34842 - Automatic Controls and Theory of Systems M

Learning outcomes

The course aims to introduce basics in system theory and advanced control methodologies for linear and nonlinear multivariable systems.  In the firts part of the course, the objective is to introduce the student with fundamental properties of dynamical systems in the state space. In the second part of the course optimal control techniques in the deterministic and stochastic framework will be presented for linear systems. The final part of the course will introduce the student to basic approaches of robust control for nonlinear systems in the state space.

Pre-requisites: basic knowledge about automatic control and physics are required. A good knowledge of linear algebra is also helpful.
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Course contents

1. Introduction
 Introduction to modern control theory. Examples of advanced automatic control. Modern and classic automatic control theory.

2. System Theory
 State space model. Relation between state space model and transfer function. Controllability (Stabilizability) and Observability (Detectability). State space transformation. Minimal forms and realization theory. Free and forced  motion. Jordan, observability and controllability canonical forms. Pole placement via state feedback. The dual problem. Design of Luenberger observers. Reduced-order observers. Dynamic output feedback. Stability of linear and nonlinear systems via Lyapunov analysis. Lyapunov and La Salle criterions.

3. Optimal Control in the deterministic framework
 Introduction to the optimal control problem. Hamiltonian function and Eulero-Lagrange equations. Pontryagin Principle. Linear Quadratic optimal control. Optimal control by state and output feedback. Optimal tracking. Set point control. Optimal control with frequency specs. Examples. Optimal control in the stochastic framework  Basics on Probability theory. Optimal state estimation: the Kalman-Bucy filter. The optimal observer in the stationary case. Dynamic output feedback and separation principle. Examples.

4. Robust Regulation of nonlinear systems
 Design via linearization. Regulation by integral control. Control of nonlinear systems via “Gain Scheduling”. Feedback linearization and input-output  linearization. Examples of stabilization and tracking via state feedback. Lyapunov-based design.  Robust “Set Point” control with integral action. Introduction to adaptive control with examples.

Readings/Bibliography

[1] G. Marro, Teoria dei Sistemi di controllo", Zanichelli Editore, 1999.  

[2] S. Rinaldi, C. Piccardi, "I Sistemi Lineari", Citta Studi Edizioni

[3] M. Tibaldi, "Progetto di sistemi di controllo", Pitagora editrice Bologna.

[4]  A. Isidori, “Nonlinear control systems”, Springer Verlag.

[5] H. Khalil, “Nonlinear systems”, Prentice Hall.

Teaching methods

Methdological lessons will be joined to Matlab-Simulink exercises. Assisted lab sessions are not foreseen during the course.

Assessment methods

Oral examination with presentation of a Matlab-simulink design

Teaching tools

Mainly the black-board will be used during lessons. Matlab-simulink will be used as simulation tool. 

Links to further information

http://www-lar.deis.unibo.it/people/lmarconi/studenti.html

Office hours

See the website of Lorenzo Marconi