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43rd Chinese control Conference, CCC 2024

作者： Zhang, Yibo Li, Dewei Zhou, Yuanqiang Gao, Furong Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Shanghai201100 China Tongji University College of Electronic and Information Engineering Shanghai201804 China Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong The Guangzhou Hkust Fok Ying Tung Research Institute Department of Chemical and Biological Engineering Guangzhou511458 China

ISBN: (纸本)9789887581581

Quality prediction of plastic products has always been an important research topic in the injection molding production process. However, existing quality prediction methods struggle to handle both high-frequency data and statistical data generated during the injection molding production process. This paper introduces a novel quality prediction model for injection molding production, utilizing a combination of Convolutional Neural Networks (CNN) and Parallel-Cascade Long Short-Term Memory (PCLSTM) networks. The PCLSTM network is designed to handle the complexities of high-frequency data in injection molding, overcoming the limitations of traditional LSTM methods in processing long sequence data. By segmenting data into stages and utilizing a cascade mechanism, the PCLSTM architecture efficiently captures important temporal dynamics. Additionally, the CNN network is designed to extract statistical data features. The quality prediction results are obtained by fusing two types of data features and then passing them through a fully connected network. Experimental results on real production datasets show that the CNN-PCLSTM model outperforms existing models in prediction accuracy and training convergence speed. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Convolutional neural networks

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Specificity-preserving RGB-D saliency detection

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Computational Visual Media 2023年第2期9卷 297-317页

作者： Tao Zhou Deng-Ping Fan Geng Chen Yi Zhou Huazhu Fu School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing 210094China Key Laboratory of System Control and Information Processing Ministry of EducationShanghaiChina Computer Vision Lab ETH Z¨urichZ¨urichSwitzerland School of Computer Science and Engineering Northwestern Polytechnical UniversityXi’anChina School of Computer Science and Engineering Southeast UniversityNanjingChina Inception Institute of Artificial Intelligence Abu DhabiUnited Arab Emirates

Salient object detection(SOD)in RGB and depth images has attracted increasing research *** RGB-D SOD models usually adopt fusion strategies to learn a shared representation from RGB and depth modalities,while few methods explicitly consider how to preserve modality-specific *** this study,we propose a novel framework,the specificity-preserving network(SPNet),which improves SOD performance by exploring both the shared information and modality-specific ***,we use two modality-specific networks and a shared learning network to generate individual and shared saliency prediction *** effectively fuse cross-modal features in the shared learning network,we propose a cross-enhanced integration module(CIM)and propagate the fused feature to the next layer to integrate cross-level ***,to capture rich complementary multi-modal information to boost SOD performance,we use a multi-modal feature aggregation(MFA)module to integrate the modalityspecific features from each individual decoder into the shared *** using skip connections between encoder and decoder layers,hierarchical features can be fully *** experiments demonstrate that our SPNet outperforms cutting-edge approaches on six popular RGB-D SOD and three camouflaged object detection *** project is publicly available at https://***/taozh2017/SPNet.

关键词： salient object detection(SOD) RGB-D cross-enhanced integration module(CIM) multi-modal feature aggregation(MFA)

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Fault estimation for singular fractional-order systems based on H∞ unknown input observers

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Ocean Engineering 2025年 335卷

作者： Zhang, Qing-Hao Lu, Jun-Guo Zhu, Zhen Cui, Ren-Jie Department of Automation Ministry of Education of China and Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University No.800 Dong Chuan Road Min Hang Shanghai200240 China

This paper investigates the fault estimation problem for singular fractional-order systems. An H∞ unknown input observer is designed to meet both admissibility and performance conditions. By using the bounded real lemma, the existence condition of the observer gain matrices with low conservativeness is derived. The methods for designing the full-order and reduced-order observers for the system with order 0 © 2025 Elsevier Ltd

关键词： Convergence of numerical methods

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Distributionally Robust control Synthesis for Stochastic systems with Safety and Reach-Avoid Specifications

arXiv

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arXiv 2025年

作者： Chen, Yu Li, Yuda Li, Shaoyuan Yin, Xiang Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

We investigate the problem of synthesizing distributionally robust control policies for stochastic systems under safety and reach-avoid specifications. Using a game-theoretical framework, we consider the setting where the probability distribution of the disturbance at each time step is selected from an ambiguity set defined by the Wasserstein distance. The goal is to synthesize a distributionally robust control policy that ensures the satisfaction probability exceeds a specified threshold under any distribution within the ambiguity set. First, for both safety and reach-avoid specifications, we establish the existence of optimal policies by leveraging the dynamic programming principles. Then we demonstrate how the associated optimization problem can be efficiently solved using the dual representation of Wasserstein distributionally robust optimization. Furthermore, for safety specifications in particular, we introduce a novel concept of distributionally robust control barrier certificates and show how these enable the efficient synthesis of controllers through sum-of-squares programming techniques. Finally, our experimental results reveal that incorporating distributional robustness during the synthesis phase significantly improves the satisfaction probability during online execution, even with limited statistical knowledge of the disturbance distribution. Copyright © 2025, The Authors. All rights reserved.

关键词： Stochastic systems

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State-of-Charge Balancing control for Battery Energy Storage systems of Near-Space Vehicles with an Electric-Thermal-Aging Coupling Model

State-of-Charge Balancing Control for Battery Energy Storage...

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Chinese control Conference (CCC)

作者： Zhiqiang Zuo Yunhao Du Yijing Wang Zhicheng Zhang Peng Li Tianjin Key Laboratory of Intelligent Unmanned Swarm Technology and System School of Electrical and Information Engineering Tianjin University Tianjin P. R. China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai China

State-of-charge (SoC) balancing is crucial for improving the efficiency and lifetime of the battery energy storage system in near-space vehicles. In this paper, the SoC balancing control problem is investigated by a coupling battery model with electric-thermal-aging dynamics. Firstly, a system identification experiment is carried out to obtain the internal parameters of the battery. Secondly, a weight optimization index is designed by integrating the balancing speed and battery state-of-health (SoH). Then, a receding horizon control algorithm is proposed to realize multi-unit battery SoC balancing with partial swarm optimization (PSO). Finally, the effectiveness of the proposed strategy is verified by simulation results.

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Growing Well-connected Networks for Entropy Rate Maximization

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IEEE Transactions on Automatic control 2025年

作者： Wang, Weizhen Duan, Xiaoming He, Jianping Shanghai Jiao Tong University State Key Laboratory of Submarine Geoscience Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 China

We study the problem of enhancing the maximum entropy rate of Markov chains by strategically adding edges from a candidate set to a given network. We first formulate an optimization problem to identify the optimal edges for addition, which is inherently hard since the edge selection is combinatorial. We then treat the edge addition as a perturbation to the network and derive a perturbation bound on the relative change of a low dimensional vector that is directly related to the maximum entropy rate when a small number of edges are being added. This analysis based on perturbation theory allows us to establish a linear approximation to the change of the maximum entropy rate, which further leads to efficient heuristic algorithms. Building on the efficient solution methods for vector equations in the existing literature, our algorithms only require additional simple vector operations and comparisons to obtain a near-optimal solution for the growing networks problem. Although we derive the heuristics from the perturbation theory, we further show that it can also be interpreted as moving along a direction that leads to the most improvement in the objective function. Extensive simulations validate the effectiveness of our algorithms. © 1963-2012 IEEE.

关键词： entropy rate Markov chain perturbation theory robotic surveillance

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Multi-Objective Optimization of Integrated Energy system for Cchp Based on Improved Cuckoo Search Algorithm

SSRN

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SSRN 2024年

作者： Zheng, KaiYue Zhao, ChuanZhen School of Control Science and Engineering Key Laboratory of Intelligent Control of Electrical Equipment Tiangong University Tianjin300387 China School of Electronics & Information Engineering Tianjin Key Laboratory of Optoelectronic Detection Technology and System Tiangong University Tianjin300387 China

This paper discusses the optimization and scheduling of an integrated energy system for combined cooling, heating and power (CCHP). It seeks to provide better scheduling of device outputs in the system and to decrease the daily economic and environmental operating costs of the system. We build an integrated energy system with wind turbine, micro gas turbine, electric cooling unit and so on, and establish a multi-objective optimization model with the goal of minimizing economic and environmental costs. In addition, we propose a novel optimal scheduling method. It combines chaotic mapping and cuckoo search algorithm to address issues such as slow convergence speed and high iteration counts in traditional optimization algorithms. Finally, the improved cuckoo search algorithm is used to solve the system optimization problem. The optimization solution is implemented in the case of satisfying the constraints of equipment output and power balance. The simulation results show that the CCHP system can effectively improve the economic efficiency and reduce the pollutant emissions compared with the common micro-grid system. Furthermore, compared with other algorithms, the improved cuckoo algorithm shows much better performance in solving the model and can decrease the whole system's total cost. © 2024, The Authors. All rights reserved.

关键词： Multiobjective optimization

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Obstacle Avoidance Algorithm of Handling Robot in Cultural Complex

Obstacle Avoidance Algorithm of Handling Robot in Cultural C...

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2022 International Conference on Culture-Oriented Science and Technology, CoST 2022

作者： Ren, Hui Li, Mengnan Shen, Xiaochen School of Information and Commmunication Engineering Communication University of China Beijing100024 China Key Laboratory of Acoustic Visual Technology and Intelligent Control System Beijing100024 China Beijing100024 China

ISBN: (数字)9781665462488

ISBN: (纸本)9781665462488

In order to solve the problem of rapid switching of space equipment in the cultural complex, this paper combines Moth-Flame optimization Algorithm (MFO) and Dynamic Window Approach (DWA) to realize the hybrid path planning of handling robots in dynamic environments. Compared with the mainstream algorithms, the Moth-Flame optimization algorithm has the best performance. Different paths were classified and judged, and the global path planning based on MFO was proposed. The intersection of the dynamic obstacle and the global path were detected, and the dynamic window approach was used for local path planning in the road section where the collision occurs. Finally the combination of the global path planning and the local dynamic obstacle avoidance was realized. The effect of the hybrid path planning algorithm in the complex environment of the cultural complex had been verified. © 2022 IEEE.

关键词： Optimization

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A Novel Second-Order Neurodynamic system to Fixed-Time Nash Equilibrium Seeking

A Novel Second-Order Neurodynamic System to Fixed-Time Nash ...

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International Conference on information Science and Technology (ICIST)

作者： Yao Song Xingxing Ju Chaoli Yao College of Electronics and Information Engineering Sichuan University Chengdu China Key Laboratory of System Control and Information Processing Shanghai Jiaotong University Shanghai China Key Laboratory of Engineering Modeling and Statistical Computation of Hainan Province Hainan University Haikou China

ISBN: (数字)9798350353334

ISBN: (纸本)9798350353341

In this paper, we investigate fixed-time (FT) Nash equilibrium (NE) seeking problem for non-cooperative games. A novel second-order NE seeking neurodynamic system is proposed to accelerate the convergence. The proposed neurodynamic system's trajectory converges to NE within fixed-time under potential function and strongly monotone potential function respectively. It is shown that the upper bounds of the settling time are independent of the initial state of the system. The robustness of the proposed NE seeking neurodynamic system under bounded perturbations is further studied. The practicability of the proposed NE seeking system is illustrated via a simulation example and an analog circuit implementation on Multisim 14.3.

关键词： Neurodynamics information science Upper bound Perturbation methods Games Analog circuits Nash equilibrium Robustness Trajectory Convergence

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Task Order Filing Generation Algorithm Based on Multidimensional Association Rules and Similarity Matching

Task Order Filing Generation Algorithm Based on Multidimensi...

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2024 International Conference Optoelectronic information and Optical Engineering, OIOE 2024

作者： Zhang, Xinyue Lv, Weimin Wei, Dingjiang Yang, Dongsheng Yang, Lin Naval Aviation University Yantai China No. 91593 of PLA Sanya China The Laboratory for Military-Civilian Integration Emergency Command and Control Jinan University Guangzhou China School of Public Management/Emergency Management Jinan University Guangzhou China Key Laboratory of Information System Engineering National University of Defense Technology Changsha China School of Information and Intelligent Engineering University of Sanya Academician Guoliang Chen Team Innovation Center University of Sanya Sanya China

ISBN: (数字)9781510688209

ISBN: (纸本)9781510688193

This paper examines the potential for leveraging the order dispatch algorithm in the commercial domain to enhance the intelligent allocation of military combat tasks, thereby improving the speed and accuracy of command decision-making in the context of intelligent warfare. The study proposes an "order-based" command and control model, which aims to overcome the efficiency bottleneck and information lag problem in traditional combat task allocation. The algorithmic design incorporates multi-dimensional association rules and similarity matching algorithms to guarantee the optimal matching of combat tasks and resources, thereby achieving the objectives of rapid response, efficient resource utilization, and enhanced combat effectiveness. This paper focuses on the challenges and advantages of this model in the context of naval warfare. It highlights the strategic value of order-based command and control in non-direct confrontation missions, such as reconnaissance, patrol, and intelligence gathering. The utilization of the "combat cloud," artificial intelligence, and a highly integrated command network enables the precise allocation of resources and the rapid execution of tasks. This, in turn, enhances the ISR capability, network-centered warfare effectiveness, and cross-domain synergistic warfare capability in naval warfare. The study also delineates the specific implementation process of the algorithm, which includes multi-dimensional association rule mining and similarity matching based on historical cases, with the objective of generating optimal combat scenarios and ensuring the effective execution of task orders. © 2025 SPIE.

关键词： Naval warfare

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