Pub Date : 2018-05-01DOI: 10.1109/DDCLS.2018.8515914
Wei Wei, Pengfei Xia, Min Zuo
This paper concentrates on the active disturbance rejection control of a nanopositioner driven by a piezoelectric actuator. Hysteresis reduces the accuracy or even breaks the stability of a nanopositioner. For the purpose of improving the closed-loop performance of a nanopositioning stage, active disturbance rejection control (ADRC) is utilized. Fourth order extended state observer is designed to get system output, first and second derivative of system output, and the total disturbance. System performance can be guaranteed by compensating total disturbance via control law. Based on an identified model of a nanopositioning stage, simulations have been performed. Numerical results have been presented to confirm the ability of ADRC in high-precision positioning.
{"title":"On Disturbance Rejection of Piezo-actuated Nanopositioner","authors":"Wei Wei, Pengfei Xia, Min Zuo","doi":"10.1109/DDCLS.2018.8515914","DOIUrl":"https://doi.org/10.1109/DDCLS.2018.8515914","url":null,"abstract":"This paper concentrates on the active disturbance rejection control of a nanopositioner driven by a piezoelectric actuator. Hysteresis reduces the accuracy or even breaks the stability of a nanopositioner. For the purpose of improving the closed-loop performance of a nanopositioning stage, active disturbance rejection control (ADRC) is utilized. Fourth order extended state observer is designed to get system output, first and second derivative of system output, and the total disturbance. System performance can be guaranteed by compensating total disturbance via control law. Based on an identified model of a nanopositioning stage, simulations have been performed. Numerical results have been presented to confirm the ability of ADRC in high-precision positioning.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"56 1","pages":"688-692"},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86702371","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}
In order to improve the reliability of moving objects detection and tracking, this paper presents a method for moving object real-time detection and tracking based on Vibe and Gaussian mixture model (GMM). This method uses the "Virtual" background model that is trained by video sequence instead of the first frame image for background modeling. And then the foreground object is extracted based on the pixel classification. Finally, according to the morphological method, the clearer moving targets are conducted to realize the real-time detection and tracking. The experimental results show that, in comparison with the current mainstream background subtraction techniques, our approach effectively works on a wide range of complex scenarios, with faster detection speed and more reliable detection results.
{"title":"Moving Object Real-time Detection and Tracking Method Based on Improved Gaussian Mixture Model","authors":"Shanliang Zhu, Xin Gao, Haoyu Wang, Guangwei Xu, Qiuling Xie, Shuguo Yang","doi":"10.1109/DDCLS.2018.8515905","DOIUrl":"https://doi.org/10.1109/DDCLS.2018.8515905","url":null,"abstract":"In order to improve the reliability of moving objects detection and tracking, this paper presents a method for moving object real-time detection and tracking based on Vibe and Gaussian mixture model (GMM). This method uses the \"Virtual\" background model that is trained by video sequence instead of the first frame image for background modeling. And then the foreground object is extracted based on the pixel classification. Finally, according to the morphological method, the clearer moving targets are conducted to realize the real-time detection and tracking. The experimental results show that, in comparison with the current mainstream background subtraction techniques, our approach effectively works on a wide range of complex scenarios, with faster detection speed and more reliable detection results.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"24 1","pages":"654-658"},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86376903","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}
Pub Date : 2018-05-01DOI: 10.1109/DDCLS.2018.8516075
Jinbo Huang, Yiming Wu, Liping Chang, Xiongxiong He, Sheng Li
In this paper, we investigate the resilient consensus problem for the second-order multi-agent system communicating via switching networks. The term resilient means that the control protocols should consider the presence of attacks by some malicious agents. Assuming that the maximum number of malicious agents in the neighborhood of each agent is bounded and known, we propose a local neighbors’ information-based on distributed consensus protocol suitable for time-varying topologies to deal with the malicious attacks. It is shown that if the union of communication graphs over a bounded period satisfies certain network robustness property, the states of all normal agents can be guaranteed to reach an agreement resiliently. Numerical simulations are provided to illustrate the effectiveness of the theoretical results.
{"title":"Resilient Consensus with Switching Networks and Double-Integrator Agents","authors":"Jinbo Huang, Yiming Wu, Liping Chang, Xiongxiong He, Sheng Li","doi":"10.1109/DDCLS.2018.8516075","DOIUrl":"https://doi.org/10.1109/DDCLS.2018.8516075","url":null,"abstract":"In this paper, we investigate the resilient consensus problem for the second-order multi-agent system communicating via switching networks. The term resilient means that the control protocols should consider the presence of attacks by some malicious agents. Assuming that the maximum number of malicious agents in the neighborhood of each agent is bounded and known, we propose a local neighbors’ information-based on distributed consensus protocol suitable for time-varying topologies to deal with the malicious attacks. It is shown that if the union of communication graphs over a bounded period satisfies certain network robustness property, the states of all normal agents can be guaranteed to reach an agreement resiliently. Numerical simulations are provided to illustrate the effectiveness of the theoretical results.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"61 1","pages":"802-807"},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83693687","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}
Pub Date : 2018-05-01DOI: 10.1109/DDCLS.2018.8516079
Weiming Zhang, Dezhi Xu, X. Lou, Wenxu Yan, Weilin Yang
A novel adaptive control strategy based on input/output (I/O) data is proposed in this paper to solve the problem of power management of battery energy storage system (BESS). In the proposed control strategy, a time-varying parameter named pseudo-partial derivative (PPD) parameter utilized in dynamic linearization is estimated by an adaptive observer. Besides, the input saturation problem is considered and a compensation signal is added to consummate the anti-windup control algorithm. Finally, simulation results are presented to validate the effectiveness and performance of the proposed control strategy.
{"title":"Power Management of Battery Energy Storage System Using Model Free Adaptive Control","authors":"Weiming Zhang, Dezhi Xu, X. Lou, Wenxu Yan, Weilin Yang","doi":"10.1109/DDCLS.2018.8516079","DOIUrl":"https://doi.org/10.1109/DDCLS.2018.8516079","url":null,"abstract":"A novel adaptive control strategy based on input/output (I/O) data is proposed in this paper to solve the problem of power management of battery energy storage system (BESS). In the proposed control strategy, a time-varying parameter named pseudo-partial derivative (PPD) parameter utilized in dynamic linearization is estimated by an adaptive observer. Besides, the input saturation problem is considered and a compensation signal is added to consummate the anti-windup control algorithm. Finally, simulation results are presented to validate the effectiveness and performance of the proposed control strategy.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"27 1","pages":"798-801"},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88523392","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}
Pub Date : 2018-05-01DOI: 10.1109/DDCLS.2018.8515957
F. Zhu, Jiancheng Zhang, Fengning Wang, S. Guo
The problem of unknown input and measurement noise estimations for a class of switched Lipschitz nonlinear systems is investigated in this paper. An augmented state is used to construct a new descriptor system to deal with the measurement noise in output vector, and then the descriptor system does not contain measurement noise in form. The main results are for the constructed descriptor system, a new Lyapunov-type precondition is developed in detail to present a sliding mode observer, which can estimate both the original system states and unknown inputs simultaneously. And the sliding model term is introduced to deal with the system nonlinearity and the unknown input. Finally, a simulation example of an electric circuit system is considered to show the effectiveness of the proposed methods.
{"title":"Unknown Input and Measurement Noise Estimations for Switched Nonlinear Systems","authors":"F. Zhu, Jiancheng Zhang, Fengning Wang, S. Guo","doi":"10.1109/DDCLS.2018.8515957","DOIUrl":"https://doi.org/10.1109/DDCLS.2018.8515957","url":null,"abstract":"The problem of unknown input and measurement noise estimations for a class of switched Lipschitz nonlinear systems is investigated in this paper. An augmented state is used to construct a new descriptor system to deal with the measurement noise in output vector, and then the descriptor system does not contain measurement noise in form. The main results are for the constructed descriptor system, a new Lyapunov-type precondition is developed in detail to present a sliding mode observer, which can estimate both the original system states and unknown inputs simultaneously. And the sliding model term is introduced to deal with the system nonlinearity and the unknown input. Finally, a simulation example of an electric circuit system is considered to show the effectiveness of the proposed methods.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"32 1","pages":"408-413"},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77554505","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}
The paper solves the problem of decentralized robust adaptive output-feedback control for a class of large-scale stochastic time-delay nonlinear systems. Simulation results show that the closed-loop system is globally stable in probability and the output signals can converge to a small neighborhood of the origin in probability under some milder conditions.
{"title":"Decentralized Robust Adaptive Output-Feedback Control for A Class of Large-Scale Stochastic Time-Delay Nonlinear Systems","authors":"Qian Wang, Qiangde Wang, Zhengqiang Zhang, Chunling Wei","doi":"10.1109/DDCLS.2018.8516110","DOIUrl":"https://doi.org/10.1109/DDCLS.2018.8516110","url":null,"abstract":"The paper solves the problem of decentralized robust adaptive output-feedback control for a class of large-scale stochastic time-delay nonlinear systems. Simulation results show that the closed-loop system is globally stable in probability and the output signals can converge to a small neighborhood of the origin in probability under some milder conditions.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"1 1","pages":"160-165"},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72923096","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}
Pub Date : 2018-05-01DOI: 10.1109/DDCLS.2018.8515935
Hui Yu, Z. Shuhua, Chi Rong-hu
In this work, a novel data-driven optimal ILC with an extended state observer for a class of nonlinear non-affine discrete-time repetitive system has been proposed. The main feature of the approach is that the controller design depends merely on the I/O data, and an ESO has been introduced for the estimation of disturbance and uncertainty. The final simulation results verify the effectiveness of the proposed method.
{"title":"A Data-driven Optimal ILC Method Incorporated with Extended State Observer for Nonlinear Discrete-time Repetitive Systems","authors":"Hui Yu, Z. Shuhua, Chi Rong-hu","doi":"10.1109/DDCLS.2018.8515935","DOIUrl":"https://doi.org/10.1109/DDCLS.2018.8515935","url":null,"abstract":"In this work, a novel data-driven optimal ILC with an extended state observer for a class of nonlinear non-affine discrete-time repetitive system has been proposed. The main feature of the approach is that the controller design depends merely on the I/O data, and an ESO has been introduced for the estimation of disturbance and uncertainty. The final simulation results verify the effectiveness of the proposed method.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"516 1","pages":"77-80"},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77126248","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}
Pub Date : 2018-05-01DOI: 10.1109/DDCLS.2018.8516035
M. Chiang, An-Sheng Liu, L. Fu
This paper considers the optimal consensus of multi-agent systems using reinforcement learning control. The system is nonlinear and the number of agents can be large. The control objective is to design the controllers for each agent such that all the agents will be consensus to the leader agent. We use the Actor-Critic Network and the Deterministic Policy Gradient method to realize the controller. The policy iteration algorithm is discussed and many simulations are provided to validate the result.
{"title":"Reinforcement Learning Control for Consensus of the Leader-Follower Multi-Agent Systems","authors":"M. Chiang, An-Sheng Liu, L. Fu","doi":"10.1109/DDCLS.2018.8516035","DOIUrl":"https://doi.org/10.1109/DDCLS.2018.8516035","url":null,"abstract":"This paper considers the optimal consensus of multi-agent systems using reinforcement learning control. The system is nonlinear and the number of agents can be large. The control objective is to design the controllers for each agent such that all the agents will be consensus to the leader agent. We use the Actor-Critic Network and the Deterministic Policy Gradient method to realize the controller. The policy iteration algorithm is discussed and many simulations are provided to validate the result.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"20 1","pages":"1152-1157"},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76619043","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}
Pub Date : 2018-05-01DOI: 10.1109/DDCLS.2018.8515909
Binquan Zhou, Zhuoqi Wang, Yueyang Zhai, H. Yuan
We propose a couple of data-driven analysis methods for the state controllability and state observability of a class of discrete linear time-invariant (LTI) systems with delays, which have unknown parameter matrices. To analyze the state controlla-bility and the state observability, these data-driven methods first transform the system model into an augmented state-space model, and then use the state/output data that were previously measured, to directly build the controllability/observability matrices of this augmented model. Our methods have two main advantages over the traditional model-based characteristics analysis approaches. First, the unknown parameter matrices are not necessary to be identified for verifying the state controllability/observability of the system, but these characteristics can be directly verified according to the measured data, thus our methods have less workload. Second, their computational complexity is lower for the construction of the state controllability/observability matrices.
{"title":"Data-Driven Analysis Methods for Controllability and Observability of A Class of Discrete LTI Systems with Delays","authors":"Binquan Zhou, Zhuoqi Wang, Yueyang Zhai, H. Yuan","doi":"10.1109/DDCLS.2018.8515909","DOIUrl":"https://doi.org/10.1109/DDCLS.2018.8515909","url":null,"abstract":"We propose a couple of data-driven analysis methods for the state controllability and state observability of a class of discrete linear time-invariant (LTI) systems with delays, which have unknown parameter matrices. To analyze the state controlla-bility and the state observability, these data-driven methods first transform the system model into an augmented state-space model, and then use the state/output data that were previously measured, to directly build the controllability/observability matrices of this augmented model. Our methods have two main advantages over the traditional model-based characteristics analysis approaches. First, the unknown parameter matrices are not necessary to be identified for verifying the state controllability/observability of the system, but these characteristics can be directly verified according to the measured data, thus our methods have less workload. Second, their computational complexity is lower for the construction of the state controllability/observability matrices.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"147 1","pages":"380-384"},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76865873","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}
In magnetic flux leakage (MFL) detection, transient fault appears unavoidably on individual sensor when we collect magnetic flux leakage signals, which makes MFL data insufficient. Data mending for insufficient data concerns the accuracy of the defects inversion. A precise data mending method based on K Nearest Neighbor-Support Vector Regression (KNN-SVR) is introduced, which effectively reduces the training cost of SVR and greatly improves the accuracy of the algorithm. The method is tested by experiment data obtained. The results demonstrate that the proposed method can improve the accuracy rate of data mending of insufficient data with an acceptable time cost.
{"title":"A KNN-SVR Data Mending Method for Insufficient Data of Magnetic Flux Leakage Detection","authors":"Xinbo Zhang, Jian Feng, Zhiqiang Yao, Jinhai Liu, Huaguang Zhang","doi":"10.1109/DDCLS.2018.8516108","DOIUrl":"https://doi.org/10.1109/DDCLS.2018.8516108","url":null,"abstract":"In magnetic flux leakage (MFL) detection, transient fault appears unavoidably on individual sensor when we collect magnetic flux leakage signals, which makes MFL data insufficient. Data mending for insufficient data concerns the accuracy of the defects inversion. A precise data mending method based on K Nearest Neighbor-Support Vector Regression (KNN-SVR) is introduced, which effectively reduces the training cost of SVR and greatly improves the accuracy of the algorithm. The method is tested by experiment data obtained. The results demonstrate that the proposed method can improve the accuracy rate of data mending of insufficient data with an acceptable time cost.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"5 1","pages":"442-445"},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82470660","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}
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