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2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS)最新文献

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An Imbalance Fault Detection Approach based on Differential Concordia Transform for Marine Current Turbine 基于差分协和变换的船用水轮机不平衡故障检测方法
2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2020-11-20 DOI: 10.1109/DDCLS49620.2020.9275073
Tao Xie, Tianzhen Wang
{"title":"An Imbalance Fault Detection Approach based on Differential Concordia Transform for Marine Current Turbine","authors":"Tao Xie, Tianzhen Wang","doi":"10.1109/DDCLS49620.2020.9275073","DOIUrl":"https://doi.org/10.1109/DDCLS49620.2020.9275073","url":null,"abstract":"Marine current turbine (MCT) has gradually entered and contributed to world energy resources. However, MCT imbalance fault often occurs due to the blades are attached by marine biological growth or marine pollutants, and this imbalance fault will disorder the stator current or output power of generator. In this paper, a novel method based on Differential Concordia transform (DCT) for MCT imbalance fault detection is presented. The goal of this method is to provide a sensitive and robust fault indicator to detect the imbalance fault of MCT under wave and turbulence. The proposed method acquires the 3-phases stator current from the generator and using Concordia transform (CT). Then, reconstruct the Concordia transform components (CTC) to gain the Concordia transform modulus (CTM) and calculate differential to remove trend. Finally, the frequency spectral analysis is used to monitor the condition of blade. A 230-W prototype experimental study verified that the proposed method provides an effective fault indicator for MCT imbalance fault.","PeriodicalId":420469,"journal":{"name":"2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126330366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Open-Closed-Loop Iterative Learning Control for Discrete-time Systems with Vector Relative Degree under Iterative Varying Duration 迭代变持续时间下矢量相对离散系统的开闭环迭代学习控制
2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2020-11-20 DOI: 10.1109/DDCLS49620.2020.9275093
Yun‐Shan Wei, Zhi Weng, T. Xiao
{"title":"Open-Closed-Loop Iterative Learning Control for Discrete-time Systems with Vector Relative Degree under Iterative Varying Duration","authors":"Yun‐Shan Wei, Zhi Weng, T. Xiao","doi":"10.1109/DDCLS49620.2020.9275093","DOIUrl":"https://doi.org/10.1109/DDCLS49620.2020.9275093","url":null,"abstract":"An open-closed-loop iterative learning control (ILC) scheme is presented for linear discrete-time multiple-input multiple-output (MIMO) systems with iterative varying duration in this article, where the vector relative degree of MIMO system is considered. To recompense the absent tracking information at former iterations caused by iterative varying duration, the feedback control strategy is adopted in modified tracking error based ILC law. It is proved that the convergent condition depends on the P-type control gain. By selecting the proper feedback control gain, the developed open-closed-loop ILC scheme can expedite the convergent speed. Finally, a numerical example is provided for validation.","PeriodicalId":420469,"journal":{"name":"2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"134 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123492715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Data-Driven Backstepping Control of Chemical Process 数据驱动的化工过程反演控制
2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2020-11-20 DOI: 10.1109/DDCLS49620.2020.9275044
Jiawen Gao, Jingwen Huang
{"title":"Data-Driven Backstepping Control of Chemical Process","authors":"Jiawen Gao, Jingwen Huang","doi":"10.1109/DDCLS49620.2020.9275044","DOIUrl":"https://doi.org/10.1109/DDCLS49620.2020.9275044","url":null,"abstract":"Modeling and control design of complex chemical processes are challenge tasks because of their multi-variable, timedelay and non-linear features. On the other hand, the plant dynamics are hard to characterize precisely on line when facing uncertain disturbance. In the light of this, this paper presents a data-driven backstepping control scheme for the nonlinear chemical process. Compared with other regular chemical process control schemes, the proposed scheme is independent of specific mathematical models, and free of decoupling operation, linearization, or off-line recognition and modeling. By constructing Lyapunov function and feedback control rate based on real-time data, the integral stability is guaranteed. Williams-Otto reactor example is provided to demonstrate the effectiveness and applicability of the scheme.","PeriodicalId":420469,"journal":{"name":"2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123177695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Event-triggered based consensus control for a type of multi-agent systems 一类多智能体系统的基于事件触发的共识控制
2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2020-11-20 DOI: 10.1109/DDCLS49620.2020.9275173
Jiantao Shi
{"title":"Event-triggered based consensus control for a type of multi-agent systems","authors":"Jiantao Shi","doi":"10.1109/DDCLS49620.2020.9275173","DOIUrl":"https://doi.org/10.1109/DDCLS49620.2020.9275173","url":null,"abstract":"In this note, the consensus control problem has been researched for a type of leader-follower multi-agent systems by using the event-triggered strategy. In order to eliminate continuous information transmission between the neighboring agents or nodes, the consensus controller is constructed by using the estimated state information of neighboring agents instead of their real states. The communication instants are determined by the developed event-triggered strategy to minimize the amount of communication between neighboring agents. A type of error convergence analysis based on Lyapunov function has been provided to prove the bounded convergence of the proposed consensus scheme. Finally, a simulation case is given to verify the effectiveness of the given event-based consensus control strategy.","PeriodicalId":420469,"journal":{"name":"2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"150 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123260450","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Power-sum Function Activated Recurrent Neural Network Model for Solving Multi-linear Systems with Nonsingular M-tensor 求解非奇异m张量多线性系统的幂和函数激活递归神经网络模型
2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2020-11-20 DOI: 10.1109/DDCLS49620.2020.9275245
Shuqiao Wang, Xiujuan Du
{"title":"Power-sum Function Activated Recurrent Neural Network Model for Solving Multi-linear Systems with Nonsingular M-tensor","authors":"Shuqiao Wang, Xiujuan Du","doi":"10.1109/DDCLS49620.2020.9275245","DOIUrl":"https://doi.org/10.1109/DDCLS49620.2020.9275245","url":null,"abstract":"Recurrent neural network (RNN), as a branch of artificial intelligence, shows powerful abilities to solve the complicated computational problems. Due to the similarity between solving equations and controlling dynamic systems, RNN-based approaches can also be analysed from the perspectives of control. Multi-linear systems, on the other hand, are a type of tensor equations with considerable complexity due to the special structure of tensors. In this paper, a power-sum function activated RNN model is proposed to find the solutions of the multi-linear systems with nonsingular ${mathcal{M}}$-tensors. It is theoretically proved that the proposed RNN model is stable in the sense of Lyapunov stability theory and converges to the theoretical solution. In addition, computer simulations are provided to substantiate the effectiveness and superiority of the proposed RNN model.","PeriodicalId":420469,"journal":{"name":"2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123545366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Adaptive Output Feedback Control for a Multi-Motor Driving System with Completely Tracking Errors Constraint 具有完全跟踪误差约束的多电机驱动系统的自适应输出反馈控制
2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2020-11-20 DOI: 10.1109/DDCLS49620.2020.9275262
Minlin Wang, Xueming Dong, X. Ren
{"title":"Adaptive Output Feedback Control for a Multi-Motor Driving System with Completely Tracking Errors Constraint","authors":"Minlin Wang, Xueming Dong, X. Ren","doi":"10.1109/DDCLS49620.2020.9275262","DOIUrl":"https://doi.org/10.1109/DDCLS49620.2020.9275262","url":null,"abstract":"This paper proposes an adaptive output feedback controller for the multi-motor driving system (MDS) to achieve the precision motion control with completely tracking errors constraint. By adopting a K-filter observer to estimate the unknown system states, a modified barrier Lyapunov function (MBLF) is integrated into the adaptive output feedback control to make all the tracking errors constrained within the prescribed bounds. Since the MBLF is suitable for both constrained and unconstrained conditions, it expands the application filed of the classical Lyapunov function. Moreover, minimize learning parameter technique is utilized into the adaptive law design, which improves the adaptive learning process greatly. The system stability is proven by Lyapunov theory. The simulations are conducted on a four-motor driving system to illustrate the efficiency of the proposed controller.","PeriodicalId":420469,"journal":{"name":"2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131467785","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Comparison of Three Different Wheeled-hopping Robots 三种不同轮式跳跃机器人的比较
2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2020-11-20 DOI: 10.1109/DDCLS49620.2020.9275271
Lufeng Zhang, X. Ren
{"title":"Comparison of Three Different Wheeled-hopping Robots","authors":"Lufeng Zhang, X. Ren","doi":"10.1109/DDCLS49620.2020.9275271","DOIUrl":"https://doi.org/10.1109/DDCLS49620.2020.9275271","url":null,"abstract":"Hopping robot is playing an important role in the exploration on bumpy road. This paper presents three different wheeled-hopping robots all designed by ourselves. Firstly, the mechanical structure of the three different wheeled-hopping robots is introduced. The corresponding advantages and disadvantages are also discussed in our paper. Then, through the simulated results we compare these three different designs on the hopping performance and the factors affecting the motion. Finally, we’ll conclude and show our thoughts about the future development on the wheeled-hopping robot.","PeriodicalId":420469,"journal":{"name":"2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131843016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Early Fault Feature Extraction of Nuclear Main Pump Based on MEMD-1.5 dimensional Teager Energy Spectrum 基于MEMD-1.5维Teager能谱的核主泵早期故障特征提取
2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2020-11-20 DOI: 10.1109/DDCLS49620.2020.9275256
Shule Li, Jie Ma
{"title":"Early Fault Feature Extraction of Nuclear Main Pump Based on MEMD-1.5 dimensional Teager Energy Spectrum","authors":"Shule Li, Jie Ma","doi":"10.1109/DDCLS49620.2020.9275256","DOIUrl":"https://doi.org/10.1109/DDCLS49620.2020.9275256","url":null,"abstract":"For the weak component in the early failure of the nuclear main pump, it is easy to be masked by strong faults or overwhelmed by strong noise to cause leakage diagnosis, and in actual working condition measurement, multiple sensors are usually used to synchronize the signals. The existing traditional feature analysis methods are only the single-channel vibration signal measured by multi-sensors is processed, and the multi-channel data fusion is not performed at the later stage to achieve the multi-channel synchronization correlation analysis. A multi-dimensional empirical mode decomposition (Multivariate Empirical Mode Decomposition, MEMD)-1.5-dimensional Teager energy spectrum is proposed for the extraction of micro-fault features. Firstly, use the MEMD to adaptively decompose the multi-channel vibration signals on the collected multi-channel fault characteristic signals under the same state to obtain the Intrinsic Mode Functions(IMF) components of each channel, and then calculate the kurtosis value and correlation coefficient of each channel IMF component to select the best IMF component containing the main information of the fault. Finally, the 1.5-dimensional Teager energy spectrum is used to obtain the fault characteristic information of the signal to achieve the extraction of minor fault features. In order to verify the feasibility of the theory, simulation tests are carried out and the method is applied to the early failure of the outer ring of the bearing, and compared with EMD and envelope demodulation, it is verified that this method can effectively deal with early multi-channel failure information of rotating machinery. It has theoretical guidance significance for early diagnosis of small faults of nuclear main pump.","PeriodicalId":420469,"journal":{"name":"2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130608288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
An ESN based Modeling for Roll-to-Roll Printing Systems 基于回声状态网络的卷对卷印刷系统建模
2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2020-11-20 DOI: 10.1109/DDCLS49620.2020.9275061
Zhihua Chen, Tao Zhang, Zheng Zhang
{"title":"An ESN based Modeling for Roll-to-Roll Printing Systems","authors":"Zhihua Chen, Tao Zhang, Zheng Zhang","doi":"10.1109/DDCLS49620.2020.9275061","DOIUrl":"https://doi.org/10.1109/DDCLS49620.2020.9275061","url":null,"abstract":"In this paper, a modeling scheme based on Echo State Networks (ESN) is designed and discussed for modeling in Roll-to-Roll (R2R) systems. R2R system involves transport and process of thin, flexible, continuous materials (called webs). An accuracy model is critical to the research of R2R system, such as model-based control and prediction. Existing mechanism modeling methods currently used in R2R systems require complex derivation and do not provide the accuracy performance for changing operating conditions and material properties. The modeling scheme based on ESN utilizes the nonlinear approximation approach where the optimal output connect weights of the network are calculated based on matching of the actual closed-loop R2R printing system. The model established by the proposed method considers the effect of operating conditions and material properties. Experimental data from an industrial printing system is used to corroborate the accuracy of R2R system model can be raised double by the proposed method which compared with mechanism modeling methods.","PeriodicalId":420469,"journal":{"name":"2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116553576","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
DNN-based Implementation of Data-Driven Iterative Learning Control for Unknown System Dynamics 基于dnn的未知系统动力学数据驱动迭代学习控制实现
2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2020-11-20 DOI: 10.1109/DDCLS49620.2020.9275089
Junkang Li, Yong Fang, Yu Ge, Yuzho Wu
{"title":"DNN-based Implementation of Data-Driven Iterative Learning Control for Unknown System Dynamics","authors":"Junkang Li, Yong Fang, Yu Ge, Yuzho Wu","doi":"10.1109/DDCLS49620.2020.9275089","DOIUrl":"https://doi.org/10.1109/DDCLS49620.2020.9275089","url":null,"abstract":"As the condition of iterative learning control, it is usually necessary to estimate the parameters of the system model to determine whether the system satisfies the global Lipschitz condition and estimate the upper and lower bounds of the rate of change of the system. However, for systems with unknown dynamics, the data-driven iterative learning control based on system input and output cannot be realized fully. In this paper, using the nonlinear mapping and feature extraction ability of deep learning, only input / output data is used to determine whether the uncertain system satisfies the global Lipschitz condition and estimate the upper and lower bounds of the system's rate of change, so as to realize the iterative learning control of the system. The simulation results verify the validity of estimating whether the system satisfies the ILC condition only based on the input / output data of the system.","PeriodicalId":420469,"journal":{"name":"2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"108 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120849360","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
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