Nonlinear Control Systems): Describing function and self-oscillations
  7. -
  8. Laboratory exercise 5 (Estimation Theory): Nonrecursive parametric identification methods
  9. Laboratory exercise 6 (Estimation Theory): Recursive parametric identification methods
  10. -
  11. Laboratory exercise 7 (Nonlinear Control Systems): Forced oscillations
  12. Laboratory exercise 8 (Estimation Theory): Estimation of the satellite position in the planar orbit around Earth
  13. -
  14. -
  15. -

Study Programmes

University graduate
Control Engineering and Automation (profile)
(2. semester)

Literature

(2005.), MATLAB manuals, MathWorks
Lecturers (2008.), Estimation Theory - Instructions for laboratory work, FER
Lecturers (2008.), Nonlinear Control Systems - Instructions for laboratory work, FER

Laboratory exercises

General

ID 35227
  Summer semester
3 ECTS
L1 English Level
L1 e-Learning
15 Lectures
0 Exercises
30 Laboratory exercises
0 Project laboratory

Grading System

87.5 Excellent
75 Very Good
62.5 Good
50 Acceptable