Abstract The paper deals with implicit and explicit approaches for fractional nonlinear model order estimation using a benchmark model relating applied angular rate and neuron's firing intensity within the vestibu...
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Abstract The paper deals with implicit and explicit approaches for fractional nonlinear model order estimation using a benchmark model relating applied angular rate and neuron's firing intensity within the vestibular system. The implicit approach is based on an interacting multiple models scheme, where several extended Kalman filters with fixed fractional order nonlinear models are running in parallel. An alternative method based on augmented Unscented Kalman filter is proposed where, where the fractional order of the model is estimated explicitly within the filter state. The performance of the estimators is compared with respect to parameters of estimators and fractional derivative approximation.
Abstract In this paper it is studied the classical problem of target tracking by a new approach consisting in the treatment of the classical nonlinear measurement process in a form amenable for polynomial filtering wi...
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Abstract In this paper it is studied the classical problem of target tracking by a new approach consisting in the treatment of the classical nonlinear measurement process in a form amenable for polynomial filtering without the need of the measure map linearization, as required by other standard sub-optimal algorithms. The main idea is to transfer the nonlinearity of the measure map into a modification of the noise sequence distribution in a nongaussian white sequence. This is indeed the property required for Kalman filtering which, although non more optimal, remains to be the optimal linear filtering algorithm. Conditions for polynomial filtering are also satisfied, allowing to face the nongaussian nature of the modified noise sequence. Simulations show high performances of the proposed algorithm.
Abstract In this paper we describe a consensus based control strategy to obtain a formation of groups of mobile robots. Weighted graphs are used to obtain the desired formation–shape while avoiding collisions among t...
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Abstract In this paper we describe a consensus based control strategy to obtain a formation of groups of mobile robots. Weighted graphs are used to obtain the desired formation–shape while avoiding collisions among the robots. Since mobile robots usually move in unknown and unstructured environments, we show how to extend our control strategy to make the robots avoid collision with obstacles as well. To validate our control strategy, we provide both analytical proofs and simulative and experimental results.
Abstract Three approaches of iterative learning control (ILC) applied to a Gantry-Tau parallel kinematic robot are studied; ILC algorithms using 1) measured motor angles, 2) tool-position estimates, and for evaluation...
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Abstract Three approaches of iterative learning control (ILC) applied to a Gantry-Tau parallel kinematic robot are studied; ILC algorithms using 1) measured motor angles, 2) tool-position estimates, and for evaluation purposes, 3) measured tool position. The approaches are compared experimentally, with the tool performance evaluated using external sensors. It is concluded that the tool performance can be improved using tool-position estimates in the ILC algorithm, compared to when using motor-angle measurements. Applying ILC algorithms to a system following trajectories with so-called lead-in/lead-out is also considered in the paper.
During a Wet End break, the loss of paper feed through the paper machine causes loss of sensory information and the remaining parts of the process are operated in open-loop. This causes the stock composition in the He...
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During a Wet End break, the loss of paper feed through the paper machine causes loss of sensory information and the remaining parts of the process are operated in open-loop. This causes the stock composition in the Headbox to deviate substantially from the nominal specifications, causing paper quality (after start up) and paper machine runability issues. In this work, the Base Sheet Ash measurement of the scanner is estimated using a least absolute value (LAV) model which can then be used for control of the chalk valve during the breaks to keep the Headbox Ash within specified limits. The model is computed using a very fast optimization algorithm which is able to compute the LAV solution using only basic elementary operations. The proposed approach has been developed for a UK paper mill. (C) 2010 Elsevier Ltd. All rights reserved.
The recognition problem is considered in which the initial information is given by the values of similarity functions on pairs of objects. A generalization of the estimation algorithm model for this problem is propose...
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The recognition problem is considered in which the initial information is given by the values of similarity functions on pairs of objects. A generalization of the estimation algorithm model for this problem is proposed. A theory for the description and analysis of algebraic closures of the generalized and classical models is developed.
Abstract We have developed a star sensor system with four heads as a prototype of a future small and low-priced satellite attitude controller. To reduce size and cost of the controller, we integrate attitude sensors i...
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Abstract We have developed a star sensor system with four heads as a prototype of a future small and low-priced satellite attitude controller. To reduce size and cost of the controller, we integrate attitude sensors into star sensors with medium resolution and medium sensitivity and adopt Commercial Off-The-Shelf (COTS) electrical parts for key parts such as CPU and CCD. Newly developed computer architecture is introduced for high reliability with COTS parts under the harsh space environment. In this paper, we will focus on the new star identification algorithm dedicated for the proposed four-head star sensor system and evaluate it with ground test and on-orbit check-out data.
The fluoroBancroft algorithm is an analytical approach that converts a collection of fluorescence intensity measurements generated by an isolated sub-diffraction limit source into an estimate with nanometer-scale prec...
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The fluoroBancroft algorithm is an analytical approach that converts a collection of fluorescence intensity measurements generated by an isolated sub-diffraction limit source into an estimate with nanometer-scale precision of the source position. Based on this algorithm, we have developed a scheme for tracking single fluorescent particles in a confocal microscope. In this paper, we determine an optimal measurement constellation for the estimation algorithm. The position estimation bias and uncertainty arising from the photon counting statistics are calculated based on the assumption that the natural logarithm of a Poisson random variable with large rate can be approximated as a random variable with a Gaussian distribution. A sufficient condition for an unbiased measurement constellation and the optimal radius of a given constellation geometry with six measurements are then derived. The results are illustrated through numerical simulation.
Practical metrics for performance evaluation of estimation algorithms are discussed. A variety of metrics useful for evaluating various aspects of the performance of an estimation algorithm is introduced and justified...
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Practical metrics for performance evaluation of estimation algorithms are discussed. A variety of metrics useful for evaluating various aspects of the performance of an estimation algorithm is introduced and justified. They can be classified in two different ways: 1) absolute error measures (without a reference), relative error measures (with a reference), or frequency counts (of some events), and 2) optimistic (i.e., how good the performance is), pessimistic (i.e., how bad the performance is), or balanced (neither optimistic nor pessimistic). Pros and cons of these metrics and the widely-used RMS error are explained. The paper advocates replacing the RMS error in many cases by a measure called average Euclidean error.
This paper is an endeavour to address the problem of distributed leader selection in a formation of autonomous agents where the agents do not communicate directly via communication channels. The algorithm that the age...
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This paper is an endeavour to address the problem of distributed leader selection in a formation of autonomous agents where the agents do not communicate directly via communication channels. The algorithm that the agents use to select a leader relies on the agents observing each others’ behaviours. It is shown that the proposed algorithm is terminated, on average, in finite number of step and results in the selection of a leader for the formation. Moreover, It is established that the algorithm has some common elements with an algorithm widely used in data networks, i.e. Slotted Aloha. The application of the algorithm to a formation controlled by a nonlinear control law is studied and some numerical examples are presented to show the general performance of the algorithm.
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