Current Issue : October - December Volume : 2016 Issue Number : 4 Articles : 5 Articles
In the effort for manufacturing companies to meet up with the emerging consumer\ndemands for mass customized products, many are turning to the application of lean in their\nproduct development process, and this is gradually moving from being a competitive advantage\nto a necessity. However, due to lack of clear understanding of the lean performance\nmeasurements, many of these companies are unable to implement and fully integrated the lean\nprinciple into their product development process. Extensive literature shows that only few\nstudies have focus systematically on the lean product development performance (LPDP)\nevaluation. In order to fill this gap, the study therefore proposed a novel hybrid model based on\nFuzzy Reasoning Approach (FRA), and the extension of Fuzzy-AHP and Fuzzy-TOPSIS\nmethods for the assessment of the LPDP. Unlike the existing methods, the model considers the\nimportance weight of each of the decision makers (Experts) since the performance\ncriteria/attributes are required to be rated, and these experts have different level of expertise.\nThe rating is done using a new fuzzy Likert rating scale (membership-scale) which is designed\nsuch that it can address problems resulting from information lost/distortion due to closed-form\nscaling and the ordinal nature of the existing Likert scale....
The trajectory tracking of under actuated nonlinear system with two degrees of freedom is tackled by an adaptive fuzzy hierarchical\nsliding mode controller. The proposed control law solves the problem of coupling using a hierarchical structure of the sliding\nsurfaces and chattering by adopting different reaching laws.The unknown system functions are approximated by fuzzy logic systems\nand free parameters can be updated online by adaptive laws based on Lyapunov theory. Two comparative studies are made in\nthis paper. The first comparison is between three different expressions of reaching laws to compare their abilities to reduce the\nchattering phenomenon.The second comparison is made between the proposed adaptive fuzzy hierarchical sliding mode controller\nand two other control laws which keep the coupling in the under actuated system. The tracking performances of each control law\nare evaluated. Simulation examples including different amplitudes of external disturbances are made....
This paper deals with the stability analysis and fuzzy stabilizing controller design for\na class of Takagiââ?¬â??Sugeno fuzzy singular systems with interval time-varying delay and\nlinear fractional uncertainties. By decomposing the delay interval into two unequal\nsubintervals and seeking a appropriate ÃÂ, a new Lyapunovââ?¬â??Krasovskii functional is\nconstructed to develop the improved delay-dependent stability criteria, which ensures\nthe considered system to be regular, impulse-free and stable. Furthermore, the desired\nfuzzy controller gains are also presented by solving a set of strict linear matrix inequalities.\nCompared with some existing results, the obtained ones give the result with less\nconservatism. Finally, some examples are given to show the improvement and the\neffectiveness of the proposed method....
In order to solve the real-time parameter adjustment problem of the speed sensorless vector\ncontrol system for the induction motor, the paper presents a fuzzy self-adaptive method with\nintelligent gain adjustment. By the means of the sensorless vector control for induction motor, adapt\nModel reference adaptive system (MRAS) to achieve the rotor position estimation to improve the\nspeed and estimated accuracy, and then design the out-loop controller to achieve high response of\nspeed control. So applying the method of the fuzzy self-adaptive parameter adjustment, track and\ncorrect controller parameters in time to achieve high dynamic response. The simulation results\nconfirm the validity and effectiveness of the proposed control strategy....
This paper proposes a new combined model to predict the spindle deformation, which combines the grey models and the ANFIS\n(adaptive neurofuzzy inference system) model.The grey models are used to preprocess the original data, and the ANFIS model is\nused to adjust the combined model. The outputs of the grey models are used as the inputs of theANFISmodel to train the model.\nTo evaluate the performance of the combined model, an experiment is implemented. Three Pt100 thermal resistances are used\nto monitor the spindle temperature and an inductive current sensor is used to obtain the spindle deformation.The experimental\nresults display that the combined model can better predict the spindle deformation compared to BP network, and it can greatly\nimprove the performance of the spindle....
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