Difference between revisions of "CDS270(Fall2014)"
(Created page with "CDS 270-1: Networked Control Systems TuTh 1:30-3 pm, 243 ANB Pre-requisites: Undergraduate linear algebra, probability and signal processing, understanding of modern (state spa...") |
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| − | + | <font color='blue' size='+2'>Networked Control Systems</font> | |
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| − | Pre-requisites | + | Tu/Th 1:30-3 pm, 243 ANB |
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| + | Instructor: Yilin Mo | ||
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| + | ==Pre-requisites== | ||
Undergraduate linear algebra, probability and signal processing, understanding of modern (state space) control theory | Undergraduate linear algebra, probability and signal processing, understanding of modern (state space) control theory | ||
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| − | Course Description | + | ==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. | 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 Goals | + | ==Course Goals== |
1. Understanding of some new topics in the area of networked control theory | 1. Understanding of some new topics in the area of networked control theory | ||
2. Understanding of techniques, such as Riccati equation, non-negative matrix theory, large deviation and linear structured system theory, which could be useful for other areas of research | 2. Understanding of techniques, such as Riccati equation, non-negative matrix theory, large deviation and linear structured system theory, which could be useful for other areas of research | ||
| − | Syllabus | + | ==Syllabus== |
1. Estimation and Control over Networks: Classical Kalman Filtering, Algebraic Riccati Equations, Estimation and Control over Lossy Networks | 1. Estimation and Control over Networks: Classical Kalman Filtering, Algebraic Riccati Equations, Estimation and Control over Lossy Networks | ||
2. Distributed Control: Non-negative Matrix, Perron-Frobenius Theorem, Average Consensus, Randomized Gossip Algorithm, Convergence Rate, Consensus with Additive Noise | 2. Distributed Control: Non-negative Matrix, Perron-Frobenius Theorem, Average Consensus, Randomized Gossip Algorithm, Convergence Rate, Consensus with Additive Noise | ||
3. Distributed Inference: Hypothesis Testing, Large Deviation (Chernoff’s Lemma and Cramer’s Theorem), Asymptotic Performance of Distributed Hypothesis Testing | 3. Distributed Inference: Hypothesis Testing, Large Deviation (Chernoff’s Lemma and Cramer’s Theorem), Asymptotic Performance of Distributed Hypothesis Testing | ||
4. Security: Fault Detection and Identification, Generic Properties of Linear Structured Systems, Detectabiliy and Identifiability of Malicious Nodes in Consensus Algorithms. | 4. Security: Fault Detection and Identification, Generic Properties of Linear Structured Systems, Detectabiliy and Identifiability of Malicious Nodes in Consensus Algorithms. | ||
Revision as of 22:50, 18 September 2014
Networked Control Systems
Tu/Th 1:30-3 pm, 243 ANB
Instructor: Yilin Mo
Pre-requisites
Undergraduate linear algebra, probability and signal processing, understanding of modern (state space) control theory
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 Goals
1. Understanding of some new topics in the area of networked control theory 2. Understanding of techniques, such as Riccati equation, non-negative matrix theory, large deviation and linear structured system theory, which could be useful for other areas of research
Syllabus
1. Estimation and Control over Networks: Classical Kalman Filtering, Algebraic Riccati Equations, Estimation and Control over Lossy Networks 2. Distributed Control: Non-negative Matrix, Perron-Frobenius Theorem, Average Consensus, Randomized Gossip Algorithm, Convergence Rate, Consensus with Additive Noise 3. Distributed Inference: Hypothesis Testing, Large Deviation (Chernoff’s Lemma and Cramer’s Theorem), Asymptotic Performance of Distributed Hypothesis Testing 4. Security: Fault Detection and Identification, Generic Properties of Linear Structured Systems, Detectabiliy and Identifiability of Malicious Nodes in Consensus Algorithms.
