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- 저자 : Qiao Lyu (검색결과 2 건)
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Implementation of design of experiment for structural optimization of annular jet pumps
Qiao Lyu,Zhihuai Xiao,Qinlong Zeng,Longzhou Xiao,Xinping LONG 대한기계학회 2016 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.30 No.2
The DOE (Design of experiments) method together with CFD (Computational fluid dynamics) was applied to optimize structural combination of the Annular jet pumps (AJPs). An AJP with an area ratio of 1.75 was selected as the simulation prototype and the numerical results were validated by experiments. According to the DOE method, four impact factors were selected for simulation. The results showed that two-factor’s reciprocal action is more applicable than that of single factor on the AJP’s performance, especially the two groups ,α*l t and l t *β, respectively. The structure of AJPs with different area ratios ranging from 1.5 to 40 were optimized by the DOE method. The corresponding optimum structural parameters and performance curves were plotted to acquire the performance envelope lines, the efficiency envelope line and the peak efficiency lines, which are useful for AJP’s structure design and obtaining its operation condition.
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Adaptive Back-stepping Neural Control for an Embedded and Tiltable Vtail Morphing Aircraft
Fuxiang Qiao,Jingping Shi,Xiaobo Qu,Yongxi Lyu 제어·로봇·시스템학회 2022 International Journal of Control, Automation, and Vol.20 No.2
This paper presents an adaptive back-stepping neural control (ABNC) method for the coupled nonlinear model of a novel type of embedded surface morphing aircraft. Based on a large number of aerodynamic data for different V-tail configurations, the longitudinal and lateral aerodynamic characteristics of the aircraft are analyzed, and a nonlinear model with six degrees-of-freedom is established. To avoid the problem of “differential explosion,” the controller is designed using the traditional back-stepping control (TBC) method with a first-order filter. Radial basis function neural networks are introduced to estimate the uncertainty and external disturbance of the model, and a controller based on the ABNC method is designed. The stability of the proposed ABNC controller is proved using Lyapunov theory, and it is shown that the tracking error of the closed-loop system converges uniformly within specified bounds. Simulation results show that the ABNC controller works well, with better tracking performance and robustness than the TBC controller.
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