Cooperative Filtering and Parameter Identification for Advection-Diffusion Processes Using a Mobile Sensor Network

This article presents an online parameter identification scheme for advection-diffusion processes using data collected by a mobile sensor network. The advection-diffusion equation is incorporated into the information dynamics associated with the trajectories of the mobile sensors. A constrained cooperative Kalman filter is developed to provide estimates of the field values and gradients along the trajectories of the mobile sensors so that the temporal variations in the field values can be estimated. This leads to a co-design scheme for state estimation and parameter identification for advection-diffusion processes that is different from comparable schemes using sensors installed at fixed spatial locations. Using state estimates from the constrained cooperative Kalman filter, a recursive least-square (RLS) algorithm is designed to estimate unknown model parameters of the advection-diffusion processes. Theoretical justifications are provided for the convergence of the proposed cooperative Kalman filter by deriving a set of sufficient conditions regarding the formation shape and the motion of the mobile sensor network. Simulation and experimental results show satisfactory performance and demonstrate the robustness of the algorithm under realistic uncertainties and disturbances

Dynamic Mathematical Modelling of the Removal of Hydrophilic VOCs by Biotrickling Filters

A mathematical model for the simulation of the removal of hydrophilic compounds using biotrickling filtration was developed. The model takes into account that biotrickling filters operate by using an intermittent spraying pattern. During spraying periods, a mobile liquid phase was considered, while during non-spraying periods, a stagnant liquid phase was considered. The model was calibrated and validated with data from laboratory- and industrial-scale biotrickling filters. The laboratory experiments exhibited peaks of pollutants in the outlet of the biotrickling filter during spraying periods, while during non-spraying periods, near complete removal of the pollutant was achieved. The gaseous outlet emissions in the industrial biotrickling filter showed a buffered pattern; no peaks associated with spraying or with instantaneous variations of the flow rate or inlet emissions were observed. The model, which includes the prediction of the dissolved carbon in the water tank, has been proven as a very useful tool in identifying the governing processes of biotrickling filtration

Adaptive Observation Strategy for Dispersion Process Estimation Using Cooperating Mobile Sensors ⋆

Abstract: Efficient online state estimation of dynamic dispersion processes plays an important role in a variety of safety-critical applications. The use of mobile sensor platforms is increasingly considered in this context, but implies the generation of situation-dependent vehicle trajectories providing high information gain in real-time. In this paper, a new adaptive observation strategy is presented combining state estimation based on partial differential equation models of the dispersion process with a model-predictive control approach for multiple cooperating mobile sensors. In a repeating sequential procedure, based on the Ensemble Transform Kalman Filter, the uncertainty of the current estimate is determined and used to find valuable measurement locations. Those serve as target points for the controller providing optimal trajectories subject to the vehicles ’ motion dynamics and cooperation constraints. First promising results regarding accuracy and efficiency were obtained

Localization performance evaluation of extended kalman filter in wireless sensors network

This paper evaluates the positioning and tracking performance of Extended Kalman Filter (EKF) in wireless sensors network. The EKF is a linear approximation of statistical Kalman Filter (KF) and has the capability to work efficiently in non-linear systems. The EKF is based on an iterative process of estimating current state information from the previously estimated state. Its working is based on the linearization of observation model around the mean of current state information. The EKF has small computation complexity and requires low memory compared to other Bayesian algorithms which makes it very suitable for low powered mobile devices. This paper evaluates the localization and tracking performance of EKF for (i) Position (P) model, (ii) Position-Velocity (PV) model and (iii) Position-Velocity-Acceleration (PVA) model. The EKF processes distance measurements from cricket sensors that are acquired through time difference of arrival between ultrasound and Radio Frequency (RF) signals. Further, localization performance under varying number of beacons/sensors is also evaluated in this paper. © 2014 Published by Elsevier B.V.Peer ReviewedPostprint (published version

Robust PID tuning. Application to a Mobile Robot Pathtraking problem.

IFAC Digital Control: Past,Present and Future of PlO Control.Terrassa.Spain.2000This paper presents a methodology for tuning PIDs considering the nominal performance and the robustness as control specifications. The synthesis procedure is similar to the Ziegler-Nichols method for PID controllers and can be easily used for industrial processes. As a workbench for testing the PID controller a mobile robot has been used. The path tracking problem of a mobile robot has been used as a workbench for testing the PID controller

Active Learning of Gaussian Processes for Spatial Functions in Mobile Sensor Networks

This paper proposes a spatial function modeling approach using mobile sensor networks, which potentially can be used for environmental surveillance applications. The mobile sensor nodes are able to sample the point observations of an 2D spatial function. On the one hand, they will use the observations to generate a predictive model of the spatial function. On the other hand, they will make collective motion decisions to move into the regions where high uncertainties of the predictive model exist. In the end, an accurate predictive model is obtained in the sensor network and all the mobile sensor nodes are distributed in the environment with an optimized pattern. Gaussian process regression is selected as the modeling technique in the proposed approach. The hyperparameters of Gaussian process model are learned online to improve the accuracy of the predictive model. The collective motion control of mobile sensor nodes is based on a locational optimization algorithm, which utilizes an information entropy of the predicted Gaussian process to explore the environment and reduce the uncertainty of predictive model. Simulation results are provided to show the performance of the proposed approach. © 2011 IFAC

A Platform for Proactive, Risk-Based Slope Asset Management, Phase II

INE/AUTC 15.0