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Zero-sum game-based formation control for multi-USVs under hybrid irregular output constraints

nonlinear Dynamics 2025年 1-15页

作者： Tang, Daocheng Pang, Ning Wang, Xin Lei, Yan Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Westa Southwest University Chongqing400715 China Department of Automation Shanghai Jiao Tong University Shanghai200240 China Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Southwest University Chongqing400044 China

This article investigates the finite-time leader-following formation control (LFC) problem for multiple unmanned surface vehicles (USVs) subject to disturbances and hybrid irregular (HI) output constraints. Here, the HI output constraint refers to a generalized output constraint that alternates irregularly among unilateral (only upper or lower) constraints, bilateral (both upper and lower) constraints, and free-constraint over time. To handle HI output constraints in multi-USV scenarios, a novel barrier function (BF) is proposed to uniformly transform both bilateral and unilateral constrained systems into unconstrained ones for controller designs. Subsequently, a zero-sum game (ZSG)-based controller is developed for multi-USVs to guarantee robust optimal control (ROC) against the worst-case disturbance without extra anti-disturbance module designs. Furthermore, the identifier-actor-critic reinforcement learning (IAC-RL) framework is designed in the ZSG-based control scheme, where the identifier, critic, and actor are utilized to estimate unknown system dynamics, evaluate system performance, and implement control policy, respectively. Finally, the effectiveness of the proposed control scheme is demonstrated through simulations of the multi-USV model. © The Author(s), under exclusive licence to Springer Nature B.V. 2025.

关键词： Robust control

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Distributed Fixed-Time Dynamic Event-Triggered Algorithm for Economic Dispatch in Smart Grids Over Directed Networks

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2025年 3卷 173-180页

作者： Ji, Lianghao Liu, Qi Zhang, Cuijuan Yang, Shasha Li, Huaqing Chongqing University of Posts and Telecommunications Chongqing Key Laboratory of Image Cognition Chongqing400065 China Southwest University Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Chongqing400715 China

A fresh algorithm is presented for tackling the economic dispatch problem (EDP) in smart grids with directed network topology. This algorithm is based on distributed consensus and aims to minimize the total cost of power generation while maintaining the balance between power supply and demand and complying with generation capacity restrictions. Even with unknown and bounded disturbances, this algorithm can achieve optimal dispatch within a fixed time. In addition, an expected settling time can be calculated, which is a more accurate upper bound estimation that relies solely on the generators' initial conditions. Meanwhile, the dynamic event-triggered strategy is employed for reducing sampling frequency and network resource consumption. Finally, numerical simulations validate the effectiveness of the designed algorithm. © 2024 IEEE.

关键词： Consensus algorithm

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Identification of non-Restorative Sleep Associated with Depression Using Ambulatory Electrocardiogram and Triaxial Acceleration

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IEEE Transactions on Biomedical Engineering 2025年第6期72卷 1909-1919页

作者： Liu, Li Wang, Chongyang Liu, Guangyuan Wen, Wanhui Southwest University Chongqing Key Laboratory of Generic Technology and System of Service Robots Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Chongqing400715 China Sichuan University Key Laboratory of Rehabilitation Medicine in Sichuan Province Department of Rehabilitation Medicine Institute of Rehabilitation Medicine West China Hospital Chengdu610041 China

Objective: non-restorative sleep is prevalent among individuals with depression and is strongly associated with the severity of the condition. Therefore, identifying non-restorative sleep can aid in the early screening of depression. Investigating non-restorative sleep in depression necessitates long-term monitoring under naturalistic conditions. Methods: In this study, we recruited 149 participants and collected electrocardiogram and triaxial acceleration from them, resulting in a total of 761 nights of data. The period from midnight to 6:30 AM was segmented into 78 five-minute intervals, from which 40 physiological features were extracted for each interval. To deal with variations in sleep patterns across participants and dates, we reordered the sleep data based on levels of parasympathetic nervous system (PNS) activation to explore the underlying neural mechanisms of non-restorative sleep in individuals with depressive symptoms. Results: We developed a model that integrated convolutional neural networks with an attention mechanism to identify nonrestorative sleep in individuals with depressive symptoms. The model demonstrated impressive performance on an independent test set, achieving an accuracy of 81.25% and an F1 score of 77.85%. Additionally, Bayes' theorem was used to compute the posterior probability indicating nonrestorative sleep in this population, assessing abnormal PNS activation. Conclusion: Finally, we designed a system capable of automatically evaluating nighttime sleep status and quantifying changes in non-restorative sleep associated with depression. Significance: This system offers a novel tool and method for the early identification of individuals at risk of depression. © 1964-2012 IEEE.

关键词： Sleep research

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High-order consensus graph learning for incomplete multi-view clustering

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Applied Intelligence 2025年第6期55卷 1-25页

作者： Guo, Wei Che, Hangjun Leung, Man-Fai College of Electronic and Information Engineering Southwest University Chongqing400715 China Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Chongqing400715 China Faculty of Science and Engineering Anglia Ruskin University CambridgeCB1 1PT United Kingdom

Incomplete Multi-View Clustering (IMVC) aims to partition data with missing samples into distinct groups. However, most IMVC methods rarely consider the high-order neighborhood information of samples, which represents complex underlying interactions, and often neglect the weights of different views. To address these issues, we propose a High-order Consensus Graph Learning (HoCGL) model. Specifically, we integrate a reconstruction term to recover the incomplete multi-view data. High-order proximity matrices are constructed, and the self-representation similarity matrices and multiple high-order proximity matrices are learned mutually, allowing the similarity matrices to incorporate complex high-order information. Finally, the consensus graph representation is derived from the similarity matrices through a self-weighted strategy. An efficient algorithm is designed to solve the proposed model. The excellent clustering performance of the proposed model is validated by comparing it with eight state-of-the-art models across nine datasets. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Consensus algorithm

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A Survey of Distributed Algorithms for Aggregative Games

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IEEE/CAA Journal of Automatica Sinica 2025年第5期12卷 859-871页

作者： Li, Huaqing Li, Jun Ran, Liang Zheng, Lifeng Huang, Tingwen College of Electronic and Information Engineering Southwest University Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Chongqing400715 China College of Computer Science and Engineering Chongqing University of Technology Chongqing400054 China School of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen518107 China

Game theory-based models and design tools have gained substantial prominence for controlling and optimizing behavior within distributed engineering systems due to the inherent distribution of decisions among individuals. In non-cooperative settings, aggregative games serve as a mathematical framework model for the interdependent optimal decision-making problem among a group of non-cooperative players. In such scenarios, each player's decision is influenced by an aggregation of all players' decisions. Nash equilibrium (NE) seeking in aggregative games has emerged as a vibrant topic driven by applications that harness the aggregation property. This paper presents a comprehensive overview of the current research on aggregative games with a focus on communication topology. A systematic classification is conducted on distributed algorithm research based on communication topologies such as undirected networks, directed networks, and time-varying networks. Furthermore, it sorts out the challenges and compares the algorithms' convergence performance. It also delves into real-world applications of distributed optimization techniques grounded in aggregative games. Finally, it proposes several challenges that can guide future research directions. © 2014 Chinese Association of Automation.

关键词： Nash equilibrium

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Deep Learning and Projection Neural Network With Finite-Time Convergence for Energy Management of Multi-Energy System

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IEEE Transactions on Smart Grid 2025年第3期16卷 2156-2168页

作者： Liu, Xueying He, Xing Li, Chaojie Huang, Tingwen Southwest University Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Chongqing400715 China North China Electric Power University State Key Laboratory of Alternate Electrical Power System With Renewable Energy Sources Beijing102206 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518055 China

In this paper, an approach based on projection neural network (PNN), sliding mode control technique, and deep learning is proposed to solve the energy management problem of multi-energy systems (MES) containing dynamic parameters. First, the sliding mode technique is introduced in the PNN to design a finite-time PNN (FTPNN). The stability and finite-time convergence of the proposed FTPNN are proved by the Lyapunov method and the setting time bound is given. Then, the deep FTPNN (DFTPNN) is designed by combining deep learning with the proposed FTPNN. The dynamic parameters in the MES that change over time are used as input variables for the DFTPNN, allowing the trained DFTPNN to respond immediately to changes in the dynamic parameters and to predict the solutions of the FTPNN with different parameters directly. Simulation experiments show that FTPNN has faster convergence compared to PNN. DFTPNN significantly reduces the computation time compared to FTPNN. DFTPNN provides predicted solutions to FTPNN. Since DFTPNN can respond immediately to changes in dynamic parameters and directly provide energy management strategies under different parameters, it can adapt to changing environments and promote the economic and stable operation of MES. © 2010-2012 IEEE.

关键词： Cogeneration Heuristic algorithms Convergence Deep learning Prediction algorithms Optimization Heating systems Energy management Approximation algorithms Power system stability

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Distributed Fixed-Time Algorithms for Time-Varying Constrained Optimization Problems

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年

作者： He, Xing Li, Yue Zhang, Meng Huang, Tingwen Southwest University Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Chongqing400715 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518055 China

In this paper, the distributed form of the zeroing neural network for solving time-varying optimal problems is put forward. Compared with traditional centralized algorithms, distributed algorithms possess better privacy and scalability. This paper initially proposes a centralized time-varying optimisation algorithm with fixed-time convergence and certain robustness, which is based on the integration-enhanced zeroing neural network. Subsequently, the algorithm is enhanced, and two distributed algorithms are designed separately. Both of these two algorithms have a fixed convergence time and certain robustness. Additionally, this paper utilizes the penalty function approach to handle time-varying optimization problems with inequality constraints, thereby making the algorithm more widely applicable. The effectiveness of the algorithm is verified through several numerical examples, and the applicability of the algorithm is demonstrated by solving the package-level state-of-charge balancing problem. © 2020 IEEE.

关键词： Optimization algorithms

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Robust Deep Matrix Factorization with Low-rank and Hypergraph Learning for Multi-view Data processing

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IEEE Transactions on Consumer Electronics 2025年

作者： Pan, Baicheng Che, Hangjun Li, Chenglu Li, Hongfei Southwest University College of Electronic and Information Engineering Chongqing400715 China Hong Kong Baptist University Department of Mathematics Kowloon Tong Hong Kong Southwest University Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Chongqing400715 China

As the advancement of sensing technology and cyber-physical equipment, the complexity and dimensionality of the data from consumer internet of things have increased dramatically. processing the complex and multi-view data to analyze the consumption characteristics becomes a significant issue. Deep matrix factorization (DMF) is a widely spread method with the ability to learn intrinsic components in data based on its multiple-layers structures. However, the multiple-layers structures may magnify noises in data and the standard DMF cannot reflect the manifold and low-rank structures on the coefficient matrix well. In the paper, a robust deep matrix factorization with low-rank and hypergraph learning (RDMFLRH) model is proposed for multi-view data processing. Firstly, an arc tangent loss function with less sensitivity to noises and outlines is introduced to enhance robustness. Additionally, low-rank and hypergraph learning are adopted to extract the relationships between clusters and maintain the binary manifold structures from the original space. Then, an optimization algorithm based on the multiplicative update rule is developed to solve the proposed model with convergence proven theoretically and experimentally. Finally, abundant experiments on real-world and noisy datasets validate the superiority of the proposed method, improving around 1% to 10% performance compared with the comparison state-of-the-art methods. © 1975-2011 IEEE.

关键词： Matrix factorization

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Secrecy outage analysis for underlay cognitive radio networks over correlated channels

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Science China(information Sciences) 2017年第2期60卷 107-115页

作者： Jiliang ZHANG Hui ZHAO Gaofeng PAN Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing School of Electronic and Information Engineering Southwest University

This paper investigates the secrecy outage performance of underlay cognitive radio networks, in which a source in a secondary system transmits its confidential information to a legitimate destination in the presence of an eavesdropper. Specifically, the main(the source-to-destination) and eavesdropping(the sourceto-eavesdropper) channels are assumed to be correlated. Moreover, it is also assumed that the main channel and the channel from the source to the primary user's receiver are correlated. Tight closed-form analytical expression for secrecy outage probability and the closed-form analytical expression for the probability of non-zero secrecy capacity are derived and validated by simulation results when the interference temperature limit is comparably large.

关键词： cognitive radio networks correlated fading channels probability of non-zero secrecy capacity secrecy outage probability

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A Fully Distributed Approach to Optimal Energy Scheduling of Users and Generators Considering a Novel Combined Neurodynamic Algorithm in Smart Grid

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IEEE/CAA Journal of Automatica Sinica 2021年第7期8卷 1325-1335页

作者： Chentao Xu Xing He the Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information EngineeringSouthwest UniversityChongqing 400715China

A fully distributed microgrid system model is presented in this *** the user side,two types of load and plug-in electric vehicles are considered to schedule energy for more *** charging and discharging states of the electric vehicles are represented by the zero-one variables with more *** solve the nonconvex optimization problem of the users,a novel neurodynamic algorithm which combines the neural network algorithm with the differential evolution algorithm is designed and its convergence speed is faster.A distributed algorithm with a new approach to deal with the inequality constraints is used to solve the convex optimization problem of the generators which can protect their *** results and comparative experiments show that the model and algorithms are effective.

关键词： Differential evolution algorithm distributed algorithm electric vehicle neural network zero-one variable.

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