Adaptive Method of Helicopter Track and Balance

2004 ◽  
Vol 127 (2) ◽  
pp. 275-282 ◽  
Author(s):  
Shengda Wang ◽  
Kourosh Danai ◽  
Mark Wilson
Keyword(s):  
Neural Networks ◽  
A Priori ◽  
Adaptive Method ◽  
Estimation Accuracy ◽  
A Priori Knowledge ◽  
Interval Model ◽  
Vibration Data ◽  
Stochastic Nature ◽  
Flight Regimes ◽  
Helicopter Vibration

An adaptive method of helicopter track and balance is introduced to improve the search for the required blade adjustments. In this method, an interval model is used to represent the range of effect of blade adjustments on helicopter vibration, instead of exact values, to cope with the nonlinear and stochastic nature of aircraft vibration. The coefficients of the model are initially defined according to sensitivity coefficients between the blade adjustments and helicopter vibration, to include the ‘a priori’ knowledge of the process. The model coefficients are subsequently transformed into intervals and updated after each tuning iteration to improve the model’s estimation accuracy. The search for the required blade adjustments is performed according to this model by considering the vibration estimates of all of the flight regimes to provide a comprehensive solution for track and balance. The effectiveness of the proposed method is evaluated in simulation using a series of neural networks trained with actual vibration data. The results indicate that the proposed method improves performance according to several criteria representing various aspects of track and balance.

A Forward Approach for Helicopter Track and Balance

2002 ◽  
Author(s):  
Shengda Wang ◽  
Kourosh Danai
Keyword(s):  
Simulation Model ◽  
Forward Model ◽  
Estimation Accuracy ◽  
Vibration Measurements ◽  
Interval Model ◽  
Stochastic Nature ◽  
Sensitivity Coefficients ◽  
Helicopter Vibration

A method of helicopter track and balance is introduced that uses a forward-model to search for the appropriate blade modifications. This method uses an interval model to represent the ranges of effects of blade modifications on helicopter vibration, instead of exact values, in order to cope with the stochastic nature of aircraft vibration. The coefficients of the interval model are initially defined according to sensitivity coefficients between the blade modifications and helicopter vibration, but they are subsequently updated after each tuning iteration to improve the model’s estimation accuracy. The effectiveness of the proposed method is demonstrated through a simulation model that represents experimental vibration measurements of Black Hawk helicopters.

Incorporating a priori knowledge into neural networks

1995 ◽  
Vol 31 (22) ◽  
pp. 1930-1931 ◽  
Author(s):  
D. Anguita ◽  
S. Rovetta ◽  
S. Ridella ◽  
R. Zunino
Keyword(s):  
Neural Networks ◽  
A Priori ◽  
A Priori Knowledge ◽  
Priori Knowledge

WTLS-Bayes Filter-based Relative Pose Estimation for Space Non-cooperative Unknown Target Without a Priori Knowledge

2021 ◽  
Author(s):  
CHENGGUANG ZHU ◽  
zhongpai Gao ◽  
Jiankang Zhao ◽  
Haihui Long ◽  
Chuanqi Liu
Keyword(s):  
Pose Estimation ◽  
A Priori ◽  
Convergence Time ◽  
Estimation Accuracy ◽  
A Priori Knowledge ◽  
One Step ◽  
Relative Pose ◽  
The One ◽  
Negative Factors ◽  
Priori Knowledge

Abstract The relative pose estimation of a space noncooperative target is an attractive yet challenging task due to the complexity of the target background and illumination, and the lack of a priori knowledge. Unfortunately, these negative factors have a grave impact on the estimation accuracy and the robustness of filter algorithms. In response, this paper proposes a novel filter algorithm to estimate the relative pose to improve the robustness based on a stereovision system. First, to obtain a coarse relative pose, the weighted total least squares (WTLS) algorithm is adopted to estimate the relative pose based on several feature points. The resulting relative pose is fed into the subsequent filter scheme as observation quantities. Second, the classic Bayes filter is exploited to estimate the relative state except for moment-of-inertia ratios. Additionally, the one-step prediction results are used as feedback for WTLS initialization. The proposed algorithm successfully eliminates the dependency on continuous tracking of several fixed points. Finally, comparison experiments demonstrate that the proposed algorithm presents a better performance in terms of robustness and convergence time.

scholarly journals Vibration Data Processing for Bedload Monitoring in Underwater Environments

2020 ◽  
Vol 12 (17) ◽  
pp. 2797
Author(s):  
Gabriel Vasile
Keyword(s):  
Data Processing ◽  
Total Energy ◽  
Steel Plate ◽  
A Priori ◽  
A Priori Knowledge ◽  
Vibration Spectra ◽  
Vibration Data ◽  
Accurate Localization ◽  
Set Up ◽  
Processing Framework

This paper proposes a novel data processing framework dedicated to bedload monitoring in underwater environments. After calibration, by integration the of total energy in the nominal bandwidth, the proposed experimental set-up is able to accurately measure the mass of individual sediments hitting the steel plate. This requires a priori knowledge of the vibration transients in order to match a predefined dictionary. Based on unsupervised hierarchical agglomeration of complex vibration spectra, the proposed algorithms allow accurate localization of the transients corresponding to the shocks created by sediment impacts on a steel plate.

Damage Mechanisms Identification in FRP Using Acoustic Emission and Artificial Neural Networks

2006 ◽  
Vol 514-516 ◽  
pp. 789-793 ◽  
Author(s):  
Rui de Oliveira ◽  
António Torres Marques
Keyword(s):  
Neural Networks ◽  
Acoustic Emission ◽  
Artificial Neural Networks ◽  
Tensile Test ◽  
A Priori ◽  
The Self ◽  
A Priori Knowledge ◽  
Self Organizing Maps ◽  
Damage Mechanisms ◽  
Artificial Neural

In this study is proposed a procedure for damage discrimination based on acoustic emission signals clustering using artificial neural networks. An unsupervised methodology based on the self-organizing maps of Kohonen is developed considering the lack of a priori knowledge of the different signal classes. The methodology is described and applied to a cross-ply glassfibre/ polyester laminate submitted to a tensile test. In this case, six different AE waveforms were identified. The damage sequence could so be identified from the modal nature of those waves.

A Survey on Neural Networks in Automated Negotiations

2011 ◽  
pp. 1524-1529 ◽  
Author(s):  
Ioannis Papaioannou ◽  
Ioanna Roussaki ◽  
Miltiades Anagnostou
Keyword(s):  
Neural Networks ◽  
A Priori ◽  
Research Field ◽  
A Priori Knowledge ◽  
Automated Negotiation ◽  
Multilateral Negotiation ◽  
The Third ◽  
Automated Negotiations ◽  
Single Instance ◽  
Or Modelling

Automated negotiation is a very challenging research field that is gaining momentum in the e-business domain. There are three main categories of automated negotiations, classified according to the participating agent cardinality and the nature of their interaction (Jennings, Faratin, Lomuscio, Parsons, Sierra, & Wooldridge, 2001): the bilateral, where each agent negotiates with a single opponent, the multi-lateral which involves many providers and clients in an auction-like framework and the argumentation/persuasion-based models where the involving parties use more sophisticated arguments to establish an agreement. In all these automated negotiation domains, several research efforts have focused on predicting the behaviour of negotiating agents. This work can be classified in two main categories. The first is based on techniques that require strong a-priori knowledge concerning the behaviour of the opponent agent in previous negotiation threads. The second uses mechanisms that perform well in single-instance negotiations, where no historical data about the past negotiating behaviour of the opponent agent is available. One quite popular tool that can support the latter case is Neural Networks (NNs) (Haykin, 1999). NNs are often used in various real world applications where the estimation or modelling of a function or system is required. In the automated negotiations domain, their usage aims mainly to enhance the performance of negotiating agents in predicting their opponents’ behaviour and thus, achieve better overall results on their behalf. This paper provides a survey of the most popular automated negotiation approaches that are using NNs to estimate elements of the opponent’s behaviour. The rest of this paper is structure as follows. The second section elaborates on the state of the art bilateral negotiation frameworks that are based on NNs. The third section briefly presents the multilateral negotiation solutions that exploit NNs. Finally, in the last section a brief discussion on the survey is provided.

scholarly journals System Identification Using Multilayer Differential Neural Networks: A New Result

2012 ◽  
Vol 2012 ◽  
pp. 1-20 ◽  
Author(s):  
J. Humberto Pérez-Cruz ◽  
A. Y. Alanis ◽  
José de Jesús Rubio ◽  
Jaime Pacheco
Keyword(s):  
Neural Networks ◽  
Upper Bound ◽  
Exponential Convergence ◽  
Dead Zone ◽  
A Priori ◽  
A Priori Knowledge ◽  
Unmodeled Dynamics ◽  
A Value ◽  
Tuning Process ◽  
Learning Law

In previous works, a learning law with a dead zone function was developed for multilayer differential neural networks. This scheme requires strictly a priori knowledge of an upper bound for the unmodeled dynamics. In this paper, the learning law is modified in such a way that this condition is relaxed. By this modification, the tuning process is simpler and the dead-zone function is not required anymore. On the basis of this modification and by using a Lyapunov-like analysis, a stronger result is here demonstrated: the exponential convergence of the identification error to a bounded zone. Besides, a value for upper bound of such zone is provided. The workability of this approach is tested by a simulation example.

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