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RISS 인기검색어
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- 저자 : Qixin Zhu (검색결과 5 건)
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A Robust Speed Controller with Smith Predictor for A PMSM Drive System with Time Delay
Qixin Zhu,Lei Xiong,Hongli Liu 제어·로봇·시스템학회 2017 International Journal of Control, Automation, and Vol.15 No.5
In this paper, using H∞control theory, a robust speed controller with smith predictor is proposed toimprove the speed control performance of the permanent magnet synchronous motor (PMSM) servo system withtime delay. The robust speed controller is designed to improve the robustness of control system. Meanwhile,considering the time delay of the system, the speed controller combined with smith predictor is used to compensatethe impact of system time delay. Firstly, based on theH∞state space (time domain) method, a robust speed controlleris established, which has simple structure and easy to be realized. Secondly, theH∞robust standard design problem istransformed to an optimization solution of the linear matrix inequalities (LMIs). Thirdly, a smith predictor is stalledat the parallel position of the robust speed controller, which is of great help to improve the performance of the servosystem with time delay. Finally, simulation results demonstrate that compared with traditional PI controller, thenovel robust controller has better control performance and the robust controller with smith predictor is effective forthe PMSM drive system with time delay.
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Application of recurrent neural network to mechanical fault diagnosis: a review
Junjun Zhu,Quansheng Jiang,Yehu Shen,Chenhui Qian,Fengyu Xu,Qixin Zhu 대한기계학회 2022 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.36 No.2
With the development of intelligent manufacturing and automation, the precision and complexity of mechanical equipment are increasing, which leads to a higher requirement for fault diagnosis. Fault diagnosis has gradually transformed from traditional diagnosis algorithm to deep feature mining and expression of highly nonlinear, complex and multidimensional systems. At present, the mechanical fault signals of various equipment are mostly time series. In addition, recurrent neural network (RNN) has strong nonlinear feature learning and processing ability of time sequence information, which has achieved promising results in mechanical fault diagnosis and big data processing. Therefore, this study reviews state-of-the-art RNN method in mechanical fault diagnosis and introduces applications from two aspects: RNN and the combined neural networks which include RNN. Then, this paper discusses the challenges and future development of RNN based fault diagnosis.
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Hongli Liu,Qixin Zhu 제어·로봇·시스템학회 2014 International Journal of Control, Automation, and Vol.12 No.6
For linear discrete-time systems, the traditional finite horizon optimal controller is proved to render the closed-loop systems asymptotically stable under some assumptions in literature. In this paper, a new form of finite horizon discrete-time Riccati equation is proposed. It is proved that the new form of fi-nite horizon discrete-time Riccati equation is equivalent to the other three old ones. Based on this new form of finite horizon discrete-time Riccati equation, the finite horizon optimal controller of linear discrete time systems is proved to render the closed-loop system exponentially stable without any assumptions. At the same time, a new form of infinite horizon discrete-time Riccati equation is proposed when the discrete system is controllable or stabilizable. It is proved that the new form of infi-nite horizon discrete-time Riccati equation is equivalent to the other three old ones too. Based on this new form of infinite horizon discrete-time Riccati equation, the infinite horizon optimal controller of linear discrete-time systems is proved to render the closed-loop system exponentially stable when the open-loop system is either controllable or stabilizable. Finally an unstable batch reactor and an unsta-ble inverted pendulum are used to verify the theory results of this paper.
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Nonlinear machine fault detection by semi-supervised Laplacian Eigenmaps
Quansheng Jiang,Qixin Zhu,Bangfu Wang,Lihua Guo 대한기계학회 2017 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.31 No.8
A semi-supervised Laplacian Eigenmaps algorithm for machine fault detection is proposed. The purpose of the algorithm is to efficiently extract the manifold geometric characteristics of nonlinear vibration signal samples, and to determine fault classification of operating equipment so that the accuracy of fault detection can be improved. The data acquisition and pre-processing of the vibration signal is firstly implemented from monitoring equipment, then hybrid domain feature is obtained, and the initial sample set can be built. This is followed by implementing the semi-supervised Laplacian Eigenmaps algorithm so that the sensitive nature characteristics of manifold can be obtained from the device initial sample set. In order to establish the intelligent diagnostic model, the Least square Support vector machine (LS-SVM) is then adopted, which fault diagnosis and decisions can be achieved in the feature space of the low-dimensional manifold. The experiment results of using the IRIS data, gearbox and compressor fault data show the proposed method has more advantage when compared with the PCA and Laplacian Eigenmaps on improving the accuracy of fault detection.
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Jiahao Chen,Yehu Shen,Qixin Zhu,Quansheng Jiang,Ou Xie,Jing Miao 대한기계학회 2022 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.36 No.7
In visual simultaneous localization and mapping (vSLAM) systems, motion blur often leads to insufficient number of matched features, resulting in tracking failure. Existing solutions often tackle this problem by restoring sharp images from blurry ones. However, the computational costs are high, and the restored sharp images are usually distorted. The effect of blurry image sequences to vSLAM system is analyzed, and the relationships between feature matching and motion blur are acquired to deal with the above mentioned problems. A local residual motion blur discrimination network is proposed to detect images with motion blur efficiently. Motion blur recognition results are coupled with a vSLAM system so that the feature extraction process is guided by the results from the local residual motion blur discrimination network. The performance of the vSLAM system can be effectively enhanced when it is applied to sequences with motion blur. Experimental results on the Technische Universität München dataset show that the proposed algorithm increases the average tracking length by about 200 frames compared with the original method on some image sequences with violent motions. This algorithm effectively improves the stability and accuracy of the vSLAM system.
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