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检索条件"任意字段=10th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2021"

共 359 条记录，以下是261-270 订阅

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State-dependent Event Triggered Path Following control of Unmanned Ground Vehicle with Uncertainties 11

State-dependent Event Triggered Path Following Control of Un...

引用

11th ieee data driven control and learning systems conference, ddcls 2022

作者： Zhang, Pengfei Sun, Hong-Tao Chen, Ziran Tan, Cheng College of Engineering Qufu Normal University Rizhao276826 China Intelligent Control Laboratory Rizhao Huilian Zhongchuang Institute of Intelligent Technology Rizhao276826 China School of Mechatronic Engineering and Automation Shanghai University Shanghai200444 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

Event-triggered communication (ETC) is a very important scheme to save more limited network resources, while preserving the desired control performance. However, due to the controlled system become more and more complicated, especially there are exist some uncertain parameters in state. this motivates us to investigate a state-dependent event-triggered communication (SD-ETC) to solve the problem of path following control of unmanned ground vehicle (UGV). then, an event-triggered H∞ controller is designed, which based on state adjustment scheme and combined with the time-delay system modeling method, Lyapunov stability theory. Compared with the existing static ETC, the SD-ETC can dynamically adjust event-triggered threshold based on the real-time state detection of the path following control system. the effectiveness of the proposed SD-ETC is verified by Matlab simulation experiments. © 2022 ieee.

关键词： Time delay

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Time-delay Estimation Based Model-free control with Adaptive Iterative learning Compensator for Parallel Back-support Exoskeleton 10

Time-delay Estimation Based Model-free Control with Adaptive...

引用

10th ieee data driven control and learning systems conference, ddcls 2021

作者： Wang, Kai Wang, Haoping Tian, Yang LaFCAS School of Automation Nanjing University of Science and Technology Nanjing210094 China

ISBN: (纸本)9781665424233

To perform the trajectory tracking control of a 4-SPS/SC parallel structure based back-support exoskeleton for the repetitive rehabilitation, a time-delay estimation based model-free control with adaptive Iterative learning compensator and initial state learning (AILTDE-MFC) is proposed in this paper. Firstly, to assist with many types of waist movements and not limit the natural motion, a 4-SPS/SC parallel structure based back-support exoskeleton is designed in SolidWorks and the virtual prototype is built in MATLAB/Simscape Multibody. Secondly, for the AILTDE-MFC design, an ultra-local model is utilized to reconstruct the system dynamics and the lumped unknown information in the model is obtained by TDE, which can realize model-free trajectory tracking control in time domain. the designed iterative adaptive law is to compensate the estimation error of TDE and the initial state learning is to guarantee the convergence of initial states. the stability of AILTDE-MFC is verified by CEF. Finally, to validate the proposed method, a visualized co-simulation is implemented by a virtual prototype. Compared with PD and TDE-iPD controllers, the proposed algorithm demonstrates the high tracking performance in time domain and the error convergence along iteration axis. © 2021 ieee.

关键词： MATLAB

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Event-Triggered Fault Detection Filter for Positive Semi-Markovian Jump systems 10

Event-Triggered Fault Detection Filter for Positive Semi-Mar...

引用

10th ieee data driven control and learning systems conference, ddcls 2021

作者： Li, Qiang Zhang, Junfeng Zhang, Shitao School of Automation Hangzhou Dianzi University Hangzhou310018 China

ISBN: (纸本)9781665424233

this paper designs an L1 fault detection filter for positive semi-Markovian jump systems with disturbances based on event-triggering scheme. First, an event-triggering strategy is presented in terms of linear inequality and a fault detection filter is proposed. then, an augmented fault detection system is constructed. Using stochastic copositive Lyapunov function and linear programming, the filter matrices are designed for guaranteeing the positivity and stochastic L1 stability of the considered system. One illustrative example is provided to verify the effectiveness of the results in this paper. © 2021 ieee.

关键词： Linear programming

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Multiple object tracking and trajectory recognition based on deep learning 10

Multiple object tracking and trajectory recognition based on...

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10th ieee data driven control and learning systems conference, ddcls 2021

作者： Zhang, Zhen Jiang, Ping School of Electrical Engineering Nantong University Nantong Jiangsu226019 China

ISBN: (纸本)9781665424233

In automobile plastic parts factory, the workers need to check every automotive plastic assembly whether the fastening buckle is missing. this manual method is inefficient. To solve this problem, the space and channel attention mechanism is applied to YOLOv3-tiny network, which uses K-means clustering to find the best suitable size of anchor box. this algorithm has significantly improved detection accuracy in fastening buckles in comparison with original YOLOv3 algorithm, and the speed has increased by 3 times. threshold segmentation and Hu comparison method are adopted to correct the confidence of the detection target with weight, so as to filter out the false target. Finally, the Lucas-Kanade optical flow method and the Delay-precision mechanism are used to realize the tracking and recognition of the fastening buckle of the automobile plastic assembly. In the experiment of fastening buckle video sequence, the MOTA(Multiple-Object Tracking Accuracy) of this algorithm is about 3.9%7.6% higher than other algorithms. In 200 experiments, the recognition accuracy of automotive plastic components and fastening buckles reaches 100%. © 2021 ieee.

关键词： K-means clustering

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Memory-Based PI-Type Sampled-data Consensus control for Nonlinear Multiagent systems with Time-Varying Delays 10

Memory-Based PI-Type Sampled-Data Consensus Control for Nonl...

引用

10th ieee data driven control and learning systems conference, ddcls 2021

作者： Yang, Jin Zhong, Qishui Shi, Kaibo Zhong, Shouming Han, Sheng School of Aeronautics and Astronautics University of Electronic Science and Technology of China Chengdu611731 China Institute of Electronic and Information Engineering of Uestc in Guangdong Dongguan523808 China School of Information Science and Engineering Chengdu University Chengdu610106 China School of Mathematics Sciences University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (纸本)9781665424233

In this paper, the sampled-data consensus problem of nonlinear multiagent systems (MASs) with time-varying delays is investigated. Compared with the widely used sampled-data controller, a proportional integral type (PI-type) protocol utilizing the information of neighbors considering the effects of memory delay is adopted. then, by adequately considering characteristic about the time-varying delays, an improved time-varying quadratic type of Lyapunov-Krasovskii functional (LKF) is developed. Besides, augmented state vectors and two-sided looped-functional approach are adopting to constructed the LKF, some relaxed matrices in the LKF are not necessarily positive definite. Furthermore, some sufficient criteria are derived to ensure the consistency of the MASs. By solving a series of linear matrix inequalities, the desired memory PI-type sampled-data control gain matrices are obtained. Finally, the numerical examples are presented to illustrate the theoretical results. © 2021 ieee.

关键词： Multi agent systems

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Adaptive PID control for time-varying fermentation processes 10

Adaptive PID control for time-varying fermentation processes

引用

10th ieee data driven control and learning systems conference, ddcls 2021

作者： Wang, Zhiwen Chen, Chongcheng Chen, Xiaoling Li, Dagang Zeng, Feihu Fujian University Applied Technical Engineering Center of Practical Chemical Materials Liming Vocational University Quanzhou362000 China School of Light Industry Liming Vocational University Quanzhou362000 China School of New Materials and Shoes Clothing Engineering Liming Vocational University Quanzhou362000 China

ISBN: (纸本)9781665424233

PID controller is a common control regulator for industrial processes and is widely used in chemical processes. However, conventional proportional-integral-derivative controller has been found insufficient for nonlinear and time-varying fermentation processes. In this work, an auto-tuning neuron PID (ANPID) method based on particle swarm optimization is proposed to handle the time-varying fermentation process. Specifically, the PSO method is first adopted to initialize the parameters of PID. then, an ANPID controller is utilized to adaptively manipulate the process. the design advantage of ANPID is that its control parameters can be updated using useful historical information. Consequently, the time-varying behavior can be captured and good control performance can be achieved. Simulation results illustrate that the proposed controller can obtain better performance than traditional PID. © 2021 ieee.

关键词： three term control systems

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On Short Term Photovoltaic Power Prediction Based on ITMBA-BP Algorithm 10

On Short Term Photovoltaic Power Prediction Based on ITMBA-B...

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10th ieee data driven control and learning systems conference, ddcls 2021

作者： Cao, Hanwen Zhao, Yan Ma, Yanjuan Graduate School Shenyang Institute of Engineering Shenyang110136 China New Energy College Shenyang Institute of Engineering Shenyang110136 China Key Laboratory of Regional Multi-energy System Integration and Control of Liaoning Province Shengyang110136 China

ISBN: (纸本)9781665424233

Photovoltaic power generation, with its clean, low-carbon and sustainable characteristics, is playing an increasingly important role in the formulation of national energy strategies. the photovoltaic power generation is affected by many factors. the traditional prediction method is not accurate to solve the high dimensional optimization problem, and it is easy to fall into the local optimal problem. In this paper, a collaborative algorithm based on improved adaptive T-distribution variation bat algorithm and traditional BP neural network (ITMBA-BP) is proposed. the improved bat algorithm is used to enhance the global coverage and the ability to solve high-dimensional optimization problems. the ITMBA algorithm is used to adjust the structure and parameters of the neural network, which enhances the global search capability of the BP neural network. Use real photovoltaic power plant data to predict and analyze the algorithm proposed in this paper, and compare it with the traditional BP algorithm. the simulation results show that ITMBA-BP algorithm has the advantages of high accuracy and small mean square error. It can effectively predict the change of power generation and has strong practical application value. © 2021 ieee.

关键词： Mean square error

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Transfer learning Based Rolling Bearing Fault Diagnosis 10

Transfer Learning Based Rolling Bearing Fault Diagnosis

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10th ieee data driven control and learning systems conference, ddcls 2021

作者： Yang, Zhengni Wang, Xuying Yang, Rui School of Advanced Technology Xi'an Liaotong-Liverpool University Suzhou215123 China Research Institute of Big Data Analytics Xi'an Jiaotong-Liverpool University Suzhou215123 China

ISBN: (纸本)9781665424233

In recent years, transfer learning has been an important method to address the problem that labeled data are rarely in the real world. In many industry scenarios, collected labeled sample signals are usually not in the same data distribution. A major assumption for traditional learning and deep learning based bearing fault diagnosis methods is the training data and testing data must follow the same data distribution. However, this assumption may not hold in reality. To address the different distribution problem, this paper proposed an unsupervised approach for bearing fault diagnosis based on transfer learning. the correlation alignment (CORAL) algorithm is used to align data distribution of domains in the proposed approach, then the proposed approach applies statistical algorithms to extract shallow features and wavelet scattering network to extract deep features. the 1 nearest neighbors (1-NN) classifier is trained with the feature vector matrix of source domain, which is able to classify the unlabeled samples of target domain presenting the effectiveness of the proposed approach. Different experiments were carried out to demonstrate the performance of the proposed approach. the experiment results show that the proposed model is superior to other bearing fault diagnosis methods. © 2021 ieee.

关键词： Fault detection

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Optimal controller design for state estimation of Boolean control networks 10

Optimal controller design for state estimation of Boolean co...

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10th ieee data driven control and learning systems conference, ddcls 2021

作者： Chen, Yantao Yang, Junqi Cui, Lizhi Zhu, Junjie College of Electrical Engineering and Automation Henan Polytechnic University Jiaozuo454000 China

ISBN: (纸本)9781665424233

In this paper, a kind of optimal controller is proposed to estimate the state of Boolean control networks (BCNs). Different from the standard observer, the optimal state estimation is completed by designing the control input instead of directly using it, where the maximum-minimum method is employed such that the state of BCNs can be uniquely estimated in possible short time steps. A set observer is first proposed to estimate the state of BCNs at any time steps. Based on the set observer, an initial output-dependent reconstructible state tree is developed, where an algorithm is provided to generate the nodes of such tree and can be implemented offline. the optimal control sequence for uniquely determining the state of BCNs is derived from the reconstructible state tree by a breadth-first search algorithm, where the output of BCNs is dynamically employed. An example is given to illustrate the applicability and usefulness of the developed methods. © 2021 ieee.

关键词： State estimation

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Evaluation of Smart Energy System Based on Machine learning 10

Evaluation of Smart Energy System Based on Machine Learning

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10th ieee data driven control and learning systems conference, ddcls 2021

作者： Cheng, Xuran Chai, Lin School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering and School of Automation Southeast University Nanjing210096 China

ISBN: (纸本)9781665424233

In the context of the gradual shortage of fossil energy and serious pollution, the voice of energy transformation is increasing year by year. With the development of Internet and artificial intelligence, the smart energy is getting more and more attention, and it is also the key direction of the upgrading of the world energy industry. In this process, in order to better understand its development and effectively guide the future construction and development of smart energy, appropriate and reasonable evaluation is essential. In the past few years, some researchers have put forward some evaluation methods for smart energy systems, and have achieved some good results. However, only traditional evaluation methods are used which are more subjective. therefore, the application of machine learning method in the evaluation of smart energy system is worth trying and studying. Starting from the requirements of energy system and human development, this paper first introduces the emergence and development of smart energy and its evaluation methods. Simultaneously indicating the necessity of evaluation. then the formation of the evaluation index system is briefly introduced. Secondly, machine learning algorithm models such as support vector machines, decision trees, and BP neural networks used are introduced respectively. Finally, experiments are carried out to train the model, and then comparing and analyzing the results. the final results show that the machine learning method plays a good role in the research of smart energy system evaluation, which is worthy of further research. © 2021 ieee.

关键词： Decision trees

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