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2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS)最新文献

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Identification of an ARMAX model based on a momentum-accelerated multi-error stochastic information gradient algorithm 基于动量加速多误差随机信息梯度算法的ARMAX模型辨识
2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2021-05-14 DOI: 10.1109/DDCLS52934.2021.9455685
Shaoxue Jing
{"title":"Identification of an ARMAX model based on a momentum-accelerated multi-error stochastic information gradient algorithm","authors":"Shaoxue Jing","doi":"10.1109/DDCLS52934.2021.9455685","DOIUrl":"https://doi.org/10.1109/DDCLS52934.2021.9455685","url":null,"abstract":"The ARMAX model is widely used in industrial modeling. However, the traditional stochastic information gradient algorithm for ARMAX identification needs less computation, but its convergence speed is too slow. To accelerate the algorithm, we propose a two-step algorithm based on a gradient acceleration strategy. The first step is to replace the error scalar with the error vector, and the second step is to introduce a momentum related to the gradient. The simulation results show that the proposed algorithm can obtain more accurate estimation and the convergence speed is greatly improved.","PeriodicalId":325897,"journal":{"name":"2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134461137","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
Recent Advances in Iterative Learning Control with Fading Channel 衰落信道迭代学习控制研究进展
2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2021-05-14 DOI: 10.1109/DDCLS52934.2021.9455537
D. Shen, Jiaxi Qian
{"title":"Recent Advances in Iterative Learning Control with Fading Channel","authors":"D. Shen, Jiaxi Qian","doi":"10.1109/DDCLS52934.2021.9455537","DOIUrl":"https://doi.org/10.1109/DDCLS52934.2021.9455537","url":null,"abstract":"With the rapid development of communication technology, network control is widely used. In the process of wireless transmission, a signal may be affected by the attenuation channel. In this paper, we review the recent advances in learning control with fading channels. We first study the case that the fading channel statistics are known, then we turn to the unknown case. We also make some comparisons among these results to illustrate the newly developed techniques. This review paper may assist the readers in understanding the progress of the researches on the design of fading channel algorithms as well as the related issues in multiplicative randomness.","PeriodicalId":325897,"journal":{"name":"2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134473804","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
Reactive Power Optimization for Power System with Distributed Generations Using PSO Hybrid SCA Algorithm 基于PSO混合SCA算法的分布式发电系统无功优化
2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2021-05-14 DOI: 10.1109/DDCLS52934.2021.9455680
Lin Wang, Zhan Shi, Zhanshan Wang
{"title":"Reactive Power Optimization for Power System with Distributed Generations Using PSO Hybrid SCA Algorithm","authors":"Lin Wang, Zhan Shi, Zhanshan Wang","doi":"10.1109/DDCLS52934.2021.9455680","DOIUrl":"https://doi.org/10.1109/DDCLS52934.2021.9455680","url":null,"abstract":"As more and more distributed generations (DGs) are introduced into the power system, the original power flow distribution and voltage quality are changed. Particle swarm optimization (PSO) has been attested to be an effective way to solve reactive power optimization in electrical power system, but it is prone to fall into the local optimization solution and premature convergence. Aiming at these weaknesses, an improved PSO which hybrid sine cosine algorithm (SCA) is proposed. SCA can attract and reject the particles because of the character of sine and cosine functions. This can guarantee the diversity of PSO, and the convergence speed and accuracy are improved effectively. The effectiveness of algorithm is verified by simulations on IEEE 14-bus system including a DG. The results show that the proposed algorithm can obtain a better optimization effect and faster convergence speed.","PeriodicalId":325897,"journal":{"name":"2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133036039","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
Adaptive Tracking Control of Flexible Joint Manipulator with Output Constraints 具有输出约束的柔性关节机械臂自适应跟踪控制
2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2021-05-14 DOI: 10.1109/DDCLS52934.2021.9455568
Yurong Nan, Shi-Gui Zhao, Kexin Ding, Qiang Chen
{"title":"Adaptive Tracking Control of Flexible Joint Manipulator with Output Constraints","authors":"Yurong Nan, Shi-Gui Zhao, Kexin Ding, Qiang Chen","doi":"10.1109/DDCLS52934.2021.9455568","DOIUrl":"https://doi.org/10.1109/DDCLS52934.2021.9455568","url":null,"abstract":"This paper proposes an adaptive tracking control scheme for the uncertain model flexible joint manipulators with output constraints to achieve satisfactory performance. A time-varying arctangent barrier Lyapunov function (TABLF) is first designed to ensure the system output constraints are not violated. By combining backstepping, the negative feedback controller is designed to guarantee the errors remain within the allowable range of the constraints. The unknown dynamic observers are employed to approximate the uncertainties in the system, and a tracking differentiator (TD) is applied to obtain the differentiation of the virtual control laws in the back stepping design. Finally, the comparative simulations are given to illustrate the effectiveness of the proposed scheme.","PeriodicalId":325897,"journal":{"name":"2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123938131","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
Fault Diagnosis of Rod Pump Oil Well Based on Support Vector Machine Using Preprocessed Indicator Diagram 基于预处理指示图的支持向量机有杆泵油井故障诊断
2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2021-05-14 DOI: 10.1109/DDCLS52934.2021.9455702
Jinze Liu, Jian Feng, Qiong Xiao, Shaoning Liu, Feiran Yang, Senxiang Lu
{"title":"Fault Diagnosis of Rod Pump Oil Well Based on Support Vector Machine Using Preprocessed Indicator Diagram","authors":"Jinze Liu, Jian Feng, Qiong Xiao, Shaoning Liu, Feiran Yang, Senxiang Lu","doi":"10.1109/DDCLS52934.2021.9455702","DOIUrl":"https://doi.org/10.1109/DDCLS52934.2021.9455702","url":null,"abstract":"With the continuous development of the petroleum industry, rod pumping has been vigorously developed and widely used in the petroleum industry, so the fault diagnosis of rod pumping wells has become very important. Today, most of the fault diagnosis for pumping wells is based on analyzing the indicator diagram. The indicator diagram can effectively reflect the working status of the rod pump pumping well. By observing the indicator diagram, various failures of the pumping well can be judged, and corresponding measures can be taken to solve the relative failure. This is of great significance to ensure the safe, stable and efficient production of pump devices. This paper takes indicator diagram as the research object, and uses support vector machine (SVM) to identify and classify indicator diagrams to diagnose the fault types of pumping wells. A series of preprocessing is adopted for the indicator diagram, and the improved Fourier descriptor is used for feature extraction to establish a sample database of indicator diagrams. The experimental results show that this indeed improves the accuracy of SVM learning, increases the fault recognition rate, and provides a guarantee for the safe operation of rod pumping wells.","PeriodicalId":325897,"journal":{"name":"2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128572785","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: 5
An Optimal Path-planning Algorithm for Unmanned Rollers with Constraints on Roller Attitude 基于姿态约束的无人滚轮路径规划算法
2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2021-05-14 DOI: 10.1109/DDCLS52934.2021.9455681
H. Xie, Kecheng Jiang, K. Song
{"title":"An Optimal Path-planning Algorithm for Unmanned Rollers with Constraints on Roller Attitude","authors":"H. Xie, Kecheng Jiang, K. Song","doi":"10.1109/DDCLS52934.2021.9455681","DOIUrl":"https://doi.org/10.1109/DDCLS52934.2021.9455681","url":null,"abstract":"The unmanned roller (UR) is promising in improving the compaction quality and efficiency simultaneously. When transiting from on work site to another, a well-designed path-planning algorithm is essential for the efficient operation of rollers between two sites. Existing path planning algorithms barely consider the vehicle attitude at the destination and the kinematic constraints, limiting the efficiency in the initial phase of operation in a new work site. Therefore, in this paper, an improved RRT* algorithm is proposed. By combining Dubins curve with RRT* algorithm, the algorithm enforces the roller to maintain an optimal attitude (orientation) when approaching the entrance of the next work site, by manipulating the target path. In addition, a smooth and achievable path is generated, by setting-up the upper bound of the curvature based on kinematic and steering dynamic models then fitting the target path using the B-spline. The proposed algorithm is tested in both simulation and experiments, confirming its effectiveness.","PeriodicalId":325897,"journal":{"name":"2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129064725","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
Adaptive Sliding Mode Trajectory Tracking Control of Automated Guided Vehicles with Sideslip Angle 具有侧滑角的自动制导车辆自适应滑模轨迹跟踪控制
2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2021-05-14 DOI: 10.1109/DDCLS52934.2021.9455642
Jian Gao, Heng Wang, Wei Quan, Qing Li, Xuanzhi Wang
{"title":"Adaptive Sliding Mode Trajectory Tracking Control of Automated Guided Vehicles with Sideslip Angle","authors":"Jian Gao, Heng Wang, Wei Quan, Qing Li, Xuanzhi Wang","doi":"10.1109/DDCLS52934.2021.9455642","DOIUrl":"https://doi.org/10.1109/DDCLS52934.2021.9455642","url":null,"abstract":"This paper studies the problem of trajectory tracking control for automated guided vehicles (AGVs). A novel control framework is proposed to deal with the nonlinear nonholonomic constrained systems with uncertainties. Firstly, an adaptive backstepping control law based on the AGV trajectory tracking model is designed to estimate sideslip angle online, which guarantees the kinematics system stability. Secondly, torque control inputs are designed by sliding mode strategy such that the linear velocity and angular velocity follow desired values accurately. The stability analysis shows that trajectory tracking errors are convergent and bounded. Finally, a simulation example is presented which demonstrates the effectiveness of the proposed approach.","PeriodicalId":325897,"journal":{"name":"2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114302539","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
Yarn-Dyed Shirt cut Pieces Defect Detection Using Attention Vector Quantized-Variational Autoencoder 基于注意力向量量化变分自编码器的色织衬衫剪片缺陷检测
2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2021-05-14 DOI: 10.1109/DDCLS52934.2021.9455583
Hongwei Zhang, Shuting Liu, Zhiqiang Ge, Pengfei Li
{"title":"Yarn-Dyed Shirt cut Pieces Defect Detection Using Attention Vector Quantized-Variational Autoencoder","authors":"Hongwei Zhang, Shuting Liu, Zhiqiang Ge, Pengfei Li","doi":"10.1109/DDCLS52934.2021.9455583","DOIUrl":"https://doi.org/10.1109/DDCLS52934.2021.9455583","url":null,"abstract":"For yarn-dyed shirt cut defects detection problems in production process, this paper proposes a yarn-dyed shirt cut defects detection method based on attention vector-quantized variational autoencoder reconstructed model and residual analysis. To solve the actual problem that the defect sample quantity scarce, defect categories imbalances, high cost and poor generalization ability of artificial design defect features. Firstly, for a certain yarn-dyed shirt cut, salt and pepper noise is artificially added to the defect-free samples to construct a training data set, and then a reconstruction model based on the attention vector quantized variational autoencoder is established and trained. Secondly, a residual map between the original image and the correspondingly reconstructed image is calculated. Finally, the defective area could be detected and located by thresholding and opening operation processing. Experimental results on several yarn-dyed shirt cut pieces data sets show that the proposed method can effectively reconstruct the yarn-dyed shirt cut pieces, detect and locate the defect area of yarn-dyed shirt cut pieces quickly.","PeriodicalId":325897,"journal":{"name":"2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114831790","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
Learning-based Event-triggered Adaptive Optimal Output Regulation of Linear Discrete-time Systems 基于学习的事件触发线性离散系统自适应最优输出调节
2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2021-05-14 DOI: 10.1109/DDCLS52934.2021.9455453
Fuyu Zhao, Weinan Gao, Tengfei Liu, Zhong-Ping Jiang
{"title":"Learning-based Event-triggered Adaptive Optimal Output Regulation of Linear Discrete-time Systems","authors":"Fuyu Zhao, Weinan Gao, Tengfei Liu, Zhong-Ping Jiang","doi":"10.1109/DDCLS52934.2021.9455453","DOIUrl":"https://doi.org/10.1109/DDCLS52934.2021.9455453","url":null,"abstract":"In this paper, a data-driven event-triggered output-feedback control approach is proposed to solve the problem of adaptive optimal output regulation for uncertain discrete-time linear systems when only the output information is available. A crucial strategy is to develop a novel co-design scheme for the event-triggering mechanism and the data-driven optimal controller. Theoretical analysis and an application to a LCL coupled inverter-based distributed generation system demonstrate the effectiveness of the proposed learning-based, event-triggered, adaptive optimal controller design with output-feedback.","PeriodicalId":325897,"journal":{"name":"2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127363396","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
Observer Design Based on Fractional-order Model of Permanent Magnet Synchronous Motor 基于分数阶模型的永磁同步电机观测器设计
2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2021-05-14 DOI: 10.1109/DDCLS52934.2021.9455647
Zhipeng Wang, Wei Yu, Wen Yu, Chenglin Wen
{"title":"Observer Design Based on Fractional-order Model of Permanent Magnet Synchronous Motor","authors":"Zhipeng Wang, Wei Yu, Wen Yu, Chenglin Wen","doi":"10.1109/DDCLS52934.2021.9455647","DOIUrl":"https://doi.org/10.1109/DDCLS52934.2021.9455647","url":null,"abstract":"Permanent magnet synchronous motor has become the mainstream motor of electric vehicle, elevator and other drive systems due to its high torque density, high efficiency and high reliability. The existing motor model-based fault diagnosis method does not consider the fractional characteristics of the motor, then it is difficult to effectively detect the motor's minor faults. The more accurately mathematical model describes the dynamic relationships of the system, the better performance of model-based fault diagnosis. Effective fault diagnosis relies on effective residual generation, and residual generation relies on the design of observer, so designing an effective state observer to generate residuals is the basis for achieving fault diagnosis. In this paper, two full-dimensional state observers are designed based on integer-order and fractional-order models for permanent magnet synchronous motors with fractional-order characteristics, and it is shown through simulation that more effective residual generation can be obtained based on the fractional-order model.","PeriodicalId":325897,"journal":{"name":"2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127541587","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
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