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检索条件"任意字段=9th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2020"

共 506 条记录，以下是211-220 订阅

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Command-Filtered Backstepping controller for DC Microgrid with Hybrid Energy Storage Devices 9

Command-Filtered Backstepping Controller for DC Microgrid wi...

引用

9th ieee data driven control and learning systems conference (ddcls)

作者： Zhang, Wei Xia, Yan Xu, Dezhi Bi, Kaitao Yang, Weilin Jiangnan Univ Sch Internet Things Engn Wuxi 214122 Jiangsu Peoples R China Yangzhou Univ Coll Elect Energy & Power Engn Yangzhou 225127 Jiangsu Peoples R China

ISBN: (纸本)9781728159225

this paper presents a control scheme for command filtering and backstepping for DC microgrid systems with hybrid energy storage devices. the proposed control scheme strictly controls the active power output by each power converter unit (PCU), and stabilizes the DC bus voltage, regardless of load transients. In terms of power distribution, the energy management system performs first-order low-pass filtering according to the required compensation power, where the high-frequency part is borne by the supercapacitor and the low-frequency part is borne by the battery. As to design of the controller, the use of backstepping control easily lead to differential expansion, which makes the design of the controller difficult. the command filter is used to solve this problem. Finally, the simulation is set up in Matlab/Simulink environment. the simulation results show that the designed power distribution strategy and controller have excellent control effect and strong robustness.

关键词： Command-Filtered Backstepping control Hybrid Energy Storage First-Order Low-Pass Filter Power Allocation

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RGBD Object Recognition and Flat Area Analysis Method for Manipulator Grasping 9

RGBD Object Recognition and Flat Area Analysis Method for Ma...

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9th ieee data driven control and learning systems conference (ddcls)

作者： Wang, Kaijun Liu, Shan Zhejiang Univ State Key Lab Ind Control Technol Coll Control Sci & Engn Hangzhou 310027 Peoples R China

ISBN: (纸本)9781728159225

Grasping guided by visual recognition and positioning is a practical requirement of discrete automation. this paper proposes a general method of using RGBD image recognition, which can recognize object with only one RGB template photograph of the target object, calculate 3D coordinates and pose, and guide the 6 DOF manipulator to grasp object. SIFT(Scale Invariant Feature Transform) is used to extract feature points of template image and real-time scene image and complete matching. Matched feature points are used as seed points to segment target objects in depth image. this method is fast and requires less prior knowledge. In order to optimize the grasping, this paper uses the Shape Index method to locate the flat area on the object which is most suitable for the suction cup. this method can make the grasping system automatically adapt to various objects and overcome the problems of overlapping and partial occlusion.

关键词： Depth image Feature Matching Shape Index Manipulator Coordinate and Pose

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A Novel Incremental Gaussian Mixture Regression and Its Application for Time-varying Multimodal Process Quality Prediction 9

A Novel Incremental Gaussian Mixture Regression and Its Appl...

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9th ieee data driven control and learning systems conference (ddcls)

作者： Li, Deyang Song, Zhihuan Zhejiang Univ Coll Control Sci & Engn State Key Lab Ind Control Technol Hangzhou 310027 Peoples R China

ISBN: (纸本)9781728159225

data-driven soft sensor approach has been widely applied on real-time prediction and control of difficult-to-measure quality variables. Among these approaches, the Gaussian mixture regression (GMR) carries the potential of dealing with nonlinear and non-Gaussian industry problems, which has drawn increasing popularity and attentions in recent years. However, the fluctuation of raw materials, change of process environment, aging of instruments and other factors will have an effect on system performances over time. Hence, the lack of adaptive mechanism will make the GMR difficult to suit for time-varying processes and may cause large prediction errors. In order to model time-varying industrial processes and improve the adaptability of the conventional GMR, an adaptive soft sensor based on incremental Gaussian mixture regression (IGMR) is proposed in this paper. the incremental idea is integrated and an adaptive mechanism is added, which endow the proposed IGMR with the capability of adapting to new data in online environment. Compared to the moving window GMR (MWGMR) and the just-in-time learning GMR (JITLGMR), the feasibility and effectiveness of the proposed IGMR are verified both in a numerical simulation and a real-life industrial process experiment.

关键词： soft sensor time-varying process adaptive mechanism incremental Gaussian mixture model

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data-driven Tie-line Scheduling Method 9

Data-Driven Tie-line Scheduling Method

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9th ieee data driven control and learning systems conference (ddcls)

作者： Cai, Zhi Dai, Sai Dai, Yuhan Zhang, Guofang Lu, Yi China Elect Power Res Inst Beijing Key Lab Res & Syst Evaluat Power Dispatch Beijing 100192 Peoples R China State Grid Sichuan Elect Power Supply Co Chengdu 610094 Peoples R China

ISBN: (纸本)9781728159225

Tie-line scheduling in an interconnected power grid is difficult to perform optimization by establishing a unified physical model due to the different scheduling methods in different dispatching centers. It may cause repeated coordination and iteration in multi-level dispatching centers. therefore, a data-driven tie-line scheduling method with self-learning ability is proposed. First, perform cluster pre-processing of historical dispatching data with K-means algorithm;secondly, establish a deep learning model of tie-line scheduling based on long short-term memory (LSTM), and build a mapping model among system load, clean energy output and tie-line schedule through historical data training;After that, the all above process is used as a foundation to do the tie-line scheduling;finally, continuously revise the model by accumulating historical data, so that it has the ability of self-evolution and self-learning. We make a case analysis based on the actual power grid data, and the calculation results show the effectiveness of the proposed method.

关键词： data-driven Tie-line scheduling Deep learning Long short-term memory K-means cluster

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Model Free Adaptive control for the Temperature Adjustment of UGI Coal Gasification Process in Synthetic Ammonia Industry 9

Model Free Adaptive Control for the Temperature Adjustment o...

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9th ieee data driven control and learning systems conference (ddcls)

作者： Liu, Shida Sun, Jiao Ji, Honghai Hou, Zhongsheng Fan, Lingling North China Univ Technol Sch Elect & Control Engn Beijing 100093 Peoples R China Qingdao Univ Sch Automat Qingdao 266071 Peoples R China Beijing Informat Sci & Technol Univ Sch Automat Beijing 100192 Peoples R China

ISBN: (纸本)9781728159225

In this manuscript, a data-driven model free adaptive control (MFAC) method is introduced for a UGI gasifier. During the UGI gasification process, the temperature of crude gas inside the UGI gasifier is very important. However, the accurate multi-input and multi-output mathematical model describing the dynamics of the crude gas temperature cannot be created due to the complexity of the gasification systems. the main feature of MFAC method is that the controller design depends only on the input and the output measurement data of the controlled plant. Specifically, the MFAC controller is designed via a novel dynamic linearization technique with a time varying parameter termed Poseudo-Jacobian Matrix (PGM), which contains the coupling information of each output variable. Further, simulation results show that MFAC has a very reliable tracking ability for the temperature adjustment of the gasification process.

关键词： data-driven control UGI gasifier model free adaptive control temperature control

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On Chaos control of Small-scale Unmanned Helicopter Based Upon HODFC 9

On Chaos Control of Small-scale Unmanned Helicopter Based Up...

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9th ieee data driven control and learning systems conference (ddcls)

作者： Guo, Xitong Qi, Guoyuan Li, Xia Ma, Shengli Tiangong Univ Tianjin Key Lab Adv Technol Elect Engn & Energy Tianjin 300387 Peoples R China Tiangong Univ Sch Mech Engn Tianjin 300387 Peoples R China

ISBN: (纸本)9781728159225

the small-scale unmanned helicopter has the high main rotor speed and the light body, which makes it highly sensitive to disturbances. In the case of improper assembly and wind interference, the fuselage will shake violently, and even produce chaotic angular velocity oscillation behavior. In this paper, the situation of chaotic oscillation of the angular velocity is given. the high order differential feedback controller (HODFC) for the angular velocity of the helicopter is designed. the controller is independent of the mathematical model of the system, with simple structure and mathematical significance for parameter adjustment. Finally, the controller is loaded after the helicopter generates oscillations in chaotic situation. the angular velocity of the helicopter tends to the reference value rapidly after the controller is loaded, indicating that the HODFC constructed can stabilize the chaotic oscillation of the helicopter's angular velocity.

关键词： High order differential feedback controller (HODFC) helicopter angular velocity chaos chaos control helicopter model

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An ESN based Modeling for Roll-to-Roll Printing systems 9

An ESN based Modeling for Roll-to-Roll Printing Systems

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9th ieee data driven control and learning systems conference (ddcls)

作者： Chen, Zhihua Zhang, Tao Zhang, Zheng Guangzhou Univ Inst Comp Sci & Technol Guangzhou 510006 Guangdong Peoples R China Huazhong Univ Sci & Technol Sch Automat Wuhan 430074 Hubei Peoples R China

ISBN: (纸本)9781728159225

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.

关键词： Modeling Roll-to-Roll printing presses Echo state network

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Event-Triggered Predictive Iterative learning control for Nonlinear Networked systems Under DoS Attacks

Event-Triggered Predictive Iterative Learning Control for No...

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ieee International conference on control Science and systems Engineering (CCSSE)

作者： Qiongxia Yu Fengchen Tian Zhihao Fan Shuaishuai Wu Zhongsheng Hou Henan Polytechnic University Jiaozuo China Qingdao University Qingdao China

In this paper, a new attacked data compensation (ADC) algorithm under denial of service (DoS) attacks and event-triggered based data driven predictive iterative learning control (DDPILC) method for a class of nonlinear networked control systems (NCSs) is proposed. A new attacked data compensation algorithm by using historical and forecast data information is proposed to against DoS attacks when the system suffers from DoS attacks. Meanwhile, In order to save network communication resources, an event triggerd mechanism is proposed in this paper. the convergence of the tracking control error is given by a rigorous theoretical analysis. Finally, the effectiveness of the proposed method is further verified by simulation.

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Online Optimal Event-triggered Tracking control with Actuator Saturation via ADP 9

Online Optimal Event-triggered Tracking Control with Actuato...

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9th ieee data driven control and learning systems conference (ddcls)

作者： Liu, Lu Song, Ruizhuo Ren, Yi Zhao, Yong Song, Biao Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China China Commun Construct Intelligence Transportat C Tianjin 300210 Peoples R China Univ Sci & Technol Beijing Beijing Inst Comp Applicat Beijing 100083 Peoples R China

ISBN: (纸本)9781728159225

this paper comes up with an optimal tracking control mechanism with saturation controller for some continuous nonlinear systems based on the event-triggered (ET) mechanism via adaptive dynamic programming (ADP). To begin with, the constrained tracking control problems are able to be transformed into common event-triggered optimal control through designing an appropriate cost function. Next, a single critic network is to estimate the cost function. Moreover, taking the appropriate triggering condition as the prerequisite, the stability proof of the system is proved according to Lyapunov theory. In addition, the simulations verify the excellent performance of ET-ADP algorithm because the optimal control law is only updated at the required times influenced by the devised triggering condition, which can accomplish high efficiency of resources and energy consumption of the system.

关键词： Optimal Tracking control Event-Triggered Adaptive Dynamic Programming

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data-driven Hydrodynamic Modeling for a Flippers-driven Underwater Vehicle-Manipulator System 9

Data-Driven Hydrodynamic Modeling for a Flippers-Driven Unde...

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9th ieee data driven control and learning systems conference (ddcls)

作者： Wang, Yu Bai, Xuejian Cheng, Long Wang, Shuo Tan, Min Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Inst Automat Beijing 100190 Peoples R China CAS Ctr Excellence Brain Sci & Intelligence Techn Shanghai 200031 Peoples R China

ISBN: (纸本)9781728159225

In this study, a data-driven hydrodynamic modeling method for a flippers-driven underwater vehicle-manipulator system (F-UVMS) is proposed. the modeling method combines parameter identification technique and computational fluid dynamics simulation, which makes the acquisition of hydrodynamic coefficients efficient and convenient. the hydrodynamic analysis of the biomimetic flipper propulsor is completed and the relationships between the generated forces and the motion parameters of the biomimetic flipper propulsor are revealed. the wake effects are analyzed to reveal the mechanism of the biomimetic flipper propulsor. Lift and drag coefficients are identified by the hydrodynamic data and the dynamic model of the biomimetic flipper propulsor is derived. the proposed hydrodynamic model is applied to the position control strategy of the F-UVMS. the motion experiments of the F-UVMS are conducted in the sea and the motion patterns of the F-UVMS are validated.

关键词： data driven modeling underwater vehicle-manipulator system underwater biomimetic vehicle hydrodynamic modeling

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