Networked Control Systems

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Related lectures (30)

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Algebraic Graph Theory: Matrices and Connectivity

Explores algebraic graph theory applied to networked control systems and consensus algorithms.

Graphs and Networks: Basics and Applications

Introduces the basics of graphs and networks, covering definitions, paths, trees, flows, circulation, and spanning trees.

Learning and Control for Complex Systems

Explores learning and control for complex systems, addressing challenges and opportunities in technology and interdisciplinary research.

Graphical Models: Representing Probabilistic Distributions

Covers graphical models for probabilistic distributions using graphs, nodes, and edges.

Consensus Theorem for Communication Networks

Explores the consensus theorem for communication networks and the implications of various consensus properties in networked control systems.

Control and Field Level Devices

Covers the fundamentals of control and field level devices in industrial automation, including PLCs, programming, different types of controllers, and advanced control strategies.

Statistical Analysis of Network Data

Introduces network data structures, models, and analysis techniques, emphasizing permutation invariance and Erdős-Rényi networks.

Graph Algorithms: Modeling and Representation

Covers the basics of graph algorithms, focusing on modeling and representation of graphs in memory.

Primitive Matrices and Spectral Properties in Networked Control Systems

Explores primitive matrices, dominant eigenvalues, and spectral properties in networked control systems.

Graphs in Deep Learning: Applications and Techniques

Explores the role of graphs in deep learning, focusing on their structure, applications, and techniques for processing graph data.