Course Unit Profile

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Basic Information


Course Unit Code: 263II

Level of course unit

Second Cycle (Laurea magistrale) Degree Programme in Robotics and Automation Engineering

Year of study

Second year

Semester when the course is delivered

First semester

Number of ECTS credits allocated: 12

Name of Lecturer(s):

Prof.: Mario Innocenti

Prof.: Andrea Caiti

Language of instruction

Italian/English (Students Choice)

General Information

Learning outcomes

The student who successfully completes the course will be able:
- to possess the analytical tools for the estimation of uncertain parameters in dynamic systems;
- to compute appropriate models of dynamic systems based on parameter estimation and processing of deterministic and stochastic measurements;
- to analyze and model multivariable linear systems under a large class of structured and unstructured uncertainties;
- to design robust controllers in the framework of norm-based performance and stability specifications.
- to write software advanced code for the digital implementation of advanced identification and control techniques.

Course contents

- Elements of Signals and Sampling theory;
- Estimation theory (Bayesian estimation and Kalman filtering);
- White/Grey/Black box modeling; Box-Jenkins models, prediction, parametric and non parametric identification; spectrum estimation; Linear Matrix Inequalities;
- Linear multivariable systems (MIMO) review. Optimization, linear quadratic regulator, linear Gaussian optimal control;
- MIMO tools: 2-Block representation, internal stability, small gain theorem. State space and frequency approaches, Singular values, signal and system norms. H2 analysis;
- MIMO shaping, weighting functions, scaling. Performance limitations. Uncertainty classification, robustness Theorems. Robust Loop shaping, MIMO stability margins, structured singular value;
- Robust Control design: Kalman Inequality, Nyquist Criterion. LQG/LTR controller. H2 e Hinf control, D-K iteration. MU analysis and synthesis.

Specific Information

Prerequisites, co-requisites, as a prerequisite for further study


For this course the prerequisite/s is/are



Prerequisite for


Mode of delivery


face to face


Not mandatory

Teaching methods

Learning activities

Recommended or required reading

The recommended text for the "Identification of Uncertain Systems" module is:
L. Ljung: "System Identification: theory for the user", Prentice-Hall, 1987

The recommended texts for the "Control of Uncertain Systems" module are:
“Robust Control Systems: Theory and Case Studies”, Mackenroth, U. Springer, 2004
“Essentials of Robust Control”, Zhou, K., Doyle, J., Prentice Hall, 1998 (
Additional material, and class notes for the same module can be downloaded at:

Assessment methods and criteria

Assessment methods

Further information

The final grade is determined as the average of the two modules (identification and control).

Assessment criteria

The student preparation will assessed during the oral exam. The primary elements of evaluation are: - The ability to communicate in a clear and formal language appropriate for the advanced understanding of science and engineering; - the capability to demonstrate the knowledge of the material with the answer to questions in all topics of the program; - The ability to describe results of advanced scientific findings in the open literature, and the presentation of a design project in a clear, scientific manner, describing the individual effort and understanding of the problem; - The capability of showing a clear conceptual understanding of the overall engineering implications of the material presented in the course.

Work placement


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