Difference between revisions of "CDS270(Fall2014)"

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Revision as of 23:21, 18 September 2014

Networked Control Systems


Tu/Th 1:30-3 pm, 243 ANB

Instructor: Yilin Mo (yilinmo@caltech.edu)

Pre-requisites

Undergraduate linear algebra, probability and signal processing, understanding of modern (state space) control theory


Course Schedule

Week Date Topic Reading
1
30 Sept (Tu) Course Overview
2 Oct (Th) State Estimation, Kalman Filtering Kalman Filtering
2 7 Oct (Tu) Discrete-time Algebraic Riccati Equations
9 Oct (Th) Estimation over Lossy Networks
3 14 Oct (Tu)
16 Oct (Th)
4 21 Oct (Tu)
9 Oct (Th) Estimation over Lossy Networks
5 7 Oct (Tu) Discrete-time Algebraic Riccati Equations
9 Oct (Th) Estimation over Lossy Networks
6 7 Oct (Tu) Discrete-time Algebraic Riccati Equations
9 Oct (Th) Estimation over Lossy Networks
7 7 Oct (Tu) Discrete-time Algebraic Riccati Equations
9 Oct (Th) Estimation over Lossy Networks
8 7 Oct (Tu) Discrete-time Algebraic Riccati Equations
9 Oct (Th) Estimation over Lossy Networks
9 7 Oct (Tu) Discrete-time Algebraic Riccati Equations
9 Oct (Th) Estimation over Lossy Networks
10 7 Oct (Tu) Discrete-time Algebraic Riccati Equations
9 Oct (Th) Estimation over Lossy Networks

Course Description

Networked control systems are spatially distributed systems for which the communication between sensors, actuators and controllers is supported by communication networks. Recent advances in sensing, communication technologies and computer architecture have led to the rapid growth of cost effective and low power devices, which dramatically increases the adaptability, efficiency and functionality of the control systems. However, networked control systems also introduce new challenges, as the information becomes local to each node and the information sharing between nodes may subject to network effects such as packet drop or delay.

In this course, we will review several recent advancements in networked control theory. We first consider a centralized control scheme, where the communication between the sensor, the controller and the actuator is unreliable. We then move to distributed control schemes and analyze the consensus algorithm, as it is key for many distributed control applications. Next, we study the performance of a consensus-based distributed inference algorithm. Finally, we discuss the consensus algorithm in adversarial environment.


Course Administration

There is no required homework and no midterm or final exam. Course grades will be based on a research presentation on last week.