TELE3003 - Industrial Automation


Examination arrangement

Examination arrangement: Written examination
Grade: Letters

Evaluation form Weighting Duration Examination aids Grade deviation
Written examination 100/100 6 hours C

Course content

Introduction to measurement systems: background, history, SI-system, traceability, standards, concepts, definitions, characteristics, structure, mathematical modeling. Statistics: mean value, standard deviation, uncertainty and regression analysis. Signal conversion in measurement systems: measuring bridges, amplifier circuits, noise and grounding, and smart transmitters. Measuring principles for pressure, level, flow and temperature measurement.

Motor drives:
Semiconductors: structure and operation of power diode, thyristor and transistor. Single-phase rectifiers: full-wave diode rectifiers, half-wave and full-wave thyristor rectifiers. Choppers: step-down (buck), step-up (boost) and full-bridge converters. Variable-frequency converters: PWM-VSI, square-wave VSI, square-wave CSI. DC machines: structure and operation. Asynchronous machines: structure, operation and variable-frequency converter drives.

Multivariate systems
Introduksjon to control of multivariate systems. Design and configuration of feedback loops using stationary analysis methods such as eigenvalue -, singular value -, and RGA analysis. Strategies for tuning controllers in multivariate systems with an emphasis on single loop control, decoupling and full state feedback. Experimental and mathematical modeling of multivariate processes. Stability theory and stability considerations for state space models. Design of basic state estimators. Introduction to system identification.

Introduction to industrial robotics with an emphasis on robot manipulators. Definition of local coordinate frames and rotational matrices. Denavit-Hartenberg convention for deriving forward kinematics. Techniques for calculating workspace, singularities and inverse kinematics. Interpolation of point-to-point movements using polynomials and trajectory tracking with independent joint control. Object detection and configuration with robot vision. Use of ROS (Robot Operating System) for programming and application of robot manipulators.

Learning outcome

Knowledge: The candidate can explain
- the most common concepts and definitions in measurement systems.
- the structure and characteristics of measurement systems.
- statistical analysis of measurement data.
- signal conversion in measurement systems.
- noise-reduction and grounding in measurement systems.
- the most common measurement principles for pressure, level, flow and temperature measurement.
- the structure and operation of power electronics semiconductors.
- the structure and operation of different power electronics converters.
- the structure and operation of DC machines and asynchronous machines.
- the purpose of the specific solutions used in multivariate control systems
- the challenges associated with multivariate control
- how stationary and dynamic analysis can be used to determine feedback loops
- different strategies that can be used to control multivariate systems
- the purpose of state estimation and system identification for designing control systems
- the structure and purpose of industrial robots
- the term kinematics in the context of robotics
- the purpose of and the general functionality of ROS
- the role of the trajectory planner in robotic operations
- the functioning and application of robot vision

Skills: The candidates can
- model and calibrate measurement systems.
- analyze measurement data using statistical methods.
- design and analyze electronic circuits for signal conversion.
- assess which measurement principles are appropriate for a given situation for pressure, level, flow and temperature measurement.
- analyze different power electronics converters with different loads.
- analyze DC machines and asynchronous machines.
- analyze combinations of converters and electrical machines.
- model multivariate systems, and derive system equations both on state space form and as transfer matrices
- analyze multivariate systems with stationary and dynamics methods, and apply the results in determining feedback loops and controller setups
- design multivariate control systems both with PID control loops and full state feedback.
- use simple estimator methods to implement control strategies based on complete state information.
- identify simple process models by means of impluse, step and frequency responses.
- reason about the relationship between theoretical results and practical application
- outline the design of industrial robot workstations.
- calculate position and speed of a general robot manipulator.
- solve the inverse kinematics problem for specific robot manipulators.
- Generate smooth movements for robot manipulators and implement them in software.
- use robot vision to estimate the position of objects.
- program simple grasping tasks for robot manipulators
- program robot manipulators using Robot Operating System (ROS)

General competence: The candidate wields a combination of theoretical and practical skills within the subject area, providing a base for solving practical problems in a professional setting - and for further education within the subject area.

Learning methods and activities

Lectures, exercises, case study exercise and laboratory exercises.

Compulsory assignments

  • Lab
  • Exercises
  • Case

Further on evaluation

3 out of 4 home assignments in each of the 4 modules must be approved (in total 12 out of 16). The case study assignment in multivariate systems must be approved. 6 out of 6 laboratory exercises must be approved. Compulsory attendance.

Specific conditions

Exam registration requires that class registration is approved in the same semester. Compulsory activities from previous semester may be approved by the department.

Admission to a programme of study is required:
Electrical Engineering (FTHINGEL)

Required previous knowledge

The course has admission requirements.

Course materials

Lecture notes. Exercises and laboratory exercises with solutions. The textbooks are specified at the start of the semester.

Credit reductions

Course code Reduction From To
IELET2105 5.0 01.09.2019
IELET2107 5.0 01.09.2019
IELET2106 5.0 01.09.2020
More on the course



Version: A
Credits:  20.0 SP
Study level: Third-year courses, level III


Term no.: 1
Teaching semester:  AUTUMN 2020

Language of instruction: Norwegian

Location: Trondheim

Subject area(s)
  • Engineering Cybernetics
  • Engineering Subjects
Contact information
Course coordinator: Lecturer(s):

Department with academic responsibility
Department of Engineering Cybernetics



Examination arrangement: Written examination

Term Status code Evaluation form Weighting Examination aids Date Time Digital exam Room *
Autumn ORD Written examination 100/100 C 09:00
Room Building Number of candidates
Spring UTS Written examination 100/100 C 09:00
Room Building Number of candidates
  • * The location (room) for a written examination is published 3 days before examination date. If more than one room is listed, you will find your room at Studentweb.

For more information regarding registration for examination and examination procedures, see "Innsida - Exams"

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