Article
Computer Science, Artificial Intelligence
Meilan Tang, Xiaofang Hu, Xinge Liu, Qiao Chen
Summary: This paper studies the asymptotic stability of static neural networks with interval time-varying delay, proposing an improved integral inequality and a novel Lyapunov-Krasovskii functional. Two less conservative delay-dependent stability criteria are derived based on the improved non-convex technique, which are validated by numerical simulation.
Article
Engineering, Electrical & Electronic
Yufeng Tian, Zhanshan Wang
Summary: This study presents an improved solution to the problem of H-infinity performance state estimation for static neural networks with time-varying delays. By deriving a less conservative criterion and designing estimator gain matrices independent of activation function, the effectiveness of the estimation method has been improved. This approach eliminates the constraint of having invertible activation functions, as compared to existing works.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS
(2021)
Article
Computer Science, Artificial Intelligence
Guoqiang Tan, Zhanshan Wang
Summary: This paper investigates the problem of extended dissipativity state estimation for delayed generalized neural networks, proposing a wdelay-product-type Lyapunov-Krasovskii functional and a delay-product-type integral inequality to accurately estimate the upper bound of time-derivative. Sufficient conditions are obtained to guarantee the accuracy of state estimation, with simulations demonstrating the effectiveness of the proposed method.
Article
Mathematics, Interdisciplinary Applications
Jin Yang, Jigui Jian
Summary: This paper focuses on the existence conditions of quasi-invariant set, global attracting set, and global exponential attracting set of competitive neural networks with time-varying and infinite distributed delays. A new bidirectional delay integral inequality and a novel integro-differential inequality are established. The obtained conditions and frameworks provide insights into the dynamics of the discussed system.
CHAOS SOLITONS & FRACTALS
(2023)
Article
Mathematics, Applied
Junkang Tian, Zerong Ren, Yanmin Liu
Summary: This paper studies the stability analysis of systems with an interval time-varying delay. New integral inequalities are introduced and less conservative stability criteria are proposed based on these inequalities. The advantages of these criteria are demonstrated through numerical examples.
Article
Computer Science, Artificial Intelligence
Xing He, Yu-bin Wu, Li-jun Song
Summary: The paper proposes an improved robust stability analysis method for linear systems with norm-bounded uncertainty and interval time-varying delay. The method divides the delay interval into multiple subintervals and introduces a new Lyapunov-Krasovskii functional for each subinterval. A novel delay-dependent stability criterion is proved using integral inequality approach. Numerical examples and a power system are used to verify the effectiveness of the proposed method.
Article
Automation & Control Systems
Jiemei Zhao
Summary: This paper deals with the reachable set estimation problem for singular systems with time-varying delays and bounded peak disturbances. An improved criterion is established using linear matrix inequalities to guarantee the regularity, impulse-free nature, and boundedness of the reachable set. Using a relaxed Lyapunov-Krasovskii functional, the addressed problem is solved without requiring all involved symmetric matrices to be positive definite. Numerical examples are provided to demonstrate the effectiveness of the proposed methods.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2021)
Article
Automation & Control Systems
Yufeng Tian, Yuzhong Wang, Junchao Ren
Summary: This article focuses on the event-triggered H-infinity state estimation problem of delayed neural networks. A new event-triggered scheme is designed to balance the performance of the state estimator and network communication bandwidth. Additionally, an auxiliary function-type free-matrix-based integral inequality is proposed to establish conditions for the estimation error system to be stable and satisfy H-infinity performance.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2022)
Article
Mathematics, Applied
Zhengliang Zhai, Huaicheng Yan, Shiming Chen, Yufang Chang, Jing Zhou
Summary: This paper investigates the stability of generalized neural networks (GNN) with time-varying delay. By constructing an augmented Lyapunov-Krasovskii functional (LKF) and introducing two sets of state vectors, new negative definite conditions (NDCs) are proposed to establish stability conditions. Numerical examples illustrate the effectiveness of the provided conditions.
APPLIED MATHEMATICS AND COMPUTATION
(2024)
Article
Mathematics, Applied
Yufeng Tian, Zhanshan Wang
Summary: This paper explores the H-infinity performance state estimation problem for static neural networks with time-varying delays. A new criterion is proposed by combining a parameter-dependent reciprocally convex inequality and an improved Lyapunov-Krasovskii functional, leading to improved performance and stability of the estimator. The study overcomes restrictions on slack matrices and demonstrates advantages through two examples.
APPLIED MATHEMATICS AND COMPUTATION
(2021)
Article
Computer Science, Artificial Intelligence
Yufeng Tian, Zhanshan Wang
Summary: This paper investigates extended dissipative state estimation for static neural networks with time varying delays, introducing a new DPT functional and a PDRCI inequality to tighten the bounds. The estimator design condition ensures asymptotic stability and dissipativity, with gain matrices solved using LMIs. Flexibility of estimator solutions is increased by overcoming restrictions on slack matrices, as demonstrated with an example.
Article
Computer Science, Artificial Intelligence
Guoqiang Tan, Zhanshan Wang
Summary: This paper introduces a new method for analyzing the stability of recurrent neural networks with time-varying delay, first by deriving an extended delay-dependent integral inequality, then by estimating a tight upper bound of the Lyapunov-Krasovskii functional derivative to obtain a new criterion for stability analysis, and finally by providing simulation results to verify the superiority of the method.
NEURAL PROCESSING LETTERS
(2021)
Article
Mathematics, Applied
Seung-Hoon Lee, Myeong-Jin Park, Oh-Min Kwon
Summary: This study investigates improved stability conditions for linear systems with time-varying delays. A proposed integral inequality derived from the properties of simple matrices is simply proved and represents a generalized form of various integral inequalities. Different forms of integral inequality can be obtained based on N. Stability results are obtained by extending the augmented terms of Lyapunov-Krasovskii functionals according to N. Finally, two well-known numerical examples demonstrate that the stability criteria derived through the proposed generalized integral inequality are less conservative than existing criteria.
APPLIED MATHEMATICS LETTERS
(2023)
Article
Engineering, Electrical & Electronic
Guoqiang Tan, Zhanshan Wang
Summary: This brief introduces a method for generalized dissipativity state estimation for static neural networks, utilizing an improved convex inequality and a PI estimator. Simulation results demonstrate the superior performance and advantages of the proposed method over existing ones.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS
(2021)
Article
Mathematics
Yupeng Shi, Dayong Ye
Summary: This paper revisits the stability analysis problem for neural networks with time-varying delay. A composite-matrix-based integral inequality (CMBII) is proposed, which considers the delay derivative. In this case, the coupling information can be fully captured in integral inequalities with the delay derivative. A new stability criterion is derived for neural networks with time-varying delay based on CMBII. The effectiveness of this method is verified through a numerical example.
Article
Automation & Control Systems
Wei Zhang, Bao-Lin Zhang, Qing-Long Han, Feng-Bin Pang, Yue-Ting Sun, Xian-Ming Zhang
Summary: This paper addresses the recoil suppression problem of a deepwater drilling riser system through active Hoo control using both current and delayed states. A dynamic equation of the system is established based on a spring-mass-damping model, considering the platform heave motion and friction force induced by drilling discharge mud and seawater. A delayed state feedback Hoo controller with delayed and current states is designed to reject recoil movements of the riser. The effects of time-delays on recoil control are analyzed, and the design of optimal artificial time-delays is formulated. Simulation results demonstrate the effectiveness of delay-free and delayed Hoo recoil control schemes.
Article
Automation & Control Systems
Xian-Ming Zhang, Qing-Long Han, Xiaohua Ge, Chen Peng
Summary: Recent research has demonstrated that a less conservative stability criterion can be obtained for continuous-time linear systems with a time-varying delay by incorporating a Lyapunov-Krasovskii functional with a polynomial matrix. This paper focuses on analyzing the stability of discrete-time linear systems with time-varying delays using a delay-square-dependent Lyapunov functional. A novel convex approach is presented to formulate a less conservative stability criterion, which is validated through numerical simulations. Moreover, it is shown that the polynomial inequality method is not applicable due to its high numerical complexity.
Article
Computer Science, Artificial Intelligence
Fei Long, Chuan-Ke Zhang, Yong He, Qing-Guo Wang, Zhen-Man Gao, Min Wu
Summary: This article improves the techniques for passivity analysis of neural networks with time-varying delay to establish a new criterion with less conservatism. It constructs a Lyapunov-Krasovskii functional (LKF) with general delay-product-type terms, develops a general convexity lemma, and obtains a hierarchical passivity criterion of less conservatism for neural networks with time-varying delay.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Tian-Shun Peng, Hong-Bing Zeng, Wei Wang, Xian-Ming Zhang, Xin-Ge Liu
Summary: This article focuses on the stability and stabilization problem of Takagi-Sugeno (T-S) fuzzy systems with time-varying delay. A new Lyapunov-Krasovskii functional is proposed, which considers more information on the time-varying delay by taking into account the membership functions. Criteria on the stability and stabilization are obtained for the closed-loop system by utilizing an N-order free-matrix-based integral inequality and a switching method. Parameter tuning and an iterative algorithm are developed to calculate control gains. Numerical examples demonstrate that the proposed criteria yield less conservative results compared to existing ones.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Automation & Control Systems
Chao Gan, Wei-Hua Cao, Lu-Zhao Wang, Kang-Zhi Liu, Min Wu
Summary: This article presents an improved dynamic optimization control system for the rate of penetration (ROP) in drilling process, which successfully improves drilling efficiency and safety. The system utilizes a three-layer framework and employs if-then strategy to identify drilling conditions and a moving window strategy to establish a dynamic ROP model. The Jaya algorithm is introduced to solve the dynamic ROP optimization issue. Simulation and industrial application results show that the proposed system significantly enhances drilling efficiency and safety.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2023)
Article
Automation & Control Systems
Xing-Chen Shangguan, Yong He, Chuan-Ke Zhang, Wei Yao, Yifan Zhao, Lin Jiang, Min Wu
Summary: In this article, a resilient and active time-delay-compensation-based LFC scheme is proposed to compensate the random time delays and time-delay attacks. A state observer is employed to estimate the state of the LFC system, and a networked predictive control method is used to predict the control signals of the system in future moments. An evaluation and compensation scheme for random time delays and time-delay attacks is constructed in the actuator side of the LFC scheme.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2023)
Article
Automation & Control Systems
Yang Zhou, Chengda Lu, Menglin Zhang, Xin Chen, Min Wu, Weihua Cao
Summary: In the geological drilling process, a reliable and highly accurate ROP prediction model is difficult to construct due to data pollution and nonlinearity. A novel ROP model is developed to handle abnormal data and achieve nonlinear fitting using a local outlier factor and support vector regression (SVR) method. A modified bat algorithm (MBA) is used to determine the optimal value of hyperparameters for the SVR-based ROP model. Experimental results show that the developed model has higher ROP prediction accuracy compared to other methods.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Automation & Control Systems
Qicheng Mei, Jinhua She, Feng Wang, Min Wu, Qing-Guo Wang, Yosuke Nakanishi
Summary: This article presents a sliding-mode-control-based-equivalent-input-disturbance (SMCEID) approach that enhances the disturbance-rejection performance of a plant. The control scheme includes a conventional equivalent-input-disturbance (EID) estimator and a newly added sliding-mode controller (SMC). The SMC sets the state-observation error as a sliding-mode surface and employs a binary-search algorithm to select the gain, ensuring the sliding-mode surface converges to the origin and enhancing disturbance-rejection performance. The validity of the SMCEID approach is demonstrated through its application in a Stewart-platform control system.
IEEE-ASME TRANSACTIONS ON MECHATRONICS
(2023)
Article
Engineering, Electrical & Electronic
Saeed H. Hanzaei, Mehdi Korki, Xian-Ming Zhang
Summary: This article proposes a distributed model-based cooperative controller to improve voltage synchronization in DC-isolated microgrids. The controller synchronizes not only the output voltages but also their derivatives by introducing a global performance index. The proposed controller ensures global system stability and simulations show its effectiveness in terms of voltage synchronization, stability, and voltage convergence speed when input voltage disturbances are present.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Wenshuo Song, Weihua Cao, Wenkai Hu, Min Wu
Summary: This study presents a cloud-edge collaboration framework for temperature regulation in continuous annealing processes, aiming to reduce energy consumption and increase efficiency by ensuring temperature control accuracy through cloud computing. It formulates a multiobjective optimization and proposes a framework for clustering operating conditions with high real-time requirements based on process analytics. Additionally, a recommendation mechanism for furnace temperatures with low real-time requirements is developed in the cloud. The effectiveness and practicality of the cloud-edge collaboration approach is demonstrated by its improvement in energy savings and control stability compared to traditional architectures.
JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS
(2023)
Article
Computer Science, Artificial Intelligence
Yuanfeng Huang, Sheng Du, Jie Hu, Witold Pedrycz, Min Wu
Summary: In this study, a condition recognition method with information granulation for burden distribution in a blast furnace was proposed. The method reduces the volume of the cooling stave temperature (CST) data and presents it in a granular form using information granulation. A novel fuzzy similarity calculation method is also presented to calculate the membership grades of information granules belonging to different standard granules. Experimental results show that the proposed method can effectively recognize the conditions in the blast furnace.
JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS
(2023)
Article
Automation & Control Systems
Luefeng Chen, Min Li, Min Wu, Witold Pedrycz, Kaoru Hirota
Summary: Convolutional feature-based broad learning with long short-term memory (CBLSTM) is proposed for recognizing multidimensional facial emotions. The CBLSTM model includes convolution and pooling layers, broad learning (BL), and long-and short-term memory network, aiming to obtain depth, width, and time scale information for facial emotion recognition. Experimental results show that CBLSTM outperforms CNN and other state-of-the-art methods in terms of recognition accuracy and computation time.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Automation & Control Systems
Manli Zhang, Chengda Lu, Shengnan Tian, Yibing Wang, Min Wu, Makoto Iwasaki
Summary: This article presents a generalized modified repetitive control structure that allows independent design of control and learning using a 2-D method. It uses a T-S fuzzy model and a parallel distribution compensation scheme to ensure the stability of a nonlinear repetitive-control system. Numerical simulations and experimental comparisons validate the effectiveness and superiority of the proposed method.
IEEE-ASME TRANSACTIONS ON MECHATRONICS
(2023)
Article
Automation & Control Systems
Jianqi An, Chenglin Yang, Min Wu, Sheng Du
Summary: This paper presents an intelligent control strategy for stabilizing the ignition temperature in the sintering process by recognizing different working conditions. Experimental results show that this method accurately controls the ignition temperature in a complex production environment.
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Jiafeng Xu, Xin Chen, Weihua Cao, Min Wu
Summary: This paper introduces a novel multi-objective optimization algorithm (BCCO) for trajectory planning in the drilling process. By employing a bi-directional constrained co-evolutionary mechanism and a series of optimization measures, BCCO is capable of effectively handling diverse constraints and demonstrates strong convergence and diversity in real-world engineering problems.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
