Input design and data-driven approaches based on convex optimization for fault diagnosis in linear systems

The complexity of automated systems has grown considerably during the past decades. This convolutes the observation of possible faults in these systems. If not being revealed timely, such faults can lead to catastrophic failures. As a result, there is a continuous interest in sophisticated fault diagnosis techniques. Since it is generally...

An Extended Kalman Filter for Magnetic Field SLAM Using Gaussian Process Regression

We present a computationally efficient algorithm for using variations in the ambient magnetic field to compensate for position drift in integrated odometry measurements (dead-reckoning estimates) through simultaneous localization and mapping (SLAM). When the magnetic field map is represented with a reduced-rank Gaussian process (GP) using...

Observing and tracking bandlimited graph processes from sampled measurements

A critical challenge in graph signal processing is the sampling of bandlimited graph signals; signals that are sparse in a well-defined graph Fourier domain. Current works focused on sampling time-invariant graph signals and ignored their temporal evolution. However, time can bring new insights on sampling since sensor, biological, and...

LQG control with minimum directed information: Semidefinite programming approach

We consider a discrete-time Linear-QuadraticGaussian (LQG) control problem in which Massey’s directed information from the observed output of the plant to the control input is minimized while required control performance is attainable. This problem arises in several different contexts, including joint encoder and controller design for data-rate...

Framework for state and unknown input estimation of linear time-varying systems

The design of unknown-input decoupled observers and lters requires the assumption of an existence condition in the literature.This paper addresses an unknown input ltering problem where the existence condition is not satised. Instead of designing a traditional unknown input decoupled lter, a Double-Model Adaptive Estimation approach is extended...

Model-based force identification in experimental ice-structure interaction by means of Kalman filtering

The level-ice forces exerted on a scale model of a compliant bottom founded structure are identified from noncollocated strain and acceleration measurements by means of a joint input-state estimation algorithm. The identification is performed based on two different finite element models: one entirely based on the blueprints of the structure, and...