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

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Cooperative control of Virtual Coupled High-Speed Trains with the Consideration of Ride Comfort: A Distributed Tube-Based Model Predictive control Approach 13

Cooperative Control of Virtual Coupled High-Speed Trains wit...

引用

13th ieee data driven control and learning systems conference, ddcls 2024

作者： Zhu, Lei Zhang, Xijuan Huang, Deqing Xuefang, L. School of Electrical Engineering Southwest Jiaotong University Chengdu610031 China Co. Ltd Chengdu610092 China School of Intelligent Systems Engineering Sun Yat-Sen University Guangzhou510275 China

ISBN: (纸本)9798350361674

In this work, a distributed tube-based model predictive control (TMPC) method is proposed to address the cooperative control of virtual coupled high-speed trains (HSTs), which ensures the ride comfort of passengers. Firstly, a nonlinear MPC scheme is developed based on the train nominal dynamics to address the cooperative operation among multiple HSTs with different initial states. Especially, the constraints related to the passenger comfort, including the acceleration and the acceleration change rate, are transformed into the input and the input increment constraints of the MPC respectively, which ensures the ride comfort of the HSTs. Subsequently, the recursive feasibility and stability of the MPC are proved theoretically. Secondly, an auxiliary feedback controller is devised based on the actual train dynamics of the train to deal with the external disturbances while improving the operational stability. the auxiliary feedback controller, comprising a discrete-time sliding mode control (DSMC) and a discrete-time disturbance observer (DTDO), utilizes the optimal state sequences generated by the nonlinear MPC as a reference for the feedback control. Additionally, the stability of the auxiliary feedback controller is strictly proved. Finally, the effectiveness of the proposed distributed TMPC is verified through simulations. © 2024 ieee.

关键词： Sliding mode control

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data-driven Adaptive Dynamic Programming for Nonlinear systems with State and Input Constraints 10

Data-Driven Adaptive Dynamic Programming for Nonlinear Syste...

引用

10th International conference on control, Decision and Information Technologies (CoDIT)

作者： Wang, Jianfeng Zhang, Ping Wang, Yan Ji, Zhicheng Jiangnan Univ Engn Res Ctr Internet Things Technol Applicat Minist Educ Wuxi Jiangsu Peoples R China Univ Kaiserslautern Landau Inst Automat Control Erwin Schroedinger Str 12 D-67663 Kaiserslautern Germany

ISBN: (纸本)9798350373981;9798350373974

In this paper a novel data-driven adaptive dynamic programming approach for nonlinear systems with state and input constraints is developed. the basic idea is to transform state constraints into input constraints by introducing a barrier index function. then a Q-learning approach is designed to solve the optimal control problem considering only input constraints. After that, a data-driven constrained value iteration approach is developed to obtain the optimal control policy. It is shown that the iterative action-value function converges to the optimal action-value function. Finally, a torsional pendulum system is used to illustrate the proposed approach.

关键词： Dynamic programming

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A Train Cooperative Operation Optimization Method Considering Passenger Comfort based on Reinforcement learning 11

A Train Cooperative Operation Optimization Method Considerin...

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

作者： Wang, Xingguo Wu, Yue Huang, Deqing Zhu, Lei Lu, Zheng He, Yan Institute of System Science and Technology School of Electrical Engineering Southwest Jiaotong University Chengdu611756 China Southwest Jiaotong University Key Laboratory of Advanced Energy Traction Comprehensive Energy Saving Railway Industry Chengdu610031 China School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China School of Design Arts Southwest Jiaotong University Chengdu610031 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

this paper mainly focuses on the high-speed train cooperative operation problem, considering passenger comfort. To solve this problem, a speed curve optimization method based on Reinforcement learning algorithm is proposed. First, according to the train dynamics system, we build the speed curve optimization object. For realizing the train cooperative operation and increasing the passenger comfort, trains distance constraint and jerk rate constraint are fully considered in formulating reward function. And then, agent of Twin Delayed Deep Deterministic Policy Gradient (TD3) is established. Based on TD3 algorithm, two groups of Q-value networks and one group of action action networks have the same structure, different initial values and update rates respectively, which improves the stability and convergence speed of the algorithm. After the training. the Agent can generate a desirable speed curve of train based on constraints of vehicle output and jerk rate under cooperative operation. © 2022 ieee.

关键词： Speed

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Multi-Motor Speed Synchronization control for Prefabricated Vegetable Vortex Cleaning Line Based on MFSMC 13

Multi-Motor Speed Synchronization Control for Prefabricated ...

引用

13th ieee data driven control and learning systems conference, ddcls 2024

作者： Cui, Zhigang Yao, Wenlong Chi, Ronghu Chen, Xianghao The College of Automation and Electronic Engineering Qingdao University of Science and Technology Qingdao266100 China

ISBN: (纸本)9798350361674

A multi-motor speed synchronization control method is proposed for the prefabricated vegetable vortex cleaning line in this paper. the focus of this method is to enhance the system's synchronization performance, disturbance rejection capability, dynamic response, and stability of the motors. this method is based on Model-Free Sliding Mode control (MFSMC) combined with an enhanced deviation coupling control structure. Its objective is to tackle the challenges arising from significant variations in motor speed tracking errors, notable differences in speed and acceleration, and the complex system structure that results in unstable cleaning effects. Initially, Permanent Magnet Synchronous Motors (PMSM) are selected as the controlled objects. An improved sliding mode manifold function is introduced to minimize the rate of change in motor speed tracking errors. Utilizing this, a data-driven model-free control technique is employed to design a model-free adaptive sliding mode controller for the motors. Additionally, the deviation coupling structure is enhanced to mitigate the negative effects caused by notable differences in speed and acceleration, along with the complex system structure on the synchronization performance. Finally, a simulation analysis is performed employing three motors to evaluate the effectiveness of the offered control method. the results obtained demonstrate the superior performance of motors controlled by the proposed control method exhibit enhanced response speed, reduced overshoot, improved disturbance rejection capability, and superior speed synchronization performance. © 2024 ieee.

关键词： Disturbance rejection

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Interval Weight State Update Approach with Deep Reinforcement learning for Dynamic Load Change Tasks in Air Separation Processes 13

Interval Weight State Update Approach with Deep Reinforcemen...

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13th ieee data driven control and learning systems conference, ddcls 2024

作者： Qiu, Ruiyu Yang, Guanghui Xu, Zuhua Shao, Zhijiang College of Control Science and Engineering Zhejiang University State Key Lab of Industrial Control Technology Hangzhou310027 China HuZhou Research Institute of Industrial Control Technology China

ISBN: (纸本)9798350361674

the air separation unit (ASU) is important for producing high-purity oxygen, nitrogen, and argon, and dynamic load change operation helps meet the needs of downstream industries. the ASU has an extremely long delay, and the requirements for this operation are strict, and model predictive control (MPC) is usually used for this task. However, the burden of online MPC computation is a key issue in improving production and economic efficiency. In this paper, a framework based on Reinforcement learning (RL) is proposed to solve the long-delayed task in ASU. the state and reward function are specially designed for this task. First, training increases the sampling time of the original model to create a low resolution environment, which makes training more efficient. As a sacrifice, the control precision is reduced in the original high resolution environment. then, an algorithm called Interval Weighted State Update (IWSU) is proposed to restore the agent and keeps the action accuracy in the original environment. the dynamic load change task works as the case in this paper. the performance of RL is compared with that of MPC, and results show that RL is almost exactly the same as MPC in key flow indicators, and can well meet the remaining content and purity range requirements. IWSU shows good performance compared with directly applying the agent to the original high resolution environment. © 2024 ieee.

关键词： Deep reinforcement learning

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Availability Attacks on Computing systems through Alteration of Environmental control: Smart Malware Approach 19

Availability Attacks on Computing Systems through Alteration...

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10th ACM/ieee International conference on Cyber-Physical systems (ICCPS)

作者： Chung, Keywhan Kalbarczyk, Zbigniew T. Iyer, Ravishankar K. Univ Illinois Urbana IL 61820 USA

ISBN: (纸本)9781450362856

In this paper, we demonstrate the feasibility of smart malware that advances state-of-the-art attacks by (i) indirectly attacking a computing infrastructure through a cyber-physical system (CPS) that manages the environment in which the computing enterprise operates, (ii) disguising its malicious actions as accidental failures, and (iii) self-learning attack strategies from cyber-physical system measurement data. We address all aspects of the malware, including the construction of the self-learning malware and the launch of a failure injection attack. We validate the attacks in a data-driven CPS simulation environment developed as part of this study.

关键词： Cyber Security Cyber Physical systems CPS Malware

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Yarn-dyed Shirt Piece Defect Detection Based on U-shaped Swin Transformer Auto-encoder 11

Yarn-dyed Shirt Piece Defect Detection Based on U-shaped Swi...

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

作者： Zhang, Hongwei Xiong, Wenbo Zhang, Weiwei Lu, Shuai School of Electronic and Information Xi'an Polytechnic University Xi'an710048 China State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou310027 China School of Medical Technology Beijing Institute of Technology Beijing100081 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

Automatic defect detection is an essential and challenging problem in the yarn-dyed weaving production process, this paper proposed a novel U-shaped Swin Transformer auto-encoder reconstructed model for yarn-dyed shirt piece defect detection. this method uses the model of Transformer to extract the global features of the image better and reconstruct it more accurately, which solves the problems of scarce and unbalanced type of defect samples and high cost in actual production. Firstly, for a certain pattern, using defect-free samples adding Gaussian noise to train the reconstruction model. then, the image to be tested is input into the Transformer model to obtain the corresponding output image. Subsequently, the residual image is calculated by subtracting the input image and its corresponding output image. Finally, the defect localization can be achieved through thresholding and morphological operation. the experiment result verifies the effectiveness of the proposed method on various types of yarn-dyed shirt pieces. © 2022 ieee.

关键词： Yarn

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Collaborative Decision-Making Technique for Wireless Communication Means Based on Multi-Agent Reinforcement learning 13

Collaborative Decision-Making Technique for Wireless Communi...

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13th ieee data driven control and learning systems conference, ddcls 2024

作者： Zhang, Kun Hu, Chao Huang, Deqing Shen, Jiachen School of Electrical Engineering Southwest Jiaotong University Chengdu611756 China Southwest Jiaotong University Key Laboratory of Advanced Energy Traction and Comprehensive Energy Saving Railway Industry Chengdu610031 China

ISBN: (纸本)9798350361674

Wireless communication is a pivotal domain within modern information and communication technology. With the proliferation of mobile devices and the Internet of things, wireless communication methods have become ubiquitous across diverse industries. Challenges arise in collaborative decision-making technology that leverages wireless communication, including heightened information variability, increased time sensitivity, and a significant need for manual intervention. this paper introduces, for the first time, the application of multi -agent reinforcement learning technology to develop wireless communication agents. this approach involves distributed training, real-time simulation interaction, and other key technologies rooted in the pertinent wireless communication mechanism. By leveraging a mechanism of iterative trial and error, the optimal collaborative decision-making strategy is derived, significantly enhancing efficiency. Experimental validation demonstrates the effectiveness of the proposed method. By leveraging a mechanism of iterative trial and error, the optimal collaborative decision-making strategy is derived, significantly enhancing efficiency. Experimental validation demonstrates the effectiveness of the proposed method. © 2024 ieee.

关键词： Reinforcement learning

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Deep Metric learning with Impulsive Neural Network 13

Deep Metric Learning with Impulsive Neural Network

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13th ieee data driven control and learning systems conference, ddcls 2024

作者： Chen, Hong-Zhou Hu, Bin Chen, Long Guan, Zhi-Hong School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China School of Future Technology South China University of Technology Guangzhou510641 China School of Electrical and Information Engineering Wuhan Institute of Technology Wuhan430205 China

ISBN: (纸本)9798350361674

Neural networks with siamese and triplet structures can discriminate between samples by explicitly learning the distance measure. But the dominant metric learning model based on deep neural networks (DNN) has issues with low energy efficiency and slow on-chip operating speed. Motivated by this, we put forth novel impulsive neural networks (INN) based deep metric learning approachs. the loss function is proposed based on the Euclidean distance between the membrane potentials of the neurons in the output layer of the sub-networks composed by Leaky integrate-and-fire (LIF) neurons. Furthermore, the back-propagation through time (BPTT) technique can be effectively implemented and utilized for network optimization through the surrogate functions. Experiments on multiple datasets show that the proposed model can learn the difference between images with performance very close to DNN-based models. At the same time, we conducted an intuitive energy consumption analysis of our model. the results show that compared with DNN-based models, the proposed model has higher energy efficiency. Besides, it only needs an extremely short time step T=10 to complete inference process, which makes the low-latency on-chip operation possible. © 2024 ieee.

关键词： Energy Efficiency Image Processing Impulsive Neural Network Metric learning

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Observer-Based Fault Tolerant Formation control of Multi-Agent systems via Adaptive Dynamic Programming 13

Observer-Based Fault Tolerant Formation Control of Multi-Age...

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13th ieee data driven control and learning systems conference, ddcls 2024

作者： Zhang, Yongwei Zhao, Bo Liu, Derong College of Mathematics and Informatics South China Agricultural University Guangzhou510642 China School of Systems Science Beijing Normal University Beijing100875 China School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen518055 China

ISBN: (纸本)9798350361674

this paper develops a fault tolerant formation control scheme for multi-agent systems (MASs) by using adaptive dynamic programming. In MASs, there exist one leader and multiple followers, where the leader is a generated ideal trajectory and the followers catch up with the leader using appropriate designed control inputs based on their own and neighbors' information. To obtain accurate fault information, a fault observer is established for each follower. Subsequently, a discounted cost function, which includes the estimated fault, the formation error, and the control laws, is developed for each following agent. In order to solve the coupled Hamilton-Jacobi-Bellman equation of each follower, a critic-only structure is built to obtain the approximate fault tolerant formation control law. Furthermore, it is proved that the uniform ultimate boundedness of the formation error of each following agent is guaranteed under the developed observer-based fault tolerant formation control law. It means that all following agents are able to maintain a specified formation and catch up with the leader. Finally, a simulation example is employed to validate the effectiveness of the present control approach. © 2024 ieee.

关键词： Cost functions

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