Principles of Optimal Control
(
first module of System Identification and Optimal Control)

Marilena Vendittelli


Information

schedule Nov 5 - Dec 18 2013, Tue 14:00-15:30, Wed 14:00-15:30, A6; 24 hrs total (3 ECTS credits)
THERE WILL BE NO CLASS ON NOVEMBER 19 AND NOVEMBER 20
office hours Thu 14:00-16:00, room A213, DIAG, Via Ariosto 25
e-mail vendittelli [at] dis [dot] uniroma1 [dot] it
SIOC Google Group   
SIOC_GG (membership is restricted to the audience of the course; send an e-mail to the instructor with subject SIOC_GG to be invited) 

Audience

Students of the Master of Science in Control Engineering (MCER) at the Dipartimento di Ingegneria informatica, automatica e gestionale (DIAG) of the Università di Roma "La Sapienza". Other possible students include those of the Master in Artificial Intelligence and Robotics (MARR).


Objective

Provide the basics on optimization and optimal control.


Syllabus NEW

topics reference material
(see below for the list of references)
nonlinear optimization Lectures 1 and 2 from [5], chapters 1 and 2 in [1], chapter 1 in [4], chapter 6 in [3]
dynamic programming Lectures 3 and 4 from [5], chapter 3 in [3], chapter 5 [4]
calculus of variations Lecture 5 from [5], chapters 4 and 5 in [1], chapter 4 in [3], chapter 2 in [4]
calculus of variations applied to optimal control Lecture 6, 8, 9,10 from [5], chapters 6 and 7 in [1], chapter 5 in [3], chapters 3 and 4 in [4]


Reference material

textbooks available in the DIAG library

[1] C. Bruni, G. Di Pillo, "Metodi variazionali per il controllo ottimo", Aracne, 2007

[2] A. Locatelli, "Optimal Control: An Introduction", Birkhäuser, 2001

[3] D. E. Kirk, "Optimal Control Theory: An Introduction, New York, NY: Dover, 2004


a textbook also available for download

[4] D. Liberzon, "Calculus of Variations and Optimal Control Theory: A Concise Introduction", Princeton University Press, 2011 (also dowloadable from here)


most of the class lectures will be based on

[5] How, Jonathan. 16.323 Principles of Optimal Control, Spring 2008. (MIT OpenCourseWare: Massachusetts Institute of Technology). License: Creative Commons BY-NC-SA.


Grading NEW      

Written (50%) + oral (50%) exam or homework (30%) + project (70%). Details on projects are given in class.


Master Theses at the Robotics Laboratory

Master Theses on the topics studied in this course are available at the Robotics Laboratory. More information can be found here.
Questions/comments: vendittelli [at] dis [dot] uniroma1 [dot] it