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Article
Publication date: 1 April 1999

Wang Bo, Luo Yimei and Wang Yuning

This paper improved the self‐adaptive filter‐forecasting model that is one of deterministic constant parameter forecasting models. An attenuation/gain function is drawn…

Abstract

This paper improved the self‐adaptive filter‐forecasting model that is one of deterministic constant parameter forecasting models. An attenuation/gain function is drawn into a direct iteration search method that belongs to the multi‐variables extreme value research based on the optimization theory, so that it combines the quantitative methods with the qualitative analysis by the experts and increases the explanatory ability and simulation level of the original model. As an empirical study, the authors applied the improved method to the forecasting of the middle‐term demand for cellular phones in China.

Details

Integrated Manufacturing Systems, vol. 10 no. 2
Type: Research Article
ISSN: 0957-6061

Keywords

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Article
Publication date: 4 March 2019

Yu Qiu, Baoquan Li, Wuxi Shi and Yimei Chen

The purpose of this paper is to present a visual servo tracking strategy for the wheeled mobile robot, where the unknown feature depth information can be identified…

Abstract

Purpose

The purpose of this paper is to present a visual servo tracking strategy for the wheeled mobile robot, where the unknown feature depth information can be identified simultaneously in the visual servoing process.

Design/methodology/approach

By using reference, desired and current images, system errors are constructed by measurable signals that are obtained by decomposing Euclidean homographies. Subsequently, by taking the advantage of the concurrent learning framework, both historical and current system data are used to construct an adaptive updating mechanism for recovering the unknown feature depth. Then, the kinematic controller is designed for the mobile robot to achieve the visual servo trajectory tracking task. Lyapunov techniques and LaSalle’s invariance principle are used to prove that system errors and the depth estimation error converge to zero synchronously.

Findings

The concurrent learning-based visual servo tracking and identification technology is found to be reliable, accurate and efficient with both simulation and comparative experimental results. Both trajectory tracking and depth estimation errors converge to zero successfully.

Originality/value

On the basis of the concurrent learning framework, an adaptive control strategy is developed for the mobile robot to successfully identify the unknown scene depth while accomplishing the visual servo trajectory tracking task.

Details

Assembly Automation, vol. 39 no. 3
Type: Research Article
ISSN: 0144-5154

Keywords

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