Learning Outcomes
Should be able to solve an optimal control problem using calculus of variations.
Should be able to design a linear quadratic controller in the continuous and digital domain.
Should be able to design an optimal state estimator and incorporate it in a control system.
Should be able to design a linear and non-linear model-predictive controller.
General Competences
Retrieve, analyse and synthesise data and information, with the use of necessary technologies
Adapt to new situations
Make decisions
Work autonomously
Work in teams
Work in an interdisciplinary team
Appreciate diversity and multiculturality
Respect natural environment
Demonstrate social, professional and ethical commitment and sensitivity to gender issues
Be critical and self-critical
Advance free, creative and causative thinking
Course Content (Syllabus)
1. Overview of automatic control principles
2. Optimal control problem formulation
Performance index selection – Constraints
3. Variational calculus in optimal control problems
Unconstrained and constrained problems
4. Linear quadratic control
Disturbance rejections and set-point tracking problems
5. Introduction to digital systems
z-transform – digital transfer function
Stability of digital systems – Digital PID
6. Control systems design in state space
Controllability and observability
State feedback – Observers and Kalman filters
7. Model predictive control
Linear and non-linear systems
Numerical solution and practical implementation
