2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS)最新文献_第8页

A Simple Reference Model Based PID Control for Wafer Scanner Systems 基于简单参考模型的晶圆扫描系统PID控制

2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2019-05-01 DOI: 10.1109/DDCLS.2019.8909073

Qiao Zhu, Jun-Xiong Chen, Fazhi Song, Yang Liu

{"title":"A Simple Reference Model Based PID Control for Wafer Scanner Systems","authors":"Qiao Zhu, Jun-Xiong Chen, Fazhi Song, Yang Liu","doi":"10.1109/DDCLS.2019.8909073","DOIUrl":"https://doi.org/10.1109/DDCLS.2019.8909073","url":null,"abstract":"In this work, we propose a new and simple controller design based on internal model principle (IMP) and existing PID, with the implementation on a wafer stage. PID has been well applied to solve control engineering problems owing to its flexibility to handle various control tasks, and the easiness to tune PID gains for a variety of plants. However, PID may fail to achieve satisfactory tracking performance when dealing with a complex control task that is beyond a simple step reference. IMP suggests that a good control response can only be expected when the internal model of the reference is incorporated into the control loop. For a well-tuned PID, however, it is not that straightforward to incorporate the internal model of the reference into the existing PID. In this work, focusing on a real wafer stage, we explore a new control design approach, reference-model (RM)-based PID, to achieve the internal model and meanwhile retain the well-tuned PID. The new controller design for the wafer stage is achieved through several steps. First, the body plots of the experimental setup of wafer stage are acquired and a second-order approximate model is established. Second, a reference model specific in wafer stage is given and its inherent internal model is analyzed. Then, the feedback controller is constructed by concatenating the internal model and an existing PID that has been well tuned. Next, considering the low-frequency characteristic of the reference, the internal model is further modified with a simple high-pass filter such that more of high-frequency components in the tracking error could be suppressed. Finally, in experiments, more accurate tracking performance of the RM-based PID is illustrated by comparing with two classic PIDs with different bandwidths.","PeriodicalId":6699,"journal":{"name":"2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"33 1","pages":"99-104"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87931786","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

Exponential Stabilizability Analysis for Constrained Switched System 约束切换系统的指数稳定性分析

2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2019-05-01 DOI: 10.1109/DDCLS.2019.8908856

Yunyun Jin, Yang Song, Taicheng Yang, Weiyan Hou, M. Schüller

引用次数: 0

A Robust Adaptive Control with Extended State Observer for a Piezo-actuated Nano-positioner 基于扩展状态观测器的压电驱动纳米定位器鲁棒自适应控制

2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2019-05-01 DOI: 10.1109/DDCLS.2019.8908943

Pengfei Xia, Wei Wei, Zaiwen Liu, Min Zuo

引用次数: 0

Time Delay Estimation Based Model Reference Adaptive Control for Robot Manipulators 基于时滞估计的机械臂模型参考自适应控制

2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2019-05-01 DOI: 10.1109/DDCLS.2019.8909032

Saim Ahmed, Haoping Wang, Yang Tian

引用次数: 1

Parameter Calibration of Microscopic Traffic Simulation Model Based on Floating Car Data 基于浮车数据的微观交通仿真模型参数标定

2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2019-05-01 DOI: 10.1109/DDCLS.2019.8909058

Lingyu Zhang, Dehui Sun, Li Wang, Haibo Zhang

引用次数: 1

SocialRT: A Recommendation Method Based On Social Trust SocialRT:基于社会信任的推荐方法

2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2019-05-01 DOI: 10.1109/DDCLS.2019.8908972

Xing Xing, Zhixin Meng, Hanting Chu, Jianyan Luo, Tiansheng Qu, Zhichun Jia

引用次数: 0

A Visual Attention Computational Model Based on Edge Detection 基于边缘检测的视觉注意计算模型

2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2019-05-01 DOI: 10.1109/DDCLS.2019.8909080

Yuwei Zhang, Ke Deng, Yongcai Pan, Qingzheng Liu

引用次数: 1

An Auxiliary Model Construction Method for System Identification and Its Application to An Indoor Multicopter Platform 系统辨识辅助模型构建方法及其在室内多旋翼平台上的应用

2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2019-05-01 DOI: 10.1109/DDCLS.2019.8909074

Zeqing Ma, Jinrui Ren, Yi-Wei Lin, Q. Quan

引用次数: 0

Multimode Process Monitoring Based on Modified Probabilistic Linear Discriminant Analysis 基于改进概率线性判别分析的多模式过程监控

2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2019-05-01 DOI: 10.1109/DDCLS.2019.8908958

Yi Liu, Jiu-sun Zeng, Lei Xie, Xun Lang, Shihua Luo, H. Su

{"title":"Multimode Process Monitoring Based on Modified Probabilistic Linear Discriminant Analysis","authors":"Yi Liu, Jiu-sun Zeng, Lei Xie, Xun Lang, Shihua Luo, H. Su","doi":"10.1109/DDCLS.2019.8908958","DOIUrl":"https://doi.org/10.1109/DDCLS.2019.8908958","url":null,"abstract":"This paper focus on developing an effective method to monitor the industrial process with multiple operation conditions. By utilizing the technique of probabilistic linear discriminant analysis (PLDA), the between- and within-class latent variables can extract more useful information. The proposed method, the modified PLDA (MPLDA), transforms the centralized samples into a new type of between-class latent variables. The current mode operation condition can be identified by comparing a series of cosine similarities deduced by the original and the new between-class latent variables. The online monitoring procedures are built on the basis of this mode identification. Unlike the conventional $T^{2}$ and $Q$ statistics designed for within-class latent variable, the proposed monitoring statistics take both between- and within-class latent variables into consideration. For the model training, the joint updating expectation-maximization (EM) algorithm is developed. The enhanced performance of the MPLDA based method is illustrated by the application of Tennessee Eastman (TE) process.","PeriodicalId":6699,"journal":{"name":"2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"49 1","pages":"604-609"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85442785","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

Run-to-run Trajectory Prediction of Uneven-length Batch Processes Using DTW-LSTM 基于DTW-LSTM的不均匀批处理运行轨迹预测

2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2019-05-01 DOI: 10.1109/DDCLS.2019.8908850

Feifan Shen, Lingjian Ye, Saite Fan, Zhiqiang Ge, Zhihuan Song

引用次数: 0