This paper studies a number of quantized feedback design problems for linear systems. We consider the case where quantizers are static (memoryless). The common aim of these design problems is to stabilize the given system or to achieve certain performance with the coarsest quantization density. Our main discovery is that the classical sector bound approach is nonconservative for studying these design problems. Consequently, we are able to convert many quantized feedback design problems to well-known robust control problems with sector bound uncertainties. In particular, we derive the coarsest quantization densities for stabilization for multiple-input-multiple-output systems in both state feedback and output feedback cases; and we also derive conditions for quantized feedback control for quadratic cost and H/sub /spl infin// performances.

The sector bound approach to quantized feedback control | NOVA. The University of Newcastle's Digital Repository

H∞ filtering for nonlinear networked systems with randomly occurring distributed delays, missing measurements and sensor saturation

The analysis and synthesis issues have gained widespread attention for complex dynamical networks (CDNs) over the past few years. Accordingly, some challenges including protocol-based scheduling, s...

A survey on state estimation of complex dynamical networks

Distributed fusion filter for networked stochastic uncertain systems with transmission delays and packet dropouts

Wireless sensor networks (WSNs) are extensively adopted for remote monitoring and tracking scenarios, such as battlefield surveillance, target detection and tracking, traffic condition detection, p...

Resource-efficient and secure distributed state estimation over wireless sensor networks: a survey

Improved robust finite-horizon Kalman filtering for uncertain networked time-varying systems

An event-triggered recursive state estimation approach for time-varying nonlinear complex networks with quantization effects

In this paper, a novel Kalman filter is developed for discrete-time linear systems with intermittent observations. The measurement data latency and dropout in transmission from the sensor to the filter is modeled by a group of Bernoulli distributed random variables. The minimum variance filter is designed when the measurement data packets are/are not time stamped by the reorganized innovation approach. Moreover, the steady-state behavior of the proposed Kalman filter is investigated. Finally, simulation results are presented to illustrate that the suggested estimator leads to the remarkably improved performance compared with the previously developed approaches.

Improved Kalman Filtering for Systems with Randomly Delayed and Lost Measurements

This paper investigates the problem of robust filtering for networked linear time-varying systems with norm-bounded modelling uncertainties. Delay and dropout of measurement data in the transmission from sensor to filter are both modelled by a Bernoulli distributed random sequence. Finite-horizon two-stage Kalman filters are introduced whether or not the data packets in the network are time stamped. The parameters of robust Kalman filters are determined such that the covariances of the estimation errors are bounded and these upper bounds are guaranteed to be minimal. Novel augmented state vectors are used to extract procedures for computation of filters’ parameters. Lastly, simulation results are presented to demonstrate the superior performance of the proposed approach compared the existing methods in the literature.

Hossein Rezaei

Papers

Improved robust finite-horizon Kalman filtering for uncertain networked time-varying systems

An event-triggered recursive state estimation approach for time-varying nonlinear complex networks with quantization effects

Improved Kalman Filtering for Systems with Randomly Delayed and Lost Measurements

Robust filtering for uncertain networked systems with randomly delayed and lost measurements

Event‐triggered distributed Kalman filter with consensus on estimation for state‐saturated systems