Estimation and Control over Communication Networks

TLDR: This paper focuses on Kalman State Estimation in Networked Systems with Asynchronous Communication Channels and Switched Sensors and some properties of the Joint Entropy of a Random Vector and Discrete Quantity References Index.

Abstract: Preface Introduction Topological Entropy, Observability, Robustness, Stabilizability, and Optimal Control Stabilization of Linear Multiple Sensor Systems via Limited Capacity Communication Channels Detectability and Output Feedback Stabilizability of Nonlinear Systems via Limited Capacity Communication Channels Robust Set-Valued State Estimation via Limited Capacity Communication Channels An Analog of Shannon Information Theory: State Estimation and Stabilization of Linear Noiseless Plants via Noisy Discrete Channels An Analog of Shannon Information Theory: State Estimation and Stabilization of Linear Noisy Plants via Noisy Discrete Channels An Analog of Shannon Information Theory: Stable in Probability Control and State Estimation of Linear Noisy Plants via Noisy Discrete Channels Decentralized Stabilization of Linear Systems via Limited Capacity Communication Networks H-infinity State Estimation via Communication Channels Kalman State Estimation and Optimal Control Based on Asynchronously and Irregularly Delayed Measurements Optimal Computer Control via Asynchronous Communication Channels Linear-Quadratic Gaussian Optimal Control via Limited Capacity Communication Channels Kalman State Estimation in Networked Systems with Asynchronous Communication Channels and Switched Sensors Robust Kalman State Estimation with Switched Sensors Appendix A: Proof of Proposition 7.6.13 Appendix B: Some Properties of Square Ensembles of Matrices Appendix C: Discrete Kalman Filter and Linear-Quadratic Gaussian Optimal Control Problem Appendix D: Some Properties of the Joint Entropy of a Random Vector and Discrete Quantity References Index