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Fully Actuated System Theory and Applications (CFASTA), Conference on

作者： Yuxin Feng Zhiqiang Li Yang Liu Zhaoshui He Hongyi Li School of Automation and Guangdong Provincial Key Laboratory of Intelligent Decision and Cooperative Control Guangdong University of Technology Guangzhou China School of Automation and Electronics Engineering Qingdao University of Science and Technology Qingdao China School of Automation Guangdong University of Technology Guangzhou China Key Laboratory for IoT Intelligent Information Processing and System Integration of Ministry of Education Guangzhou China

ISBN: (数字)9798350373691

ISBN: (纸本)9798350373707

This article addresses an adaptive tracking control problem for uncertain high-order fully actuated (HOFA) systems with unknown parameters and disturbances. Under the framework of backstepping, the unknown parameter is estimated and the external disturbance is handled by using adaptive control and H ∞ technique, respectively. Moreover, the Levant differentiator is employed to reduce the computation burden. Additionally, the boundedness of signals in the closed-loop system is proven using the Lyapunov theory. Finally, the effectiveness of the proposed scheme is validated through the simulation study.

关键词： Backstepping Manipulators Stability analysis Closed loop systems Adaptive control Lyapunov methods

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Research on cloud control platform architecture based on industrial Internet combined with cloud control technology

Research on cloud control platform architecture based on ind...

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Chinese automation Congress (CAC)

作者： Guolong Ye Song Zheng College of Electrical Engineering and Automation Fuzhou University Fuzhou China Key Laboratory of Industrial Automation Control Technology and Information Processing (Fuzhou University) of Fujian Province Fuzhou China

In order to solve the problem of system performance degradation caused by the difficulty of communication between heterogeneous devices and the large scale of devices under the traditional industrial Internet, this paper proposes a cloud control platform architecture based on the industrial Internet combined with cloud control technology, and uses this architecture to build a cloud control platform. The data engine technology is used to parse different protocols, thus solving the problems of mutual communication and data synchronization between heterogeneous devices. In addition, the cloud control technology is used to dynamically adjust the computing power of the platform to avoid the degradation of platform performance due to the increase in the number of devices. Finally, based on the platform design and construction of an unmanned factory for intelligent manufacturing, the experiments show that the cloud control system architecture proposed in this paper is scientific and effective.

关键词： Degradation Performance evaluation Cloud computing Protocols System performance Computer architecture Production facilities

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DDoS Attack Detection for Cloud control System Based on BiLSTM Algorithm

DDoS Attack Detection for Cloud Control System Based on BiLS...

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Chinese automation Congress (CAC)

作者： Shengliang Xu Song Zheng College of Electrical Engineering and Automation Fuzhou University Fuzhou China Key Laboratory of Industrial Automation Control Technology and Information Processing (Fuzhou University) of Fujian Province Fuzhou China

The huge advantages of cloud computing technology and the bottlenecks in the development of traditional network control systems have prompted the birth of cloud control systems to address the shortcomings of traditional network control systems in terms of bandwidth and performance. However, the information security issues faced by cloud control systems are more complex, and distributed denial-of-service (DDoS) attacks are a typical class of attacks that may lead to problems such as latency in cloud control systems and seriously affect the performance of cloud control systems. In this paper, we build a single-capacity water tank cloud control semi-physical simulation system with heterogeneous controllers and propose a DDoS attack detection method for cloud control systems based on bidirectional long short-term memory neural network (BiLSTM), study the impact of DDoS attacks on cloud control systems. The experimental results show that the BiLSTM algorithm can effectively detect the DDoS attack on the cloud control system.

关键词： Computers Cloud computing Networked control systems Neural networks information security Bandwidth control systems

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Resource Allocation and Task Offloading Strategy Base on Hybrid Simulated Annealing-Binary Particle Swarm Optimization in Cloud-Edge Collaborative System

Resource Allocation and Task Offloading Strategy Base on Hyb...

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IEEE Advanced information Management,Communicates,Electronic and automation control Conference (IMCEC)

作者： Xue Chen Song Zheng College of Electrical Engineering and Automation Fuzhou University Fuzhou China Key Laboratory of Industrial Automation Control Technology and Information Processing (Fuzhou University) of Fujian Province Fuzhou China

ISBN: (纸本)9781665479691

Taking the latency, energy consumption and computing resource cost into consideration comprehensively, we construct a single-cell, multi-user cloud-edge collaborative system model and decompose the optimization problem of task offloading and resource allocation aiming at minimizing the total user overhead into three sub-problems: computing resource allocation, uplink power allocation and task offloading. Computing resource and uplink power allocation are optimized by using convex optimization technique and bisection method respectively. To optimize task offloading, we propose a simulated annealing-binary particle swarm optimization algorithm(SA-BPSO). Simulation results show that SA-BPSO can better reduce the total user overhead compared with the other three schemes and ensure user quality of service quality.

关键词： Energy consumption Costs Simulation Collaboration Simulated annealing Resource management Task analysis

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Data-Driven Safe controller Synthesis for Deterministic Systems: A Posteriori Method With Validation Tests

Data-Driven Safe Controller Synthesis for Deterministic Syst...

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IEEE Conference on Decision and control

作者： Yu Chen Chao Shang Xiaolin Huang Xiang Yin Department of Automation Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai China Department of Automation Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China

In this work, we investigate the data-driven safe control synthesis problem for unknown dynamic systems. We first formulate the safety synthesis problem as a robust convex program (RCP) based on notion of control barrier function. To resolve the issue of unknown system dynamic, we follow the existing approach by converting the RCP to a scenario convex program (SCP) by randomly collecting finite samples of system trajectory. However, to improve the sample efficiency to achieve a desired confidence bound, we provide a new posteriori method with validation tests. Specifically, after collecting a set of data for the SCP, we further collect another set of independent validate data as posterior information to test the obtained solution. We derive a new overall confidence bound for the safety of the controller that connects the original sample data, the support constraints, and the validation data. The efficiency of the proposed approach is illustrated by a case study of room temperature control. We show that, compared with existing methods, the proposed approach can significantly reduce the required number of sample data to achieve a desired confidence bound.

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Optimization of Crane Scheduling Time for Steel Plate Outbound Operations Based on Backtracking Algorithm

Optimization of Crane Scheduling Time for Steel Plate Outbou...

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Chinese automation Congress (CAC)

作者： Jiang Xiang Zhezhuang Xu Jinlong Wang Boyu Chen Qingdong Zhang Xiuren Wu Yuxiong Xia College of Electrical Engineering and Automation Fuzhou University Fuzhou China Education Department of Fujian Province Key Laboratory of Industrial Automation Control Technology and Information Processing Fuzhou China Sansteel Minguang Co. Ltd. Sanming China Fujian Huading Intelligent Manufacturing Technology Co. Ltd. Fuzhou China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Steel plate is a highly customized product, and steel plates from the same order may be stored in different stacks. However, during the outbound process, the steel plates of the next order can only be dispatched from the warehouse once all plates from the preceding order have been fully completed. In this case, the outbound scheduling of steel plates is a challenging problem since its efficiency and complexity are affected by the shuffling of steel plates. In order to address the challenge, this paper formulates the steel plate outbound scheduling as a dynamic programming model with the aim of minimizing the crane operation time. To solve the problem, an decision tree-based backtracking search algorithm (DTBSA) is developed, in which the search tree is pruned through lower bound functions and dominance rules, so as to reduce the search range of the solution space. Experimental results prove that the proposed method can obtain optimal scheduling results within a reasonable time range.

关键词： Backtracking Lower bound Cranes Heuristic algorithms Optimal scheduling Search problems Dynamic scheduling Dynamic programming Steel Decision trees

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Scheduling optimization of silicon single crystal production process based on adaptive improved particle swarm optimization

Scheduling optimization of silicon single crystal production...

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2021 China automation Congress, CAC 2021

作者： Kang, Lu Liu, Ding Wu, Yali Ping, Guozheng School of Automation and Information Engineering Xi'an University of Technology National and Local Joint Engineering Research Center of Crystal Growth Equipment and System Integration Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an710048 China School of Automation and Information Engineering Xi'an University of Technology National and Local Joint Engineering Research Center of Crystal Growth Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an710048 China School of Automation and Information Engineering Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an710048 China

ISBN: (纸本)9781665426473

Aiming at the potential safety hazards of silicon single crystal production enterprises in the process of multi-furnace silicon single crystal production, and considering the maximum power load requirements in actual enterprises, a silicon single crystal production process scheduling model with the goal of minimizing the maximum completion time was established. Based on this model, an adaptive improved particle swarm optimization algorithm (Adaptive GPSO) is proposed to solve it. The algorithm retains the individual optimal value and global optimal value of the particle swarm algorithm, and introduces the crossover operation and mutation operation in the genetic algorithm to improve the new individual generation mechanism. At the same time, it performs dynamic iterative calculations on the mutation probability and crossover probability to avoid the algorithm fall into the local optimum, and improve the diversity and convergence of the algorithm. By solving different scales scheduling problems of silicon single crystal production process, the experiments on real-world data shows that the feasibility and effectiveness of the proposed algorithm. © 2021 IEEE

关键词： Genetic algorithms

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Inverse Problem Estimation of Heat Conduction using Parameters Adaptive PID Algorithm

Inverse Problem Estimation of Heat Conduction using Paramete...

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第34届中国控制与决策会议

作者： Huang Weichao Li Jiahao Liu Ding Faculty of automation and Information Engineering Xian University of Technology Shannxi Key Laboratory of Complex System Control and Intelligent Information Processing

A boundary inverse problem estimation method for heat conduction system based on a parameters adaptive PID algorithm is proposed in the *** method can solve the boundary heat flux estimation problem of onedimensional heat conduction model based on the inverse heat conduction *** reduce the error between the temperatures of the measurement points and the calculation values constantly by using feedback control of PID *** parameters of PID algorithm is optimized by Whale optimization *** is enhances the rapidity and stability of the system and solves the problem that PID parameters are difficult to *** experimental results show that,the method that proposed in the paper can realize the inverse estimation of thermal boundary conditions accurately and quickly while ensuring the stability and convergence of the system.

关键词： Inverse heat conduction problem PID algorithm Finite difference method Whale optimization algorithm

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Resilient Average Consensus: A Detection and Compensation Approach

arXiv

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arXiv 2022年

作者： Zheng, Wenzhe He, Zhiyu He, Jianping Zhao, Chengcheng Fang, Chongrong Department of Automation Shanghai Jiao Tong University China Key Laboratory of System Control and Information Processing Ministry of Education of China China State Key Laboratory of Industrial Control Technology Zhejiang University China

We study the problem of resilient average consensus for multi-agent systems with misbehaving nodes. To protect consensus value from being influenced by misbehaving nodes, we address this problem by detecting misbehaviors, mitigating the corresponding adverse impact and achieving the resilient average consensus. In this paper, general types of misbehaviors are considered, including deception attacks, accidental faults and link failures. We characterize the adverse impact of misbehaving nodes in a distributed manner via two-hop communication information and develop a deterministic detection-compensation-based consensus (D-DCC) algorithm with a decaying fault-tolerant error bound. Considering scenarios where information sets are intermittently available due to link failures, a stochastic extension named stochastic detection-compensation-based consensus (S-DCC) algorithm is proposed. We prove that D-DCC and S-DCC allow nodes to asymptotically achieve resilient average consensus exactly and in expectation, respectively. Then, the Wasserstein distance is introduced to analyze the accuracy of S-DCC. Finally, extensive simulations are conducted to verify the effectiveness of the proposed algorithms. © 2022, CC BY.

关键词： Stochastic systems

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Forget less,count better:a domain-incremental self-distillation learning benchmark for lifelong crowd counting

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Frontiers of information technology & Electronic Engineering 2023年第2期24卷 187-202页

作者： Jiaqi GAO Jingqi LI Hongming SHAN Yanyun QU James ZWANG Fei-Yue WANG Junping ZHANG Shanghai Key Laboratory of Intelligent Information Processing School of Computer ScienceFudan UniversityShanghai 200433China Institute of Science and Technology for Brain-inspired Intelligence Fudan UniversityShanghai 200433China Shanghai Center for Brain Science and Brain-inspired Technology Shanghai 201210China School of Information Science and Technology Xiamen UniversityXiamen 361005China College of Information Sciences and Technology the Pennsylvania State UniversityUniversity ParkPA 16802USA State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China

Crowd counting has important applications in public safety and pandemic control.A robust and practical crowd counting system has to be capable of continuously learning with the newly incoming domain data in real-world scenarios instead of fitting one domain ***-the-shelf methods have some drawbacks when handling multiple domains:(1)the models will achieve limited performance(even drop dramatically)among old domains after training images from new domains due to the discrepancies in intrinsic data distributions from various domains,which is called catastrophic forgetting;(2)the well-trained model in a specific domain achieves imperfect performance among other unseen domains because of domain shift;(3)it leads to linearly increasing storage overhead,either mixing all the data for training or simply training dozens of separate models for different domains when new ones are *** overcome these issues,we investigate a new crowd counting task in incremental domain training setting called lifelong crowd *** goal is to alleviate catastrophic forgetting and improve the generalization ability using a single model updated by the incremental ***,we propose a self-distillation learning framework as a benchmark(forget less,count better,or FLCB)for lifelong crowd counting,which helps the model leverage previous meaningful knowledge in a sustainable manner for better crowd counting to mitigate the forgetting when new data arrive.A new quantitative metric,normalized Backward Transfer(nBwT),is developed to evaluate the forgetting degree of the model in the lifelong learning *** experimental results demonstrate the superiority of our proposed benchmark in achieving a low catastrophic forgetting degree and strong generalization ability.

关键词： Crowd counting Knowledge distillation Lifelong learning

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