Control and Computational Intelligence

Recommended Prerequisites

Advanced engineering mathematics; Control theory.

Teaching Methods

A combination of the following methods will be employed: tutorial guidance classes; seminary classes; orientation of practical works on simulation and / or real implementation. Assessment: practical work with elaboration of a technical report and final presentation (100%).

Learning Outcomes

After attending this curricular unit, the students should have acquired theoretical and practical knowledge required for analysis and design of computer controlled systems, and computational intelligence applied in control.

Acquisition of skills, in research environment, such as analysis and synthesis, independent learning, adaptability to new contexts, applying in practice the theoretical knowledge.

Work Placement(s)

No

Syllabus

- Module I: Computer-controlled systems in state-space with state observers (e.g. Kalman Observers). Discrete-time optimal control in state-space.

- Module II: Introduction to nonlinear control: concepts, simulation and control of nonlinear systems, stability analysis.

- Module III:Computational intelligence and machine learning methodologies applied in control. 

- Applications in robotic and mechatronic systems.

Head Lecturer(s)

Rui Alexandre de Matos Araújo

Assessment Methods

Assessment
Research work: 100.0%

Bibliography

- Astrom, K., Wittenmark, B. (1997), Computer Controlled Systems: Theory and Design, Prentice-Hall.

- Hassibi, B., Sayed, A.H., Kailath, T. (1999), Indefinite-Quadratic Estimation and Control - A Unified Approach to H2 and H∞ Theories, SIAM.

- Simon, D. (2006), Optimal State Estimation: Kalman, H∞ and Nonlinear Approaches, Wiley.

- Khalil, H. (2015), Nonlinear Control, Pearson.

- Khalil, H. (2002), Nonlinear Systems, 3rd Edition, Pearson.

- Haddad W.M., Chellaboina V. (2008), Nonlinear Dynamical Systems and Control: A Lyapunov-Based Approach, Princeton University Press.

- Wang, L.-X. (1997), A Course in Fuzzy Systems and Control, Prentice-Hall.

- Haykin, S. (2009), Neural Networks and Learning Machines, 3rd Edition, Pearson.

- Babuska, R. (1998), Fuzzy Modeling for Control, Kluwer.

- Shin, Y.C., Xu C. (2009), Intelligent Systems, Modeling, Optimization, and Control, CRC Press.

- Feng, G. (2010), Analysis and Synthesis of Fuzzy Control Systems: a Model-Based Approach. CRC press.

- Golden, R. (2020), Statistical Machine Learning - A Unified Framework, CRC Press.

By Richard Golden

- Sharma, K.D., Chatterjee, A., Rakshit, A. (2018), Intelligent Control - A Stochastic Optimization Based Adaptive Fuzzy Approach, Springer.