Application of a discrete event simulation for robust system design of a melt facility

This paper presents a methodology to identify robust operating regions through the selection of controllable factory variables, using discrete event simulation. A casting plant melt facility was used as an industrial test bed to develop these techniques. A robust system design was determined by response surface analysis of key production parameters. Furthermore, robust operating policies that maximise throughput, while minimizing work-in-progress and thus energy consumption were identified.

Robust extended Kalman filter based technique for location management in PCS networks

Provisioning of real-time multimedia sessions over wireless cellular network poses unique challenges due to frequent handoff and rerouting of a connection. For this reason, the wireless networks with cellular architecture require efficient user mobility estimation and prediction. This paper proposes using Robust Extended Kalman Filter as a location heading altitude estimator of mobile user for next cell prediction in order to improve the connection reliability and bandwidth efficiency of the underlying system. Through analysis we demonstrate that our algorithm reduces the system complexity (compared to existing approach using pattern matching and Kalman filter) as it requires only two base station measurements or only the measurement from the closest base station. Further, the technique is robust against system uncertainties due to inherent deterministic nature in the mobility model. Through simulation, we show the accuracy and simplicity in implementation of our prediction algorithm.

Robust parallel job scheduling infrastructure for service-oriented grid computing systems

Recent trends in grid computing development is moving towards a service-oriented architecture. With the momentum gaining for the service-oriented grid computing systems, the issue of deploying support for integrated scheduling and fault-tolerant approaches becomes paramount importance. To this end, we propose a scalable framework that loosely couples the dynamic job scheduling approach with the hybrid replications approach to schedule jobs efficiently while at the same time providing fault-tolerance. The novelty of the proposed framework is that it uses passive replication approach under high system load and active replication approach under low system loads. The switch between these two replication methods is also done dynamically and transparently.

Application of fast and robust equalization in communication technology

In this paper, the authors explore the potential of several popular equalization techniques while overcoming their disadvantages. First, extensive literature survey on equalization is conducted. The focus is on popular linear equalization algorithms such as the conventional least-mean-square (LMS) algorithm , The recursive least-squares (RLS) algorithm, the filtered-X LMS algorithm and their development. To overcome the slow convergence problem while keeping the simplicity of the LMS based algorithms, an H2 optimal initialization is proposed.

Robust extended kalman filter applied to location tracking and trajectory prediction for PCS networks

Provisioning of real-time multimedia sessions over wireless cellular network poses unique challenges due to frequent handoff and rerouting of a connection. For this reason, the wireless networks with cellular architecture require efficient user mobility estimation and prediction. This paper proposes using robust extended Kalman filter (REKF) as a location heading altitude estimator of mobile user for next cell prediction in order to improve the connection reliability and bandwidth efficiency of the underlying system. Through analysis we demonstrate that our algorithm reduces the system complexity (compared to existing approach using pattern matching and Kalman filter) as it requires only two base station measurements or only the measurement from the closest base station. Further, the technique is robust against system uncertainties due to inherent deterministic nature in the mobility model and more effective in comparison with the standard Kalman filter.

Robust control applied to metal spraying for rapid tooling

The spray forming process is a novel method of rapidly manufacturing tools and dies for stamping and injection operations. The process sprays molten tool steel from a set of arc spray guns onto a ceramic former to build up a thick steel shell. The volumetric contraction that occurs as the steel cools is offset by a volumetric expansion taking place within the sprayed steel, which allows the dimensional accurate tools to be produced. To ensure that the required phase transformation takes place, the temperature of the steel is regulated during spraying. The sprayed metal acts both as a source of mass and a source of heat and by adjusting the rate at which metal is sprayed; the surface temperature profile over the surface of the steel can be controlled. The temperature profile is measured using a thermal imaging camera and regulated by adjusting the rate at which the guns spray the steel. Because the temperature is regulated by adjusting the feed rate to an actuator that is moving over the surface, this is an example of mobile control, which is a class of distributed parameter control. The dynamic system has been controlled using a PI controller before. The paper describes the application of H∞ tracking type controller as the desire was for the average temperature to follow a desired profile. A study on the controllability of the underlying system was aimed at.

Robust terminal attitude navigation for autonomous vehicles

Here we define the terminal attitude of the pursuer with respect to a target and present a LQR and H¿ control approach to solving the problem of pursuer achieving a desired terminal attack/approach angle. The intercept or engagement criteria is defined in terms of both minimizing the miss distance and controlling the pursuer's body attitude with respect to the target at the terminal point. This approach in comparison to previous approaches consider the relativistic approach of the pursuer with respect to the target as opposed the absolute velocities of the two dynamic bodies, and have possible applications ranging from autonomous vehicle entry in to a mother craft to nossle engagements in on-flight refuelling or even in precision missile guidance. Here we also suitably formulate the H¿ control ideas directly applicable to the underlying problem and presents both state feedback and output feed back results for the case of finite horizon and non-zero initial conditions together with a optimal parameter value to achieve a desired terminal characteristic in terms of the original weighting parameters.

Robust parallel filtering for mobile agent tracking

In this paper we develop a robust method of target/mobile agent tracking involving two independent estimators with separate measurement systems. The outputs of the two estimators are combined using simple trigonometry (post-estimation data fusion) and provide a robust and reliable tracking path. We demonstrate that through the use of recent advances in robust set-value state estimation, our robust parallel filter approach performs well even when the individual filters do not. Brief comparisons with common data fusion methods are conducted in order to demonstrate the advantages of our parallel (post-estimation fusion) approach

Robust video/ultrasonic fusion based estimation for automotive applications

We describe how object estimation by a stationary or a non-stationary camera can be improved using recently-developed robust estimation ideas. The robustness of vision-based systems can be improved significantly by employing a Robust Extended Kalman Filter (REKF). The system performance is also enhanced by increasing the spatial diveristy in measurements via employing additional cameras for video capture. We describe a normal-flow based image segmentation technique to identify the object for the application of our proposed state estimation technique. Our simulations demonstrate that dynamic system modelling coupled with the application of a REKF significantly improves the estimation system performance, especially when large uncertainties are present.

Robust parallel filtering design of a swarm tracking system

Swarming networks of mobile autonomous agents require inter-agent position information in order perform various tasks. The primary control input for the majority of current control strategies is inter-agent distance information. In this paper we provide a robust parallel filter based tracking scheme that allows a mobile agent to track other multiple mobile agents. The distance, angle, and relative position is given in a direct target tracking output. This allows the mobile agent to decide which information is best suited for the particular objective

Robust video/ultrasonic fusion-based estimation for automotive applications

In this paper, we use recently developed robust estimation ideas to improve object tracking by a stationary or nonstationary camera. Large uncertainties are always present in vision-based systems, particularly, in relation to the estimation of the initial state as well as the measurement of object motion. The robustness of these systems can be significantly improved by employing a robust extended Kalman filter (REKF). The system performance can also be enhanced by increasing the spatial diversity in measurements via employing additional cameras for video capture. We compare the performances of various image segmentation techniques in moving-object localization and show that normal-flow-based segmentation yields comparable results to, but requires significantly less time than, optical-flow-based segmentation. We also demonstrate with simulations that dynamic system modeling coupled with the application of an REKF significantly improves the estimation system performance, particularly, when subjected to large uncertainties.

Radar target tracking via robust linear filtering

In this letter, we provide a robust version of a linear Kalman filter for target tracking based on a measurement conversion technique on the nonlinear radar measurements. We prove that the state estimation error is bounded in a probabilistic sense. We compare our approach with the current state of the art in converted radar measurement-based linear filtering.