On line estimation of rolling resistance for intelligent tires

The analysis of a rolling tire is a complex problem of nonlinear elasticity. Although in the technical literature some tire models have been presented, the phenomena involved in the tire rolling are far to be completely understood. In particular, small knowledge comes even from experimental direct observation of the rolling tire, in terms of dynamic contact patch, instantaneous dissipation due to rubber-road friction and hysteretic behavior of the tire structure, and instantaneous grip. This paper illustrates in details a new powerful technology that the research group has developed in the context of the project OPTYRE. A new wireless optical system based on Fiber Bragg Grating strain sensors permits a direct observation of the inner tire stress when rolling in real conditions on the road. From this information, following a new suitably developed tire model, it is possible to identify the instant area of the contact patch, the grip conditions as well the instant dissipation, which is the object of the present work

A Measurement System for On-line Estimation of Weed Coverage

This paper describes two different solutions for the estimation of weed coverage. Both measuring systems discriminate the weed from the ground by means of the color difference between the weed and ground and can be used to on-line control tractor sprayers in order to reduce weedkiller use. The solutions differ with respect to the sensor type: one solution is based on a digital camera and a computer that analyzes the images and determines the weed amount, while the other simpler solution makes use of two photo detectors and an analog processing system. The camera-based solution provides an uncertainty of a few percentage, while the photo detector-based one, though extremely cheap, has an uncertainty of about 5% and suffers from changes in light conditions, which can alter the estimation

On-line nonparametric estimation

A survey of some recent results on nonparametric on-line estimation is presented. The first result deals with an on-line estimation for a smooth signal S(t) in the classic 'signal plus Gaussian white noise' model. Then an analogous on-line estimator for the regression estimation problem with equidistant design is described and justified. Finally some preliminary results related to the on-line estimation for the diffusion observed process are described

On-line estimation of steam boiler plant dynamics

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Adaptive filtering for stochastic risk premia in bond market

We consider the adaptive filtering problem for estimating the randomly changing risk premium and its system parameters for zero-coupon bond models. The term structure model for a zero-coupon bond is formulated including the stochastic risk-premium factor. We specify our observation data from the yield curve and bond data which are used to hedge some option claims. For the xed system parameters, the Kalman filter for the risk-premium and the factor process is constructed first. Secondly, by using the parallel filtering technique and resampling technique commonly used in particle filters, the on-line estimation algorithm for model parameters is constructed. Some simulation studies are nally presented

Reduced complexity on-line estimation of hidden Markov model parameters

In this paper we propose and study low complexity algorithms for on-line estimation of hidden Markov model (HMM) parameters. The estimates approach the true model parameters as the measurement noise approaches zero, but otherwise give improved estimates, albeit with bias. On a nite data set in the high noise case, the bias may not be signi cantly more severe than for a higher complexity asymptotically optimal scheme. Our algorithms require O(N3) calculations per time instant, where N is the number of states. Previous algorithms based on earlier hidden Markov model signal processing methods, including the expectation-maximumisation (EM) algorithm require O(N4) calculations per time instant

Application of neural network observer for on-line estimation of salient-pole synchronous generators' dynamic parameters using the operating data

Parameter identification is critical for modern control strategies in electrical power systems which is considered both dynamic performance and energy efficiency. This paper presents a novel application of ANN observers in estimating and tracking Salient-Pole Synchronous Generator Dynamic Parameters using time-domain, on-line disturbance measurements. The data for training ANN Observers are obtained through off-line simulations of a salient-pole synchronous generator operating in a one-machine-infinite-bus environment. The Levenberg-Marquardt algorithm has been adopted and assimilated into the back-propagation learning algorithm for training feed-forward neural networks. The inputs of ANNs are organized in conformity with the results of the observability analysis of synchronous generator dynamic parameters in its dynamic behavior. A collection of ANNs with same inputs but different outputs are developed to determine a set of the dynamic parameters. The ANNs are employed to estimate the dynamic parameters by the measurements which are carried out within each kind of fault separately. The trained ANNs are tested with on-line measurements to identify the dynamic parameters. Simulation studies indicate the ANN observer has a great ability to identify the dynamic parameters of salient-pole synchronous generator. The results also show that the tests which have given better results in estimation of each dynamic parameter can be obtained