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Tao J, Xu M, Chen D, Xiao Z, Rao H, Xu Y. Event-Triggered Resilient Filtering With the Interval Type Uncertainty for Markov Jump Systems. IEEE TRANSACTIONS ON CYBERNETICS 2023; 53:7834-7843. [PMID: 37015602 DOI: 10.1109/tcyb.2022.3227446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
The problem of event-triggered resilient filtering for Markov jump systems is investigated in this article. The hidden Markov model is used to characterize asynchronous constraints between the filters and the systems. Gain uncertainties of the resilient filter are the interval type in this article, which is more accurate than the norm-bounded type to model the uncertain phenomenon. The number of linear matrix inequalities constraints can be decreased significantly by separating the vertices of the uncertain interval, so that the difficulty of calculation and calculation time can be reduced. Moreover, the event-triggered scheme is applied to depress the consumption of network resources. In order to find a balance between reducing bandwidth consumed and improving system performance, the threshold parameter is designed as a diagonal matrix in the event-triggered scheme. Utilizing the convex optimization method, the sufficient conditions are derived to guarantee that the filtering error systems are stochastically stable and satisfy the extended dissipation performance. Finally, a single-link robot arm system is delivered to certify the effectiveness and advantages of the proposed method.
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Sakthivel R, Kwon OM, Choi SG, Sakthivel R. Observer-based state estimation for discrete-time semi-Markovian jump neural networks with round-robin protocol against cyber attacks. Neural Netw 2023; 165:611-624. [PMID: 37364471 DOI: 10.1016/j.neunet.2023.05.046] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 04/27/2023] [Accepted: 05/23/2023] [Indexed: 06/28/2023]
Abstract
This paper investigates an observer-based state estimation issue for discrete-time semi-Markovian jump neural networks with Round-Robin protocol and cyber attacks. In order to avoid the network congestion and save the communication resources, the Round-Robin protocol is used to schedule the data transmissions over the networks. Specifically, the cyber attacks are modeled as a set of random variables satisfying the Bernoulli distribution. On the basis of the Lyapunov functional and the discrete Wirtinger-based inequality technique, some sufficient conditions are established to guarantee the dissipativity performance and mean square exponential stability of the argument system. In order to compute the estimator gain parameters, a linear matrix inequality approach is utilized. Finally, two illustrative examples are provided to demonstrate the effectiveness of the proposed state estimation algorithm.
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Affiliation(s)
- Ramalingam Sakthivel
- School of Electrical Engineering, Chungbuk National University, Cheongju 28644, South Korea
| | - Oh-Min Kwon
- School of Electrical Engineering, Chungbuk National University, Cheongju 28644, South Korea.
| | - Seong-Gon Choi
- School of Information and Communication Engineering, Chungbuk National University, Cheongju 28644, South Korea
| | - Rathinasamy Sakthivel
- Department of Applied Mathematics, Bharathiar University, Coimbatore 641046, India; Department of Mathematics, Sungkyunkwan University, Suwon 440746, South Korea.
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Luo Y, Wang Z, Sheng W, Yue D. State Estimation for Discrete Time-Delayed Impulsive Neural Networks Under Communication Constraints: A Delay-Range-Dependent Approach. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023; 34:1489-1501. [PMID: 34460395 DOI: 10.1109/tnnls.2021.3105449] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
In this article, a delay-range-dependent approach is put forward to tackle the state estimation problem for delayed impulsive neural networks. A new type of nonlinear function, which is more general than the normal sigmoid function and functions constrained by the Lipschitz condition, is adopted as the neuron activation function. To effectively alleviate data collisions and save energy, the round-robin protocol is utilized to mitigate the occurrence of unnecessary network congestion in communication channels from sensors to the estimator. With the aid of the Lyapunov stability theory, a state observer is constructed such that the estimation error dynamics are asymptotically stable. The observer existence is ensured by resorting to a set of delay-range-dependent criteria which is dependent on both the impulsive time instant and a coefficient matrix. In addition, the synthesis of the observer is discussed by using linear matrix inequalities. Simulations are provided to illustrate the reasonability of our delay-range-dependent estimation approach.
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Chen Y, Zhang N, Yang J. A survey of recent advances on stability analysis, state estimation and synchronization control for neural networks. Neurocomputing 2023. [DOI: 10.1016/j.neucom.2022.10.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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Zou L, Wang Z, Dong H, Han QL. Energy-to-Peak State Estimation With Intermittent Measurement Outliers: The Single-Output Case. IEEE TRANSACTIONS ON CYBERNETICS 2022; 52:11504-11515. [PMID: 33750719 DOI: 10.1109/tcyb.2021.3057545] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
This article is concerned with the energy-to-peak state estimation problem for a class of linear discrete-time systems with energy-bounded noises and intermittent measurement outliers (IMOs). In order to capture the intermittent nature, two sequences of step functions are introduced to model the occurrence of the IMOs. Furthermore, two special indices (i.e., minimum and maximum interval lengths) are adopted to describe the "occurrence frequency" of IMOs. Different from the considered energy-bounded noises, the outliers are assumed to have their magnitudes larger than certain thresholds. In order to achieve a satisfactory performance constraint on the energy-to-peak state estimation under the addressed kind of measurement outliers, a novel parameter-dependent (PD) state estimation strategy is developed to guarantee that the measurements contaminated by outliers would be removed in the estimation process. The proposed PD state estimation method is essentially a two-step process, where the first step is to examine the appearing and disappearing moments for each IMO by using a dedicatedly constructed outlier detection scheme, and the second step is to implement the state estimation task according to the outlier detection results. Sufficient conditions are obtained to ensure the existence of the desired estimator, and the gain matrix of the desired estimator is then derived by solving a constrained optimization problem. Finally, a simulation example is presented to illustrate the effectiveness of our developed PD state estimation strategy.
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Li B, Wang Z, Han QL, Liu H. Distributed Quasiconsensus Control for Stochastic Multiagent Systems Under Round-Robin Protocol and Uniform Quantization. IEEE TRANSACTIONS ON CYBERNETICS 2022; 52:6721-6732. [PMID: 33079691 DOI: 10.1109/tcyb.2020.3026001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
In this article, the problem of consensus control is investigated for a class of multiagent systems (MASs) with both stochastic noises and nonidentical exogenous disturbances. The signal transmission among agents is implemented through a digital communication network subject to both uniform quantization and round-robin protocol as a reflection of network constraints. The consensus strategy is designed by adopting the estimates of the relative states of the agent to its neighbors, which renders the distributed nature of the controller. A new consensus concept, namely, quasiconsensus in probability, is employed to evaluate the state response of the agents to the stochastic noises, the exogenous disturbances, and the quantization error. An augmented system is first formed that relies on the deviations of the individual state from the average state, the observer error of the relative state, as well as the relative measurement output. Based on the augmented model, an analysis approach on dynamical behaviors is developed to facilitate the consensus analysis of MASs by means of the switching Lyapunov function technique and the stochastic analysis methods. Then, the existence condition and the explicit expression of the time-varying gain matrices are proposed for the expected controller by resorting to the feasibility of several matrix inequalities. Numerical simulation results are presented to demonstrate the applicability of the theoretical results.
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Shen Y, Wang Z, Shen B, Han QL. Recursive State Estimation for Networked Multirate Multisensor Systems With Distributed Time-Delays Under Round-Robin Protocol. IEEE TRANSACTIONS ON CYBERNETICS 2022; 52:4136-4146. [PMID: 33001817 DOI: 10.1109/tcyb.2020.3021350] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
This article is concerned with the problem of recursive state estimation for a class of multirate multisensor systems with distributed time delays under the round-robin (R-R) protocol. The state updating period of the system and the sampling period of the sensors are allowed to be different so as to reflect the engineering practice. An iterative method is presented to transform the multirate system into a single-rate one, thereby facilitating the system analysis. The R-R protocol is introduced to determine the transmission sequence of sensors with the aim to alleviate undesirable data collisions. Under the R-R protocol scheduling, only one sensor can get access to transmit its measurement at each sampling time instant. The main purpose of this article is to develop a recursive state estimation scheme such that an upper bound on the estimation error covariance is guaranteed and then locally minimized through adequately designing the estimator parameter. Finally, simulation examples are provided to show the effectiveness of the proposed estimator design scheme.
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Liu H, Wang Z, Fei W, Li J. Resilient H∞ State Estimation for Discrete-Time Stochastic Delayed Memristive Neural Networks: A Dynamic Event-Triggered Mechanism. IEEE TRANSACTIONS ON CYBERNETICS 2022; 52:3333-3341. [PMID: 33001819 DOI: 10.1109/tcyb.2020.3021556] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
In this article, a resilient H∞ approach is put forward to deal with the state estimation problem for a type of discrete-time delayed memristive neural networks (MNNs) subject to stochastic disturbances (SDs) and dynamic event-triggered mechanism (ETM). The dynamic ETM is utilized to mitigate unnecessary resource consumption occurring in the sensor-to-estimator communication channel. To guarantee resilience against possible realization errors, the estimator gain is permitted to undergo some norm-bounded parameter drifts. For the delayed MNNs, our aim is to devise an event-based resilient H∞ estimator that not only resists gain variations and SDs but also ensures the exponential mean-square stability of the resulting estimation error system with a guaranteed disturbance attenuation level. By resorting to the stochastic analysis technique, sufficient conditions are acquired for the expected estimator and, subsequently, estimator gains are obtained via figuring out a convex optimization problem. The validity of the H∞ estimator is finally shown via a numerical example.
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Zhang X, Wang Y, Wang X. A direct parameterized approach to global exponential stability of neutral-type Cohen–Grossberg neural networks with multiple discrete and neutral delays. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2021.08.068] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Yang H, Wang Z, Shen Y, Alsaadi FE, Alsaadi FE. Event-triggered state estimation for Markovian jumping neural networks: On mode-dependent delays and uncertain transition probabilities. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2020.10.050] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Qian W, Li Y, Zhao Y, Chen Y. New optimal method for L2- L∞ state estimation of delayed neural networks. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2020.06.118] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Liu H, Wang Z, Fei W, Li J. H ∞ and l 2-l ∞ state estimation for delayed memristive neural networks on finite horizon: The Round-Robin protocol. Neural Netw 2020; 132:121-130. [PMID: 32871337 DOI: 10.1016/j.neunet.2020.08.006] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Revised: 07/19/2020] [Accepted: 08/10/2020] [Indexed: 11/26/2022]
Abstract
In this paper, a protocol-based finite-horizon H∞ and l2-l∞ estimation approach is put forward to solve the state estimation problem for discrete-time memristive neural networks (MNNs) subject to time-varying delays and energy-bounded disturbances. The Round-Robin protocol is utilized to mitigate unnecessary network congestion occurring in the sensor-to-estimator communication channel. For the delayed MNNs, our aim is to devise an estimator that not only ensures a prescribed disturbance attenuation level over a finite time-horizon, but also keeps the peak value of the estimation error within a given range. By resorting to the Lyapunov-Krasovskii functional method, the delay-dependent criteria are formulated that guarantee the existence of the desired estimator. Subsequently, the estimator gains are obtained via figuring out a bank of convex optimization problems. The validity of our estimator is finally shown via a numerical example.
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Affiliation(s)
- Hongjian Liu
- Key Laboratory of Advanced Perception and Intelligent Control of High-end Equipment, Ministry of Education, Anhui Polytechnic University, Wuhu 241000, China; Artificial Intelligence Energy Research Institute, Northeast Petroleum University, Daqing 163318, China.
| | - Zidong Wang
- College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, China; Department of Computer Science, Brunel University London, Uxbridge, Middlesex, UB8 3PH, United Kingdom.
| | - Weiyin Fei
- Key Laboratory of Advanced Perception and Intelligent Control of High-end Equipment, Ministry of Education, Anhui Polytechnic University, Wuhu 241000, China; School of Mathematics and Physics, Anhui Polytechnic University, Wuhu 241000, China.
| | - Jiahui Li
- Artificial Intelligence Energy Research Institute, Northeast Petroleum University, Daqing 163318, China; Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control, Northeast Petroleum University, Daqing 163318, China.
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