Article
Computer Science, Artificial Intelligence
Chenyang Shi, Kachon Hoi, Seakweng Vong
Summary: This paper discusses the exponential stability of neural networks with time-varying delay. A novel integral inequality is derived by extending the generalized free-weighting-matrix integral inequality and using weighted orthogonal functions. The new inequality is then applied to investigate the exponential stability of time delay neural networks via an improved Lyapunov-Krasovskii functional, with numerical examples provided to demonstrate the advantages of the proposed criterion.
Article
Automation & Control Systems
Xu Li, Haibo Liu, Kuo Liu, Te Li, Yongqing Wang
Summary: The paper investigates the application of the Lyapunov-Krasovskii functional method in neural networks and proposes a generalized LKF method to derive new exponential stability criteria by weakening the strong condition. The effectiveness of the derived criteria is verified through two numerical examples.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2021)
Article
Computer Science, Artificial Intelligence
Yin Sheng, Tingwen Huang, Zhigang Zeng, Xiangshui Miao
Summary: This article investigates the Lagrange exponential stability and the Lyapunov exponential stability of memristive neural networks with discrete and distributed time-varying delays. The study uses inequality techniques, theories of the M-matrix, and the comparison strategy to consider the stability of the networks, providing less conservative methods for analyzing Lyapunov stability.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2021)
Article
Mathematics, Applied
Chen-Rui Wang, Yong He, Wen-Juan Lin
Summary: This paper introduces an improved augmented Lyapunov-Krasovskii functional to address the stability analysis issue of generalized neural networks with fast-varying delay. By utilizing specific methods and strategies based on the augmented LKF, a less conservative delay-dependent stability criterion is proposed. Numerical examples demonstrate the effectiveness of this criterion.
APPLIED MATHEMATICS AND COMPUTATION
(2021)
Article
Mathematics, Applied
Xu-Kang Chang, Yong He, Zhen-Man Gao
Summary: This article investigates the problem of global exponential stability of neural networks with a time-varying delay. By establishing an improved augmented delay-product-type Lyapunov-Krasovskii functional and utilizing the cross-term relationships and negative-determination lemma, a stability criterion is obtained.
APPLIED MATHEMATICS AND COMPUTATION
(2023)
Article
Automation & Control Systems
Hongjun Qiu, Fanchao Kong
Summary: This paper investigates the global exponential stability of a class of inertial Cohen-Grossberg neural networks with parameter uncertainties and time-varying delays. By constructing a modified delay-dependent Lyapunov-Krasovskii functional, simple algebraic inequalities are given to ensure the stability of the neural network model. The proposed model and results are more general and rigorous compared to existing methods.
INTERNATIONAL JOURNAL OF CONTROL
(2022)
Article
Computer Science, Information Systems
Hanni Xiao, Quanxin Zhu, Hamid Reza Karimi
Summary: This study focuses on the exponential stability analysis of switching stochastic delay neural networks with all unstable subsystems, and presents novel stability criteria. Compared to existing works, this study places more emphasis on all unstable subsystems, which is of greater research significance.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Yun Chen, Gang Chen
Summary: This paper focuses on the stability analysis of delayed neural networks, deriving necessary and sufficient conditions for positivity or negativity of high-order polynomials over a finite interval, constructing an appropriate Lyapunov-Krasovskii functional, and employing less conservative stability criteria. The proposed method's effectiveness and less conservativeness are demonstrated through three commonly used numerical examples.
Article
Computer Science, Information Systems
Li Wan, Qinghua Zhou
Summary: This paper addresses the exponential stability of a more general class of neutral-type Cohen-Grossberg neural networks, providing sufficient conditions to ensure the existence, uniqueness, and stability of the equilibrium point of the neural system. The conditions are easy to verify and guarantee global asymptotic stability, with two remarks indicating that they are less conservative than previous results. Two instructive examples are also given to demonstrate the effectiveness of the theoretical results and compare the stability conditions with previous findings.
Article
Mathematics, Applied
Zhongjie Zhang, Tingting Yu, Xian Zhang
Summary: This paper aims to establish global exponential stability criteria for multiple time-varying delay Cohen-Grossberg neural networks. By constructing novel Lyapunov-Krasovskii functionals, two algebraic criteria guaranteeing global exponential stability are given. Numerical examples are used to validate the effectiveness of the obtained criteria.
APPLIED MATHEMATICS AND COMPUTATION
(2022)
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
Mathematics
Molan Li, Da Li, Junxing Zhang, Xuanlu Xiang, Di Zhao
Summary: By discussing the dynamical properties of optimal cue integration with time-varying delay, we find that it is asymptotically stable and leads to a unique insect home direction. These results provide a theoretical basis for further research on insect homing behaviors and the establishment of autonomous robots that mimic insect navigation mechanisms in the future.
Article
Automation & Control Systems
Xiangyu Gao, Kok Lay Teo, Hongfu Yang, Shen Cong
Summary: This paper investigates the exponential stability problem of integral time-varying delay systems and proposes a novel exponential stability theorem, obtaining sufficient conditions through coupled linear matrix inequalities. These sufficient conditions cover previous results as special cases and cannot be directly obtained from earlier theorems due to the presence of time-varying delays.
INTERNATIONAL JOURNAL OF CONTROL
(2022)
Article
Computer Science, Artificial Intelligence
Chenyang Shi, Kachon Hoi, Seakweng Vong
Summary: This paper studies the stability of a neural network with time-varying delay. Two general inequalities with two and three terms are derived to estimate the derivative of the Lyapunov-Krasovskii functionals (LKFs). The LKF method is used to develop a stability criterion for time-delay neural networks by using these new inequalities. Numerical results are provided to demonstrate the improvement of the new criterion.
Article
Automation & Control Systems
Zhaoliang Sheng, Chong Lin, Bing Chen, Qing-Guo Wang
Summary: This paper introduces a new asymmetric Lyapunov-Krasovskii functional method for investigating the asymptotic stability of time-delay systems. Two delay-dependent asymptotic stability conditions in the form of linear matrix inequalities are provided. The advantages of the new approach are theoretically and numerically verified.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2021)
Article
Automation & Control Systems
Kamal Mammadov, Cheng-Chew Lim, Peng Shi
Summary: In this manuscript, we formulate the general Target-Attacker-Defender differential game in both continuous-time and discrete-time turn-based variants in n-dimensional Euclidean space. The objective of the Attackers is to get as close as possible to the Target before collision with the Defender, while the Target and Defender coordinate to achieve the opposite. We consider the most general setting for this zero-sum differential game, where the agents can move at different speeds, and prove the Nash equilibrium strategies in the discrete-time turn-based variant.
INTERNATIONAL JOURNAL OF CONTROL
(2022)
Article
Automation & Control Systems
Ting Shi, Peng Shi, Liping Zhang
Summary: This paper investigates the leader-following consensus problem for general linear multi-agent systems under external disturbances. The communication topologies are time-varying and switched from a finite set. A switched control system is introduced to model these topologies, and the weighted L-2 - L-infinity performance is analyzed. A topology-dependent controller is designed based on local information from the neighbors. Conditions are developed for the existence of a control protocol that achieves the leader-following consensus with a certain level of weighted L-2 - L-infinity performance. The design algorithm is formulated as a set of linear matrix inequalities (LMIs), and a numerical example is provided to demonstrate the effectiveness of the proposed consensus algorithm.
INTERNATIONAL JOURNAL OF CONTROL
(2022)
Article
Computer Science, Artificial Intelligence
Xin Yuan, Michael John Liebelt, Peng Shi, Braden J. Phillips
Summary: This paper focuses on developing agents with artificial general intelligence using a rule-based approach. The study demonstrates that association rules mining can be used to discover rules and determine relationships between data sets. The authors introduce a modified ARM method to discover rules for an agent-guided vehicle designed for autonomous parking, and validate the effectiveness of their approach through simulation testing.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2021)
Article
Automation & Control Systems
Renjie Ma, Peng Shi
Summary: This paper presents defense strategies based on switched counteraction principle to protect the secure state estimation (SSE) of Cyber-Physical Systems (CPSs) from sparse data injection (DI) attacks. The physical layer is modeled using a hybrid mechanism and malicious injections are excluded through adaptively switched counteraction searching. The proposed design methods are demonstrated to be effective and promising through numerical examples.
INTERNATIONAL JOURNAL OF CONTROL
(2022)
Article
Automation & Control Systems
Jirapun Pongfai, Chrissanthi Angeli, Peng Shi, Xiaojie Su, Wudhichai Assawinchaichote
Summary: An adaptive swarm learning process (SLP) algorithm is proposed for designing optimal PID parameters for a MIMO control system. The algorithm aims to improve performance and stability by applying swarm algorithm and learning process. Simulations confirm the algorithm's superiority in terms of performance and robustness compared to traditional SLP, NN, GA, PSO, and KIA algorithms.
INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS
(2021)
Article
Automation & Control Systems
Ge Song, Peng Shi, Ramesh K. Agarwal
Summary: This article addresses the problem of event-triggered-based fixed-time sliding mode cooperative control for leader-follower multiagent networks with bounded perturbation. A terminal integral sliding mode manifold with fast convergent speed is designed, and a distributed consensus tracking control strategy based on event-triggered and sliding mode control is developed to guarantee consensus within a fixed time. The proposed event-triggered control algorithm reduces the update frequency of control law and eliminates Zeno behavior, as demonstrated through simulation examples.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2021)
Article
Computer Science, Artificial Intelligence
Shiqi Zheng, Peng Shi, Shuoyu Wang, Yan Shi
Summary: This article studies the adaptive neural controller design for uncertain multiagent systems, utilizing neural networks to approximate unknown nonlinearities, constructing new Lyapunov functions to guarantee the conditions of NNs, and proposing new adaptive neural PI-type controllers. Illustrative examples demonstrate the advantages of the obtained results.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2021)
Article
Automation & Control Systems
Lin Zhao, Jinpeng Yu, Peng Shi
Summary: This paper investigates the problem of adaptive finite-time attitude containment control for multiple spacecrafts in spacecraft formation flying. A distributed control strategy combining finite-time command filtered backstepping and adaptive technique is proposed, which ensures containment errors of attitudes reaching the desired neighborhood in finite time. Simulation example demonstrates the effectiveness of the new designed technique.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2021)
Article
Automation & Control Systems
Junkang Ni, Peng Shi, Yu Zhao, Zhonghua Wu
Summary: This paper proposes a fixed-time distributed observer design method for high-order MAS under directed graph subject to packet dropout, and a novel fixed-time control strategy that can handle mismatched disturbances and overcome explosion of complexity and singularity problem.
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
(2021)
Article
Automation & Control Systems
Yulong Huang, Yonggang Zhang, Yuxin Zhao, Peng Shi, Jonathon A. Chambers
Summary: This article introduces a statistical similarity measure to quantify the similarity between random vectors, and uses it to develop a new outlier-robust Kalman filtering framework. The approximation errors and stability of the filter are analyzed, and iterative algorithms with convergent conditions are provided. The selection of similarity functions is considered, revealing the relations between the new method and existing outlier-robust Kalman filters. Simulation examples demonstrate the effectiveness and potential of the new filtering scheme.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2021)
Article
Computer Science, Artificial Intelligence
Qikun Shen, Peng Shi, Ramesh K. Agarwal, Yan Shi
Summary: This study examines the filter design problem of nonlinear systems and proposes a new adaptive neural network-based filter design method, which can effectively estimate system states and unknown time delays, overcoming the drawback of having unknown time delays in the designed filters. Simulation results demonstrate the effectiveness of the proposed new design method.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2021)
Article
Automation & Control Systems
Bin Xu, Xia Wang, Weisheng Chen, Peng Shi
Summary: This article explores a robust adaptive learning control strategy for uncertain single-input-single-output systems, employing dynamic surface control and sliding-mode control, with a focus on fast convergence and robust tracking performance through the combination of intelligent design and robust technique.
IEEE TRANSACTIONS ON CYBERNETICS
(2021)
Article
Engineering, Marine
Yuanjie Ren, Lanyong Zhang, Peng Shi, Ziqi Zhang
Summary: A hierarchical collaborative control energy management scheme is proposed for the propulsion system of hybrid electric ships. The scheme effectively solves the problems of steady-state oscillation and deviation from the tracking direction caused by volatility and uncertainty, achieving significant improvement.
JOURNAL OF MARINE SCIENCE AND ENGINEERING
(2022)
Article
Engineering, Electrical & Electronic
Huiyan Zhang, Hao Sun, Peng Shi, Luis Ismael Minchala
Summary: This article proposes a novel chip detection method that combines attentional feature fusion and cosine nonlocal attention to effectively handle chip images with multiple classes or complex backgrounds. Experimental results demonstrate that the proposed method outperforms the benchmark method on a medium-scale dataset.
Article
Remote Sensing
Yang Fei, Yuan Sun, Peng Shi
Summary: In this study, a hierarchical formation control strategy is used to address the robust formation control problem for a group of UAVs with system uncertainty. A sliding mode neural-based observer is constructed to estimate the nonlinear uncertainty in the UAV model, and sliding mode controllers and differentiators are designed to alleviate chattering in the control input. The proposed control scheme's effectiveness is validated through Lyapunov stability theory and numerical simulations on a multiple-UAV system.
