Checking date: 26/04/2022

Course: 2022/2023

Analysis and Design of Control Systems
Study: Master in Internet of Things: Applied Technologies (356)

Coordinating teacher: GARCIA FERNANDEZ, FERNANDO

Department assigned to the subject: Systems Engineering and Automation Department

Type: Compulsory
ECTS Credits: 3.0 ECTS


Requirements (Subjects that are assumed to be known)
Sin requisitos previos.
Basics Competences Knowledge related to control engineering techniques that can be applied to the IoT field Capacity to apply these knowledge to the specificity of the model General Competences To identify the different control methods available for IoT and their advantages and disadvantages Capacities to identify, define and formulate the control problems related to IoT applications. This capacity includes the assessment of all the factors at stake, not only at technical level but environmental and civil responsibility. Capacity to publicly communicate the concepts, developments and results of control engineering results related to IoT, adapted to the specificity of all audiences. Capacity to apply the acquired knowledge for solving problems in a wide variety of multidisciplinary new situations, with the ability to integrate knowledge. RESULTS OF THE LEARNING PROCESS The results of the learning process will be: - Capacity of analysis and synthesis for advance control systems. - Capacity to design a system of small or medium complexity and its interaction with the environment. - Knowing and applying the techniques of automatic learning in IoT.
Skills and learning outcomes
Description of contents: programme
1- Introduction to Control Engineering a. Origin of control Engineering b. Applications c. Naming and definitions 2- Classifications a. Systems b. Control Techniques 3- PID and its applications a. Controllers type P b. Controller type I c. Controller type D d. Controller type PID 4- Control techniques and alternatives a. Linear Techniques b. No-linear techniques 5- Artificial Intelligent Control a. Genetic Algorithms b. Fuzzy logic c. Machine Learning d. Deep Learning 6- Control Application in IoT a. Autonomous and connected vehicle b. Robotics c. drone d. Other examples
Learning activities and methodology
- Lectures, classes for resolution of doubts in small groups, student presentations, tutorials and individual work of students; aimed at the acquisition of knowledge (9 sessions). - Laboratory practices and sections of problems in small groups, individual tutorials and individual work of students, aimed at the acquisition of practical skills related to the syllabus of the subject (4 sessions).
Assessment System
  • % end-of-term-examination 40
  • % of continuous assessment (assigments, laboratory, practicals...) 60
Calendar of Continuous assessment
Basic Bibliography
  • K. Ogata. Modern Control Engineering. Pearson-Prentice Hall.. 2010
  • Zhang, H. and Liu, P.. Fuzzy Modeling and Fuzzy Control. Ed Birkhauser. 2006

The course syllabus may change due academic events or other reasons.