Event based state estimation with time synchronous updates

To reduce the amount of data transfer in networked systems, measurements are usually taken only when an event occurs rather than at each synchronous sample instant. However, this complicates estimation problems considerably, especially in the situation when no measurement is received anymore. The goal of this paper is therefore to develop a...

State fusion with unknown correlation: Ellipsoidal intersection

This article focuses on the problem of fusing two prior Gaussian estimates into a single estimate, when the correlation is unknown. Existing solutions either lead to a conservative fusion result, as the chosen parametrization focuses on the fusion formulas instead of correlations, or they are computationally expensive. The contribution of this...

Observer design for DC/DC power converters with bilinear averaged model

Increased demand for high bandwidth and high efficiency made full state-feedback control solutions very attractive to power-electronics community. However, full state measurement is economically prohibitive for a large range of applications. Moreover, state measurements in switching power converters are very noisy. These facts make the...

On the dispersion of the Kalman filtering algorithm

Distributed Kalman filtering (DKF) is a rapidly emerging highly relevant research topic within the control systems community, mainly due to recent advances in (wireless) sensor networks. Application examples of DKF vary from estimating the position and speed of each vehicle on a highway, to condition monitoring of a bridge via accelerometers.

A distributed Kalman filter with global covariance

Most distributed Kalman filtering (DKF) algorithms for sensor networks calculate a local estimate of the global state-vector in each node. An important challenge within distributed estimation is that all sensors in the network contribute to the local estimate in each node. In this paper, a novel DKF algorithm is proposed with the goal of...

Empirical case-studies of state fusion via ellipsoidal intersection

This article presents a practical assessment of the recently developed state fusion method ellipsoidal intersection and focusses on distributed state estimation in sensor networks. It was already proven that this fusion method combines strong fundamental properties with attractive features in accuracy and computational requirements. However,...

State fusion with unknown correlation: Ellipsoidal intersection

Some crucial challenges of estimation over sensor networks are reaching consensus on the estimates of different systems in the network and separating the mutual information of two estimates from their exclusive information. Current fusion methods of two estimates tend to bypass the mutual information and directly optimize the fused estimate....

On integration of event-based estimation and robust MPC in a feedback loop

The main purpose of event-based control, if compared to periodic control, is to minimize data transfer or processing power in networked control systems. Current methods have an (implicit) dependency between triggering the events and the control algorithm. To decouple these two, we introduce an event-based state estimator in between the sensor...

On event based state estimation

To reduce the amount of data transfer in networked control systems and wireless sensor networks, measurements are usually taken only when an event occurs, rather than at each synchronous sampling instant. However, this complicates estimation and control problems considerably. The goal of this paper is to develop a state estimation algorithm that...

On-line identification of vehicle fuel consumption for energy and emission management: An LTP System Analysis

Abstract—An Energy Management (EM) system traditionally relies on (quasi) static maps offering efficiency parameters of the vehicle powertrain. During a vehicle’s life span, these maps lose validity, so optimal performance for EM is not assured. This paper presents a proof-of-concept for a novel measurement system, estimating important engine...

An overview of non-centralized Kalman filters

The usage of Wireless Sensor Networks (WSNs) for state-estimation has recently gained increasing attention due to its cost effectiveness and feasibility. One of the major challenges of state-estimation via WSNs is the distribution of the centralized state-estimator among the nodes in the network. Significant emphasis has been on developing non...