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Stability Analysis of T-S Fuzzy State-Dependent Impulsive nonlinear Systems Based on New Improved Functional

INTERNATIONAL JOURNAL OF ROBUST AND nonlinear CONTROL 2025年第0期

作者： Chang, Fei Li, Chuandong Zhou, Yinghua Deng, Hao Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligent Chongqing Peoples R China Chongqing Univ Sci & Technol Sch Intelligent Technol & Engn Chongqing Peoples R China

In this paper, the authors propose an extended functional-based approach and develop two new improved functionals to investigate the stability of T-S fuzzy nonlinear systems with state-dependent impulsive inputs (TSFSSI). Firstly, the conversion of nonlinear systems characterized by state-dependent impulsive inputs is accomplished by employing the T-S fuzzy model and B-equivalence method, resulting in T-S fuzzy nonlinear systems with fixed-time impulsive inputs (TSFSFI). Secondly, two novel improved functionals that rely on bilateral looped functional (BLF), Lyapunov functions, and additional new functional elements are proposed. In contrast to BLF, the improved functionals not only alleviate the positivity constraints on Lyapunov functions, thereby reducing the conservativeness of stability conditions, but also impose fewer restrictions on the functional construction. Leveraging these enhanced functionals along with certain inequalities, sufficient conditions are obtained to ensure the asymptotic stability of TSFSSI. Finally, the effectiveness of the proposed conditions is validated through two application examples.

关键词： new improved functional nonlinear systems stability state-dependent impulse T-S fuzzy model

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Time-Varying Momentum-Like Neurodynamic Optimization Approaches With Fixed-Time Convergence for Nash Equilibrium Seeking in Noncooperative Games

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2025年第6期55卷 3856-3865页

作者： Ju, Xingxing Yang, Xinsong Li, Chuandong Sichuan Univ Coll Elect & Informat Engn Chengdu 610065 Peoples R China Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligent Chongqing 400715 Peoples R China

In this article, several novel time-varying momentum-like neurodynamic optimization approaches are proposed for Nash equilibrium (NE) seeking of noncooperative games. It is shown that the dynamics trajectories converge to NE within fixed-time from arbitrary initial conditions, achieving a quicker convergence rate through the selection of distinct time-varying coefficients. Moreover, the upper bounds of the settling time for the proposed NE seeking neurodynamic approaches are explicitly provided. In addition, the study investigates the robustness of the designed neurodynamic approaches in the presence of bounded noises. The superior convergence properties and practicability of our approaches are demonstrated through a simulation example involving energy consumption games.

关键词： Convergence Games Neurodynamics Optimization Vectors Nash equilibrium Upper bound Time-varying systems laboratories information processing Fixed-time (FT) convergence Nash equilibrium (NE) neurodynamic optimization approaches noncooperative games

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Triboelectric Sensor Based on NaCl/agar Hydrogel as Portable Implicit Alarm Cipher System

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JOURNAL OF ELECTRONIC MATERIALS 2025年第6期54卷 4388-4400页

作者： Mou, Qirui Wei, Ouyang Qu, Bingbing Liu, Hongfu Xie, Yiyuan Chen, Bin Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuit & Intelligent Chongqing 400715 Peoples R China

Traditional locks exhibit vulnerabilities in emergency situations, as they lack alarm or response mechanisms and are unable to prevent brute force attacks. Amid the surge in IoT and biometric technology, smart locks are undergoing transformative improvements. Biometric locks, offering enhanced security and convenience, still face challenges such as irreversible data risks and high costs. To address these limitations, we proposed a green, safe triboelectric nanogenerator (AN-TENG) based on NaCl/agar hydrogel for a portable implicit alarm cipher system. By optimizing the concentration of agar solution and NaCl dopant, the sensor achieves a highest sensitivity of 3.61 V/kPa within the pressure range of 0-6 kPa and a low-pressure detection limit (33.32 mg). Then, a smart breath detector and a keyboard were fabricated by utilizing the proposed sensor, employed to design a portable implicit cipher system, where the keyboard enabled digital input unlocking, and the breath detector triggered implicit alarm. The system offers a hidden alarm function, and, by leveraging neural network algorithms, it achieves a recognition accuracy of 99% on identifying distinct respiratory patterns, and can provide high security in critical scenarios. This innovation leverages the strengths of TENGs to deliver a more secure and versatile smart lock alternative.

关键词： Self-powered triboelectric sensor NaCl/agar hydrogel deep learning algorithm implicit alarm

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Dynamic-Based Privacy Preservation for Distributed Economic Dispatch of Microgrids

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IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS 2025年第1期12卷 1029-1039页

作者： Cheng, Huqiang Liao, Xiaofeng Li, Huaqing Lu, Qingguo Chongqing Univ Coll Comp Sci Key Lab Dependable Serv Comp Cyber Phys Soc Minist Educ Chongqing 400044 Peoples R China Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligent Chongqing 400715 Peoples R China

With an escalating emphasis on distributed economic dispatch within microgrid systems due to its inherent adaptability, scalability, and sustainability, an extensive focus on the confidentiality of this field is pronouncedly emerging. The primary emphasis of this study is the safeguarding of power-sensitive information in the distributed economic dispatch issue prevalent in microgrids. This pursuit leads us to the development of a distributed optimization algorithm that preserves privacy, tailored for directed networks. The algorithm strives to secure a balance between supply and demand at the lowest economic cost, all while adhering to real-world constraints and maintaining the confidentiality of power-sensitive information. To fulfill this objective, we propose a novel privacy-preserving distributed algorithm that capitalizes on the inherent resilience exhibited by system dynamics toward uncertainty. Specifically, to ensure privacy preservation, we strategically incorporate randomness into the mixing weights, thereby generating a degree of uncertainty in communication messages during the initial iteration. Rigorous analysis is built to delineate that our method can achieve exact convergence and ensure the confidentiality of power-sensitive information. Further, additional numerical trials conducted on an IEEE 14-bus system substantiate the algorithm's practical efficiency.

关键词： Privacy Microgrids Costs Convergence Uncertainty Cost function Supply and demand Consensus optimization economic dispatch (ED) microgrids privacy preservation

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Lightweight chip pad real-time alignment detection method and application based on improved YOLOv5s

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JOURNAL OF ELECTRONIC IMAGING 2025年第1期34卷

作者： Zou, Yanli Zhang, Zongjian Zhou, Chiyang Tan, Yufei Guangxi Normal Univ Sch Elect & Informat Engn Guangxi Key Lab Brain Inspired Comp & Intelligent Guilin Peoples R China Guangxi Normal Univ Educ Dept Guangxi Zhuang Autonomous Reg Key Lab Nonlinear Circuits & Opt Commun Guilin Peoples R China

Chip pad alignment inspection is of great importance in the industrial field. However, due to the fact that chip pads are usually small, problems such as misdetection and missed detection often occur. When applying deep learning methods for chip pad detection, it is necessary to ensure accurate detection of small target chips while meeting the requirements of lightweight detection models for industrial needs. To solve the above problems, we propose a lightweight model based on improved YOLOv5s. First, the feature extraction part is improved to increase the network's focus on the target. Second, the feature fusion layer is improved to double the resolution of the prediction head, and the context-aware network is designed to enhance the context-capture ability of key features of small targets. Finally, SIoU is adopted as the loss function to improve the speed and accuracy of the regression frame. The experimental results show that the improved YOLOv5s algorithm improves the detection accuracy by 2.3% and reduces the network parameters by 81.8% compared with the original algorithm. The improved algorithm is combined with image processing techniques to design correction methods for alignment anomalies and realize real-time alignment anomaly correction in the industry. This approach holds significant industrial application value for fully automated chip testing and sorting integrated production.

关键词： deep learning chip detection small target detection lightweighting

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Data-driven bipartite synchronization control of multi-agent systems with asymmetric input saturation over switching networks

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APPLIED MATHEMATICS AND COMPUTATION 2025年 494卷

作者： Shen, Ziwen Dong, Tao Huang, Tingwen Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligent Chongqing 400715 Peoples R China Shenzhen Univ Adv Technol Fac Comp Sci & Control Engn Shenzhen 518055 Peoples R China

This paper addresses the bipartite synchronization (BS) issue of nonlinear discrete-time multi- agent systems (NDTMASs) with asymmetric input saturation in switching cooperative-competitive networks. First, a local neighborhood error system (LNES) is constructed based on the switching cooperative-competitive networks. By utilizing a hyperbolic function, we design a novel local cost function and derive an optimal control policy. Based on this control policy, we construct a novel saturation-input policy iteration (SIPI) technique. Furthermore, we establish the convergence of the SIPI algorithm. It is demonstrated that the LNES can converge to zero, and thus the BS problem for MASs can be solved. To implement the SIPI algorithm, a control framework based on an actor-critic architecture is constructed. To achieve asymmetric input saturation, we select an asymmetric constrained function as the activation function for the actor network. Finally, an example is presented to illustrate the reliability of our approach.

关键词： Multi-agent systems Input saturation Cooperative-competitive networks Bipartite synchronization Switching topologies

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Predicting chaos in permanent magnet synchronous motor using dual reservoir interaction with high-order state reservoir computing

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PHYSICA SCRIPTA 2025年第3期100卷 036010-036010页

作者： Ye, Zuo Wei Wei, Du Qu Guangxi Normal Univ Coll Elect & Informat Engn Guangxi Key Lab Brain Inspired Comp & Intelligent Guilin 541004 Peoples R China Guangxi Normal Univ Educ Dept Guangxi Zhuang Autonomous Reg Key Lab Nonlinear Circuits & Opt Commun Guilin Peoples R China

Permanent Magnet Synchronous Motor (PMSM), as core driving components of new energy vehicles, face significant challenges in performance and stability due to their complex chaotic behavior. Accurate prediction of chaotic behavior is crucial for the modeling and control of PMSM. This paper proposes a novel approach based on Reservoir Computing-the Dual Reservoir Interacting with High-Order State Reservoir Computing (DRI-HOSRC)-for predicting chaos in PMSM. By introducing high-order state expansion and the dual reservoir interaction mechanism, this method significantly enhances the ability to capture the nonlinear dynamic characteristics of PMSM. Experimental results show that the proposed DRI-HOSRC model outperforms traditional methods in terms of both prediction accuracy and time range, enabling a more precise capture of the system's complex chaotic features. These results validate the model's potential for application in PMSM, providing an innovative solution for accurate forecasting. The research also demonstrates the advantages of the dual reservoir interacting with high-order state reservoir model in nonlinear dynamics modeling and offers an effective pathway for long-term predictions of complex systems.

关键词： Permanent Magnet Synchronous Motor (PMSM) dual reservoir interaction high-order state expansion chaos prediction nonlinear dynamics

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Chaotic time series prediction of motor system based on WOA-MFRFNN

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PHYSICA SCRIPTA 2025年第1期100卷

作者： Ma, Qianran Wei, Duqu Guangxi Normal Univ Coll Elect & Informat Engn Guangxi Key Lab Brain inspired Comp & Intelligent Guilin 541004 Peoples R China Guangxi Normal Univ Educ Dept Guangxi Zhuang Autonomous Reg Key Lab Nonlinear Circuits & Opt Commun Guilin Peoples R China

The complexity of the operation of permanent magnet synchronous motor (PMSM) has heightened interest in its stability and reliability. Evidence suggests that chaotic oscillatory behavior can occur during PMSM operation, making the prediction of such chaotic motion crucial for maintaining stable motor performance. In this paper, a WOA-MFRFNN (Whale Optimization Algorithm-Multi Functional Recurrent Fuzzy Neural Network) model is proposed to predict chaotic time series of PMSM, where WOA is used to train the network weights and the MFRFNN contains two TSK fuzzy rules for fuzzy neural networks, which are used for the output and the state of network, respectively. The experimental results of WOA-MFRFNN are compared with those of SSA-BP, ROA-RF and iTransformer. The results imply that the WOA-MFRFNN outperforms other methods and the RMSE can reach 5.45 x 10-4. It is also shown that the WOA-MFRFNN model exhibits good predictive effect on the real-world PMSM dataset.

关键词： chaotic time series motors WOA MFRFNN

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Optimized backstepping tracking control using reinforcement learning for strict-feedback nonlinear systems with monotone tube performance boundaries

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INTERNATIONAL JOURNAL OF CONTROL 2025年

作者： Zhang, Gengning Wang, Xin Wang, Ziming Pang, Ning Southwest Univ Coll Westa Chongqing Key Lab Nonlinear Circuits & Intelligent Chongqing Peoples R China Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligent Chongqing Peoples R China Hong Kong Univ Sci & Technol Guangzhou Syst Hub Robot & Autonomous Syst Thrust Guangzhou Peoples R China Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China

Recently proposed monotone tube boundaries enhance performance but exacerbate the challenge of the entry capture problem (ECP). This article addresses the ECP under monotone tube boundaries and employs optimal control strategies combined with reinforcement learning (RL) to enhance performance in unknown state situations. By utilising error scaling functions (ESF), the proposed method effectively confines the error to a predefined neighbourhood in a specified time, without knowing the specific value of the initial error. Furthermore, this article innovatively incorporates the actor-critic structure into the monotone tube boundaries, significantly enhancing system performance. Due to the condition of state immeasurability, a neural network (NN)-based state observer is designed to estimate the states of the system. Additionally, command filters are used to simplify the multiple derivative calculations in traditional backstepping methods, addressing the 'explosion of complexity' problem. Mathematical derivations and simulation confirm its ability to meet predefined performance criteria.

关键词： Entry capture problem (ECP) error scaling functions (ESF) prescribed performance control (PPC) reinforcement learning (RL) nonlinear systems

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DPO-Face: Differential privacy obfuscation for facial sensitive regions

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COMPUTERS & SECURITY 2025年 154卷

作者： Luo, Yuling Hu, Tinghua Ouyang, Xue Liu, Junxiu Fu, Qiang Qin, Sheng Min, Zhen Lin, Xiaoguang Guangxi Normal Univ Sch Elect & Informat Engn Guangxi Key Lab Brain inspired Comp & Intelligent Guilin 541004 Peoples R China Guangxi Normal Univ Educ Dept Guangxi Zhuang Autonomous Reg Key Lab Nonlinear Circuits & Opt Commun Guilin 541004 Peoples R China

User-sensitive face images captured by widely used image-collection devices are frequently shared on social media. If these images are misused, they may pose a serious threat to users' privacy. To ensure both privacy-preserving and image usability, this work introduces a Differential Privacy Obfuscation method of Face images (DPO-Face), which addresses the current limitations in balancing privacy and recognition accuracy. DPO-Face effectively balances privacy preservation and recognition accuracy to meet practical application demands. First, sensitive and non-sensitive regions of the image are accurately identified and located using an improved hybrid convolutional neural network by DPO-Face. Subsequently, face parsing technology is employed to precisely segment the input face image into multiple internal and external facial components. Moreover, precisely adjusted noise is introduced to the internal facial component regions using a differential privacy mechanism to disturb them, effectively protecting the privacy information of these regions while leaving the non-sensitive external components unchanged. Finally, the privacy-protected image is transmitted to the face detection and recognition module to evaluate the effectiveness of the privacy protection, such as maintaining high face detection and recognition accuracy. Experimental results demonstrate that DPO-Face meets epsilon-local differential privacy requirements, achieving recognition rates of 91%-96% and a maximum privacy protection success rate of 0.9720. This method allows the privacy level to be precisely adjusted, preventing privacy leaks to honest but curious third-party servers, thus achieving a balance between privacy-preserving and usability.

关键词： Differential privacy Privacy-preserving Neural network

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