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Tzu-Sung Wu,Mansour Karkoub,Chien-Ting Chen,Wen-Shyong Yu,Ming-Guo Her,Jui-Yiao Su 제어·로봇·시스템학회 2013 International Journal of Control, Automation, and Vol.11 No.6
It is proposed here to use a robust tracking design based on adaptive fuzzy control technique to control a class of multi-input-multi-output (MIMO) nonlinear systems with time delayed uncertainty in which each uncertainty is assumed to be bounded by an unknown gain. This technique will overcome modeling inaccuracies, such as drag and friction losses, effect of time delayed uncertainty, as well as parameter uncertainties. The proposed control law is based on indirect adaptive fuzzy control. A fuzzy model is used to approximate the dynamics of the nonlinear MIMO system; then, two on-line estimation schemes are developed to overcome the nonlinearities and identify the gains of the delayed state uncertainties, simultaneously. The advantage of employing an adaptive fuzzy system is the use of linear analytical results instead of estimating nonlinear system functions with an online update law. The adaptive fuzzy scheme uses a Variable Structure (VS) scheme to resolve the system uncertainties, time delayed uncertainty and the external disturbances such that H∞ tracking performance is achieved. The control laws are derived based on a Lyapunov criterion and the Riccati-inequality such that all states of the system are uniformly ultimately bounded (UUB). Therefore, the effect can be reduced to any prescribed level to achieve H∞ tracking performance. A two-connected inverted pendulums system on carts and a two-degree-of-freedom mass-spring-damper system are used to validate the performance of the proposed fuzzy technique for the control of MIMO nonlinear systems.
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Applying Multi-Objective Planning in Low-Carbon Product Design
Tsai Chi Kuo,Hsiao Min Chen,Chia Yi Liu,Jui-Che Tu,Tzu-Chang Yeh 한국정밀공학회 2014 International Journal of Precision Engineering and Vol. No.
In low-carbon product design, product environment, as well as economic and manufacturing capabilities, should be considered simultaneously. The purpose of this paper is to improve the product carbon footprint calculation efficiency. This study not only considers cost, supplier manufacturing capacity, and transport modes of product components from the viewpoint of low-carbon product design, but it also provides information on product GHG values, design phase cost, as well as the product decisions made by enterprises. In this way, enterprise objectives about carbon footprints and product cost can be met. Based on the product life cycle and product category specifications, this study, following the actual industry process flow, collects and calculates data on GHG emissions of components within the product life cycle, cost and supplier production capacity. The aim is to determine the operational parameters and constraint equations. Multi-objective planning is used to establish a low carbon optimal evaluation model. By reviewing carbon emissions in each phase, the study further determines whether or not to modify product structure and consumption in order to improve the efficiency of product carbon footprint calculation, reduce R&D cost, and help enterprises design low-carbon products. Finally, the findings are used for a case analysis.
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User Identification Design by Fusion of Face Recognition and Speaker Recognition
Chao-Yu Lin,Kai-Tai Song,Yi-Wen Chen,Shuo-Cheng Chien,Sin-Horng Chen,Chen-Yu Chiang,Jyh-Her Yang,Yi-Chiao Wu,Tzu-Jui Liu 제어로봇시스템학회 2012 제어로봇시스템학회 국제학술대회 논문집 Vol.2012 No.10
Intelligent human-robot interface helps a mobile robot to extract external information and interact with a user. User identification information allows a robot to generate appropriate behaviors and make personalized human-robot interaction (PHRI) more natural and safe. Most of service robots move around in various application settings, and biometric features always change during relative movements. In this work, a fusion recognition system based on face and speaker classifiers is designed to achieve the user recognition in various environments. We propose a method to fuse face and speech recognition results based on the concept of confidence indices. Through the derived confidence indices, the system combines the two classifiers and gives improved recognition results. Experiments on a laboratory home service robot show that person recognition rates in 15 environmental situations all outperform those of using single modality.
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