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Lecture
Networked Control Systems: Laplacian Matrix and Consensus
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Related lectures (29)
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Laplacian Matrix: Properties and Examples
Explores the Laplacian matrix, time-varying consensus theorems, and balanced graphs in networked control systems.
Graphical Models: Representing Probabilistic Distributions
Covers graphical models for probabilistic distributions using graphs, nodes, and edges.
Networked Control Systems: Properties and Connectivity
Explores properties of matrices, irreducibility, and graph connectivity in networked control systems.
Graph Algorithms: Modeling and Representation
Covers the basics of graph algorithms, focusing on modeling and representation of graphs in memory.
Expander Graphs: Properties and Eigenvalues
Explores expanders, Ramanujan graphs, eigenvalues, Laplacian matrices, and spectral properties.
Graph Algorithms: Modeling and Traversal
Covers graph algorithms, modeling relationships between objects, and traversal techniques like BFS and DFS.
Laplacian Matrix in Networked Control Systems
Explores the Laplacian matrix in electric and mechanical networks, consensus, and properties of Laplacian matrices in networked control systems.
Graph Algorithms: Basics
Introduces the basics of graph algorithms, covering traversal, representation, and data structures for BFS and DFS.
Irreducible Matrices and Strong Connectivity
Explores irreducible matrices and strong connectivity in networked control systems, emphasizing the importance of adjacency matrices and graph structures.
Consensus in Networked Control Systems
Explores consensus in networked control systems through graph weight design and matrix properties.
