First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Engineering
MECHATRONIC ENGINEERING
Course unit
SYSTEMS THEORY AND OPTIMAL AND ADAPTIVE CONTROL (C.I.)
INP5070926, A.A. 2019/20

Information concerning the students who enrolled in A.Y. 2019/20

Information on the course unit
Degree course Second cycle degree in
MECHATRONIC ENGINEERING
IN0529, Degree course structure A.Y. 2011/12, A.Y. 2019/20
N0
bring this page
with you
Number of ECTS credits allocated
Type of assessment Mark
Course unit English denomination SYSTEMS THEORY AND OPTIMAL AND ADAPTIVE CONTROL (C.I.)
Department of reference Department of Management and Engineering
Mandatory attendance No
Language of instruction Italian
Branch VICENZA
Single Course unit The Course unit can be attended under the option Single Course unit attendance
Optional Course unit The Course unit can be chosen as Optional Course unit

Lecturers
Teacher in charge LUCA SCHENATO ING-INF/04

Modules of the integrated course unit
Course unit code Course unit name Teacher in charge
INP5070927 OPTIMAL AND ADAPTIVE CONTROL (MOD.B) LUCA SCHENATO
INP5070928 SYSTEMS THEORY (MOD.A) --

Course unit organization
Period  
Year  
Teaching method frontal

Calendar
Start of activities 23/09/2019
End of activities 20/06/2020
Show course schedule 2019/20 Reg.2011 course timetable

Syllabus
Prerequisites: For the successful achievement of the objectives set, knowledge of Signals and Systems (study of signals and linear systems, analysis of signals and systems in the time and frequency domain) and Automatic Controls (Feedback systems, stability analysis, synthesis of regulators) is required, as well as knowledge about Linear Algebra and Geometry (vector spaces, linear functions, matrices and their applications in geometry).
For students coming from the Meccatronica Engineering Degree course of the University of Padua, the courses of Signals and Systems, Automatic Controls and Fundamentals of Linear Algebra and Geometry in the Three-year Degree are considered as prerequisites.
Target skills and knowledge: The course aims to teach students the knowledge of some fundamental techniques of advanced control of dynamic systems.
In particular, the study of dynamic systems, in the state-variable framework, will be analyzed in depth, giving ample space for in-depth analysis of the basic techniques for their control.
During the course, skills will be acquired that will allow the student to deal with:
- the synthesis of controllers that meet optimality requirements
- the synthesis of controllers able to adapt to the variability of some of the process model parameters
- the understanding, with the help of numerical laboratory experiments, of the benefits, implementation difficulties and limitations of the control techniques mentioned above
Examination methods: The verification of the expected knowledge and skills is carried out through a written test (for the Systems Theory module), in which the student will have to demonstrate his/her ability to apply the methodologies to simple numerical examples, and through an oral exam (for the optimal and adaptive control module), during which the student have to demonstrate the knowledge of the theoretical aspects discussed in the course. During the oral examination, (individual) written reports will also be discussed as a commentary on laboratory activities.
Assessment criteria: The written test aims to evaluate the ability to apply the theoretical knowledge acquired to the solution of non-abstract problems, even if simplified and exemplified.

The oral exam aims to evaluate the ability to deal with methodological rigor the study of complex subjects and the completeness of the preparation acquired in the integrated course.

Laboratory reports will be evaluated on the basis of:
- Completeness
- Exhibition clarity
- Ability to critically analyze the results

The final mark will consist of the weighted average of the marks assigned to the written and the oral exams.

Innovative teaching methods: Teaching and learning strategies
  • Lecturing
  • Laboratory
  • Problem based learning
  • Case study
  • Working in group
  • Problem solving
  • Loading of files and pages (web pages, Moodle, ...)

Innovative teaching methods: Software or applications used
  • Moodle (files, quizzes, workshops, ...)
  • Matlab