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Progettazione dei Sistemi di Controllo
Control System Design
David Scaradozzi

Seat Ingegneria
A.A. 2016/2017
Credits 9
Hours 72
Period II
Language ENG

Prerequisites
The student is required to be familiar with the basic elements of Systems Theory, Control Theory, Probability theory, Linear Algebra; Math Analysis; Physics.

Learning outcomes
KNOWLEDGE AND UNDERSTANDING:
The course has the aim to increase the knowledge learnt in the base courses, related to the problems of automatic control of engineering interest, referred to more general operative situations. Students will learn theorical elements for the planning and optimization of advanced control systems with high performance in case of complex real operative situations.
CAPACITY TO APPLY KNOWLEDGE AND UNDERSTANDING:
The course, even it is more aimed at increasing theorical knowledge, will provide experiences in applying the elements learnt in analysis and planning of complex automatic control systems. The considered case studies will provide experience to introduce measure indexes and optimization in real situations with high presence of noise. At the end of the course, students will be able to autonomously model and design dynamic systems.
TRANSVERSAL SKILLS:
In the course are strengthened the competences that leads to identifying and solve in a better way, complex problems of engineering nature. Besides, it is increased the problem solving capacity and the identification of suitable optimization indexes.

Program
essons (52 Ore) - Elements of probability theory and stochastic processes. - Minimum variance estimate. Orthogonal projection Lemma - Kalman filter. Optimal smoothers and predictors. - Dynamic programming equations. LQ and LQG control problems. - Optimal stabilization and tracking over finite and infinite time intervals. - Minimum variance control of industrial processes- - Adaptive control Laboratory (20 Ore) LabVIEW and Matlab use for advance control systems design.

Development of the examination
LEARNING EVALUATION METHODS
The final examination consists of an oral test. Usually, the first question concerns the discussion of a case study

LEARNING EVALUATION CRITERIA
Knowledge assessment of a number of topics of the course.

LEARNING MEASUREMENT CRITERIA
Measurement of the mastery of at least two of the course topics.

FINAL MARK ALLOCATION CRITERIA
Average between the two discussed arguments.

Recommended reading
Lessons (52 Ore) - Elements of probability theory and stochastic processes. - Minimum variance estimate. Orthogonal projection Lemma - Kalman filter. Optimal smoothers and predictors. - Dynamic programming equations. LQ and LQG control problems. - Optimal stabilization and tracking over finite and infinite time intervals. - Minimum variance control of industrial processes- - Adaptive control Laboratory (20 Ore) LabVIEW and Matlab use for advance control systems design.

Courses

• Ingegneria Informatica e dell'Automazione (Corso di Laurea Magistrale (DM 270/04))