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B028334 - NETWORKED CONTROL SYSTEMS
Main information
Teaching Language
Course Content
Suggested readings
Learning Objectives
Prerequisites
Teaching Methods
Type of Assessment
Course program
Academic Year 2019-20
Course year
Second year - Second Semester
Belonging Department
Information Engineering (DINFO)
Modulo di sola Frequenza of
Scientific Area
ING-INF/04 - SYSTEMS AND CONTROL ENGINEERING
Credits
6
Teaching Hours
48
Teaching Term
02/03/2020 ⇒ 12/06/2020
Attendance required
No
Type of Evaluation
Giudizio Finale
Course Content
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Course program
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Lectureship
Mutuality
Course teached as:
B028336 - SISTEMI DI CONTROLLO SU RETE
Second Cycle Degree in ELECTRICAL AND AUTOMATION ENGINEERING
Curriculum INGEGNERIA ELETTRICA
B028336 - SISTEMI DI CONTROLLO SU RETE
Second Cycle Degree in ELECTRICAL AND AUTOMATION ENGINEERING
Curriculum INGEGNERIA ELETTRICA
Teaching Language
Italian
Course Content
This course covers modelling of networked control systems, analysis and design tools for the design of network scheduling and control algorithms, resource-aware control, robustness against network misbehaviour and cyber-security, and data-driven control, with applications to centralised and distributed systems (e.g. wireless automation and sensor networks).
Suggested readings (Search our library's catalogue)
Lectures notes and selected articles.
Learning Objectives
This course aims at providing methodologies for modelling, analysing and designing networked control systems, centralised and distributed, with performance and resource constraints, while taking into account possible malfunctioning in the network. This course also provides methodologies for data-driven control design. As background theories, we will cover switched and hybrid systems theories, as well as relevant tools from convex programming.
Prerequisites
Fondamenti di Automatica (Control Engineering).
Teaching Methods
Lectures and tutorials.
Type of Assessment
Take-home assignment and oral exam.
Course program
1. INTRODUCTION TO NETWORKED CONTROL SYSTEMS
What is a networked control system. Examples in control theory and engineering. Challenges and opportunities for control engineering.
2. BASICS OF NETWORKED CONTROL SYSTEMS
Main ingredients of networked control systems (NCS) with examples. Modelling of NCS: how to derive a mathematical model that describes the behaviour of a NCS. Hybrid systems.
3. ANALYSIS AND DESIGN OF NETWORKED CONTROL SYSTEMS
Basic tools for analysing and designing NCS. Recap of Lyapunov stability and ISS. Analysis and design of NCS through discretisation and emulation techniques. Scheduling problems.
4. RESOURCE-AWARE CONTROL
Advanced tools for scheduling in NCS. Event-based control for centralised and distributed systems. Event-based consensus through broadcasting. Self-triggered control.
5. ROBUSTNESS
Robustness of NCS to delays and packet loss. Analysis via switched systems theory. Robustness of NCS to misbehaving network units. Algorithms for robust network consensus, Cyber-physical systems security.
6. DATA-DRIVEN CONTROL
Data-driven control. Non-parametric representation of dynamical systems. Data-driven control through Linear Matrix Inequalities and semidefinite programming. Applications in the area of networked systems.
What is a networked control system. Examples in control theory and engineering. Challenges and opportunities for control engineering.
2. BASICS OF NETWORKED CONTROL SYSTEMS
Main ingredients of networked control systems (NCS) with examples. Modelling of NCS: how to derive a mathematical model that describes the behaviour of a NCS. Hybrid systems.
3. ANALYSIS AND DESIGN OF NETWORKED CONTROL SYSTEMS
Basic tools for analysing and designing NCS. Recap of Lyapunov stability and ISS. Analysis and design of NCS through discretisation and emulation techniques. Scheduling problems.
4. RESOURCE-AWARE CONTROL
Advanced tools for scheduling in NCS. Event-based control for centralised and distributed systems. Event-based consensus through broadcasting. Self-triggered control.
5. ROBUSTNESS
Robustness of NCS to delays and packet loss. Analysis via switched systems theory. Robustness of NCS to misbehaving network units. Algorithms for robust network consensus, Cyber-physical systems security.
6. DATA-DRIVEN CONTROL
Data-driven control. Non-parametric representation of dynamical systems. Data-driven control through Linear Matrix Inequalities and semidefinite programming. Applications in the area of networked systems.