Lectures in course ME-427: Networked control systems

Introduction to Networked Control Systems

Covers classic feedback control, modeling implications, challenges, and opportunities in Networked Control Systems.

Networked Control Systems: Fundamentals and Stability Analysis

Covers the fundamentals and stability analysis of Networked Control Systems, including software installation, dynamical systems, equilibrium states, and stability testing.

Networked Control Systems: Stability and Boundability

Explores stability and boundability in networked control systems, covering the impact of packet dropouts and the design of controllers for ensuring system stability.

Networked Control Systems: Stability and Dropout Analysis

Explores the stability of Networked Control Systems under deterministic packet drops and the estimation of the maximal drop rate.

Stability of Networked Control Systems

Explores the stability analysis of Networked Control Systems under stochastic packet dropouts, emphasizing mean-square stability and practical implications.

Networked Control Systems: Coordination Among Agents

Explores coordination among agents in networked control systems through graph theory and real-world examples.

Graph Theory: Connectivity and Properties

Explores the properties of undirected and directed graphs, emphasizing connectivity and network topology modeling.

Networked Control Systems: Graph Theory and Stochastic Matrices

Explores graph theory, stochastic matrices, consensus algorithms, and spectral properties in networked control systems.

Networked Control Systems: Properties and Connectivity

Explores properties of matrices, irreducibility, and graph connectivity in networked control systems.

Convergence of Adjacency Matrix: Spectral Properties and Consensus Theorem

Explores the convergence of adjacency matrix powers and consensus theorem for primitive and stochastic matrices, emphasizing spectral properties and networked control systems.

Primitive Matrices and Spectral Properties in Networked Control Systems

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

Networked Control Systems: Convergence Rate and Digraphs

Explores convergence rate in networked control systems and consensus in digraphs, emphasizing the challenges of computing Pess(A) and weight assignment.

Discrete-time Systems and Lyapunov Theory

Covers the review of linear discrete-time systems and Lyapunov theory, focusing on stability and equilibria.

Networked Control Systems: Strongly Connected Components and Condensation Digraphs

Explores strongly connected components and condensation digraphs in networked control systems, demonstrating their practical implications.

Consensus Theorem for Communication Networks

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

Consensus Algorithms: Weight Assignment and Applications

Explores the design of graph weights for consensus and applications in sensor networks.

Distributed Algorithms for Networked Control Systems

Explores distributed algorithms for networked control systems, covering consensus, least-squares regression, and time-varying communication networks.

Laplacian Matrix: Properties and Examples

Explores the Laplacian matrix, time-varying consensus theorems, and balanced graphs in networked control systems.

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.

Networked Control Systems: Consensus and Laplacian Flow

Explores the Laplacian flow and achieving consensus in networked control systems through examples and theorems.