General Competences (GC):

CG1 - Knowledge of basic subjects and technologies enables students to learn new methods and technologies and provides them with great versatility to adapt to new situations.

GC2 - Conveying information, ideas, problems, and solutions to both specialized and non-specialized audiences.

GC3 - Ability to gather and interpret relevant data to make judgments that include a reflection on relevant social, scientific, or ethical issues.



Transversal Competences (TC)

CT1 - Ability to communicate orally and in writing, in English and Spanish, using the usual audiovisual media, and to work in multidisciplinary teams and in multidisciplinary contexts.

multidisciplinary teams and in international contexts.

TC2 - Capacity for analysis and synthesis in problem-solving.

CT3 - Ability to adequately manage the information available, creatively integrating knowledge and applying it to the resolution of computer problems using the scientific method.

CT5 - Ability to assess the social and environmental impact of engineering solutions, and to pursue quality objectives in the development of their professional activity.



Specific Competences (SC)



CE1 - Knowledge and understanding of the main artificial intelligence techniques for their practical application in resolving problems in Control Engineering.

1. Introduction to control systems and AI

1.1. Overview of control theory

1.2. Fundamentals of Artificial Intelligence (AI)

Historical context and motivation for combining AI and control 2.

2. Machine learning for control

2.1 Regression models

2.2 Classification algorithms

2.3 Neural networks for function approximation 2.4.

2.4 Reinforcement learning basics

3. Intelligent control algorithms

3.1 PID controllers

3.2 Fuzzy logic control

3.3 Model Predictive Control (MPC)

3.4 Adaptive control

4. Optimization techniques

4.1 Genetic algorithms

4.2 Particle swarm optimization

4.3 Gradient-based optimization

4.4 AI applications in control

5. Robotics and automation

5.1 Process control

5.2 Autonomous vehicles

5.3 Intelligent networks

6. Case studies and projects

6.1 Analysis of research work

6.2 Implementation of AI-enhanced control systems

6.3 Student-led projects

In the lectures (M), classroom time is devoted to the presentation of theoretical knowledge and problem-solving in the classroom. Autonomous work will be encouraged through collections of questions and additional problems.



In Classroom Practice (GA), classroom time is devoted to solving practical problems in Matlab and Python environments, working in groups. Teamwork and the ability to analyze and synthesize are encouraged through additional work related to each practical.



If health conditions prevent the completion of a face-to-face teaching activity and/or assessment, a non-face-to-face modality will be activated, of which students will be promptly informed.

Students may opt for either a continuous or a final evaluation.

Continuous evaluation.

1. The weighted sum of the grades obtained in the different evaluable tasks carried out by the student will be applied according to the following criteria:

- Compulsory class attendance: 10%.

- Final exam (written test to be developed): 70%.

- Oral defense (discussion of concepts and problems): 20%.

2. The person who wishes to waive the continuous evaluation to take the final evaluation, must indicate it in writing before the end of the eleventh week of the term using the form that will be available on the teaching platform from the beginning of the course.

3. If a student does not participate in any of the different evaluable tasks, he/she will obtain the grade of NOT PRESENTED.



Final evaluation.

1. Students will have the right to be evaluated through the final evaluation system according to the conditions set out in the Regulations governing the evaluation of students in official undergraduate degrees (Chapter II. Article 8.3). For this, it will be necessary to have submitted before the end of the eleventh week the form for waiver of continuous assessment, which will be available on the teaching platform from the beginning of the course.

2. The evaluation will be carried out by means of evaluable tasks that guarantee the competency sufficiency of the subject according to the following scale:

- Final exam (written test to be developed): 75%.

- Oral defense (discussion of concepts and problems): 25%.

3. If a student does not participate in any of the evaluable tasks, he/she will obtain the grade of NOT PRESENTED.

There is no obligation to use any specific material.

The student has different didactic material provided by the subject professors in the Teaching Platform of the UPV/EHU for studying and preparing the classes.

On the other hand, in the bibliography there are different useful sources to obtain additional information.

Basic bibliography

1. James G., Witten D., Hastie T., Tibshirani, R., An introduction to statistical learning with applications in R, New York, Springer, 2013.

2. Goodwin, G. C., Graebe, S. F., y Salgado, M. E., Control System Design, Prentice Hall, 2001.

3. Sergios Theodoridis, Machine Learning, A Bayesian and Optimization Perspective, Elsevier, 2015.

4. Sutton, R. S., y Barto, A. G., Reinforcement Learning: An Introduction, MIT Press, 2018.

5. Proakis J.G. y Manolakis A.G., Digital signal processing. Principles, algorithms and applications, Pearson Prentice Hall, 2007.

In-depth bibliography

1. Hastie T., Tibshirani R., J.F., The elements of statistical learning, New York. Springer, 2008.
2. Ruano A. E., Intelligent Control using Intelligent Computational Techniques, IEEE Control Series, 2005
3. Zilouchian, A. y Jamshidi, M., Intelligent Control Systems Using Soft Computing Methodologies, CRC Press, 2001
4. Hopgood, A. A., Intelligent systems for engineers and scientists: A practical guide to artificial intelligence. CRC Press, 2021.

Journals

1. IEEE Control Systems Magazine
2. IEEE Intelligent Systems
3. IOP Journal of Physics