Article
Mathematics
Irina Bashkirtseva
Summary: This study investigates the synthesis of stochastic sensitivity for equilibrium modes in nonlinear randomly forced dynamical systems with incomplete information, proposing a feedback regulator that uses noisy data and deriving a quadratic matrix equation for attaining assigned stochastic sensitivity. The achievability of the required stochastic sensitivity is simplified to the solvability of this equation, and a constructive algorithm is suggested for its solution. These theoretical results are utilized to stabilize equilibrium modes of nonlinear stochastic oscillators under conditions of incomplete information, demonstrated through the example of a van der Pol oscillator.
Article
Mathematics, Applied
Yuan-Wei Lv, Guang-Hong Yang
Summary: This paper investigates the problem of state estimation for nonlinear dynamic systems in the presence of randomly occurring injection attacks. It proposes an adaptive cubature Kalman filter that does not require prior statistical information of the attacks. The effectiveness of the proposed filter is validated through numerical results.
APPLIED MATHEMATICS AND COMPUTATION
(2022)
Article
Automation & Control Systems
Qiang Zhang, Dakuo He
Summary: The main contribution of this article is the introduction of adaptive backstepping technique into nonlinear cyber-physical systems to counter randomly occurring false data injection (ROFDI) attacks. The development of a new defense strategy based on nonlinear disturbance observer (NDO) is also presented, which can estimate external compound disturbance effectively in the presence of the attack and improve the robustness of the controlled system. Different from FDI attacks, the proposed method deals with ROFDI attacks injected by attackers. The use of multiple Nussbaum functions overcomes the design difficulty of unknown control directions caused by ROFDI attacks. Furthermore, the approximation of the unknown nonlinear function and the exponential growth problem in the traditional backstepping calculation process are addressed using radial basis function neural network and dynamic surface control, respectively. Finally, a new adaptive neural control method based on NDO is proposed to make all signals bounded, while ensuring convergence of tracking errors and disturbance estimation errors to the neighborhood of zero. Numerical and practical examples further illustrate the rationality of the proposed method.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Engineering, Mechanical
Junyin Li, Yong Wang, Xiaoling Jin, Zhilong Huang, Isaac Elishakoff
Summary: This study combines data-driven identification methods with stochastic averaging to reduce the dimensionality of nonlinear randomly vibrating systems. By identifying slowly-varying processes using Koopman operator theory and deriving drift and diffusion coefficients from discrete data through sparse optimization, the proposed method overcomes the limitations of traditional approaches and demonstrates efficacy through numerical examples.
NONLINEAR DYNAMICS
(2021)
Article
Engineering, Multidisciplinary
Derui Ding, Huanyi Liu, Hongli Dong, Hongjian Liu
Summary: This paper investigates the problem of recursive resilient filtering for discrete-time nonlinear dynamical networks with randomly switching topologies under randomly occurring faults and hybrid cyber-attacks. A recursive resilient filter dependent on DoS attack sequences is designed to simultaneously estimate system states and occurred faults, and the maximum posterior probability estimation is calculated to identify DoS attack sequences with the help of the classical Bayesian rule.
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING
(2022)
Article
Mathematics, Applied
Boomipalagan Kaviarasan, Oh-Min Kwon, Myeong Jin Park, Rathinasamy Sakthivel
Summary: This paper presents a mode-dependent reduced-order filtering problem for semi-Markovian jump systems with time-varying delay and external disturbance, where the measurement output is susceptible to randomly occurring false data injection attacks. The attacks are described by a nonlinear function satisfying Lipschitz continuity and the possible attack scenarios are represented by a stochastic parameter following the Bernoulli distribution. By using Lyapunov-Krasovskii stability theory and stochastic analysis, a convex optimization problem is formulated, and the filter gain matrices are efficiently obtained to ensure the stochastic stability and strict (Q, S, R) - gamma-dissipativity of the augmented filtering system. Numerical examples demonstrate the advantages and effectiveness of the proposed theoretical findings.
APPLIED MATHEMATICS AND COMPUTATION
(2023)
Article
Automation & Control Systems
Zhen Liu, Xinkai Chen, Jinpeng Yu
Summary: This article investigates the security control issue for stochastic Markov jump cyber-physical systems against actuator failures, randomly occurring injection attacks, and inaccessible states using state estimator-based adaptive sliding mode control strategy. An estimator is used to generate the knowledge of states and establish a new switching surface. An adaptive sliding mode control is developed to ensure the attainability of the switching surface under stochastic noise, unknown injection attacks, and potential actuator failures. A new stochastically stable criterion for the target system is deduced based on the switching surface and stochastic stability theory. A simulation study is conducted to verify the proposed control scheme using a tunnel diode circuit model.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Automation & Control Systems
Haiqi Peng, Quanxin Zhu
Summary: This article focuses on the stochastic finite time stability (SFTS) and stochastic fixed time stability (SFIXTS) of switched stochastic nonlinear functional systems. It establishes several novel theorems on SFTS and SFIXTS using the Lyapunov-Razumikhin approach, stochastic analysis techniques, and stochastic process theory. The article also provides estimations of stochastic settling time, considering the effects of noise, time-delays, and switches.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2023)
Article
Engineering, Electrical & Electronic
Weihao Song, Zidong Wang, Jianan Wang, Fuad Alsaadi, Jiayuan Shan
Summary: This paper investigates the particle filtering problem for a class of nonlinear/non-Gaussian systems with energy harvesting sensors subject to randomly occurring sensor saturations. The study considers the characteristics of random occurrences of sensor saturations, the effects of energy levels, and measurement losses induced by insufficient energies, and derives an explicit expression of the likelihood function. Numerical simulation examples are provided to demonstrate the feasibility and effectiveness of the proposed particle filtering algorithm.
IEEE TRANSACTIONS ON SIGNAL PROCESSING
(2021)
Article
Mathematics
Zhimin Li, Chengming Lu, Hongyu Wang
Summary: This paper investigates the non-fragile output feedback tracking control problem for nonlinear networked systems with randomly occurring gain variations. The Takagi-Sugeno (T-S) fuzzy model is used to represent the considered systems. The objective is to design an observer-based non-fragile output feedback tracking controller that ensures mean-square asymptotic stability with the desired H-infinity tracking performance. Sufficient conditions for the existence of the dynamic quantizers and the observer-based non-fragile tracking controller are proposed using the descriptor representation strategy combined with the S-procedure.
Article
Automation & Control Systems
Hongpo Fu, Zhenwei Li, Wei Huang, Yongmei Cheng, Tianyi Zhang
Summary: This work investigates the problem of state estimation for a class of nonlinear systems subject to randomly occurring measurement anomalies without prior statistical information. A novel measurement model is proposed, where the anomalous measurements and anomaly probability are modeled as Gaussian mixture distribution and Beta distribution, respectively. The model does not require a priori statistical knowledge of anomalous measurements and achieves the same performance as the classical cubature Kalman filter in the absence of measurement anomalies through adaptive learning of the anomaly probability. Variational Bayesian inference is employed to approximate the joint posterior distribution of the system state and unknown parameters, resulting in a robust filter. Numerical simulations demonstrate the effectiveness of the proposed filter.
Article
Automation & Control Systems
Wei Qian, Simeng Guo, Yunji Zhao, Xiaozhuo Xu, Shumin Fei
Summary: This article addresses the distributed H-infinity resilient state estimation problem for a class of nonlinear systems with randomly occurring communication delays and missing measurements in sensor networks. A novel sensor model and Lyapunov-Krasovskii functional are proposed to ensure system stability and performance constraints. The effectiveness of the algorithm is illustrated through a numerical example.
INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING
(2022)
Article
Automation & Control Systems
Suhuan Zhang, Junfeng Zhang, Xiushan Cai, Shuo Li
Summary: This article discusses the event-triggered nonfragile reliable control of nonlinear positive semi-Markovian jump systems with randomly occurring faults. By introducing a stochastic copositive Lyapunov function integrated with linear programming, an event-triggered control strategy is used to address the stochastic stabilization of the systems, proposing nonfragile reliable controllers with additive and multiplicative perturbations. The approach establishes a more general fault description and suggests a more practical control design compared to existing results, validated through two examples.
OPTIMAL CONTROL APPLICATIONS & METHODS
(2021)
Article
Engineering, Electrical & Electronic
Dong-Hyoun Na, Ki-Hong Park, Young-Chai Ko, Mohamed-Slim Alouini
Summary: This paper investigates the outage probability and symbol error rate of users in SatCom downlink channels. By analyzing the distribution of user locations in single beam and multibeam areas, the asymptotic expressions of outage probability and symbol error rate under high power conditions are obtained. Numerical results confirm the accuracy of the analysis.
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS
(2022)
Article
Automation & Control Systems
Meng Zhao, Yugang Niu
Summary: This article investigates the finite-time sliding mode control problem for Markovian jump systems, where uncertainties and actuator faults are considered. The study introduces two independent exponentials distributed random variables to characterize the stochastic phenomenon and develops a suitable sliding mode controller for finite-time control performance. Simulation results demonstrate the feasibility of the proposed control strategy.
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
(2021)
Article
Automation & Control Systems
Fan Wang, Zidong Wang, Jinling Liang, Carlos Silvestre
Summary: This article addresses the recursive filtering problem of a two-dimensional system array with random sensor failures and dynamic quantizations. The occurrence of sensor failures is governed by a random variable with known statistical properties. To deal with data transmission over networks with limited bandwidth, a dynamic quantizer is utilized to compress raw measurements into quantized ones. The main objective of this article is to design a recursive filter that guarantees a locally minimal upper bound on the filtering error variance. To support the filter design, the states of the dynamic quantizer and the target plant are integrated into an augmented system, which enables the derivation of an upper bound on the filtering error variance and its subsequent minimization at each step. The expected filter gain is parameterized by solving coupled difference equations. Furthermore, the article discusses the monotonicity of the resulting minimum upper bound with respect to the quantization level and investigates its boundedness. Finally, the effectiveness of the developed filtering strategy is demonstrated through a simulation example.
Article
Engineering, Electrical & Electronic
Chuang Wang, Zidong Wang, Weibo Liu, Yuxuan Shen, Hongli Dong
Summary: This article proposes a two-stage deep offline-to-online transfer learning framework (DOTLF) for long-distance pipeline leakage detection (PLD). At the offline training stage, a feature transfer-based long short-term memory network with regularization information (TL-LSTM-Ri) is developed to extract domain-invariant features and early fault features. At the online detection stage, the trained TL-LSTM-Ri is used for motion prediction to monitor the pipeline's operating condition in real time. The DOTLF is successfully applied to real-time PLD on long-distance oil-gas pipeline data, and experimental results demonstrate its effectiveness.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Computer Science, Artificial Intelligence
Zhigang Liu, Xin Luo, Zidong Wang, Xiaohui Liu
Summary: This study proposes a Constraintinduced Symmetric Nonnegative Matrix Factorization (C-SNMF) model for community detection. Experimental results demonstrate that the proposed model significantly outperforms benchmarks and state-of-the-art models in achieving highly-accurate community detection results.
INFORMATION FUSION
(2023)
Article
Computer Science, Artificial Intelligence
Zhongyi Zhao, Zidong Wang, Lei Zou, Yun Chen, Weiguo Sheng
Summary: This paper studies the distributed fusion estimation problem for a class of nonlinear networked systems with unknown-but-bounded (UBB) noises. It proposes a zonotopes-based distributed fusion estimator by designing local estimators and fusion methods. The effectiveness of the proposed method is illustrated through a numerical example.
INFORMATION FUSION
(2023)
Article
Automation & Control Systems
Kaiqun Zhu, Zidong Wang, Qing-Long Han, Guoliang Wei
Summary: This article investigates the distributed set-membership fusion filtering problem for nonlinear 2-D shift-varying systems subject to unknown-but-bounded noises over sensor networks. It introduces a logarithmic-type encoding-decoding mechanism for each sensor node to enhance transmission security and relieve communication burden. A distributed set-membership filter is designed to determine the local ellipsoidal set that contains the system state. A new ellipsoid-based fusion rule is developed to form the fused ellipsoidal set with a globally smaller volume. Sufficient conditions are derived for the existence of the desired distributed set-membership filters and fusion weights.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Automation & Control Systems
Wei Chen, Zidong Wang, Derui Ding, Gheorghita Ghinea, Hongjian Liu
Summary: This article investigates the distributed formation-containment (FC) control problem for a class of discrete-time multiagent systems (DT-MASs) under the event-triggered communication mechanism. A novel dynamic event-triggered (DET) mechanism is developed to save communication cost and improve resource utilization. Based on available relative outputs, a distributed FC control scheme under the DET mechanism is proposed for all leaders and followers. The goal is to design an FC controller such that all leaders achieve formation shape and all followers converge into a convex hull. The considered DT-MASs are decoupled into a diagonal form using the Laplacian matrix property and inequality technique, and two sufficient conditions are established to ensure the desired FC performance. The FC controller parameters are obtained based on the solutions to two matrix inequalities depending on the maximum and minimum nonzero eigenvalues of the Laplacian matrix. An illustrative example is provided to verify the effectiveness of the developed control scheme.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Engineering, Multidisciplinary
Weihao Song, Zidong Wang, Zhongkui Li, Hongli Dong, Qing-Long Han
Summary: This paper investigates the particle filtering problem for a class of discrete-time nonlinear complex networks with stochastic perturbations under the scheduling of random access protocol. The stochastic perturbations include on-off stochastic coupling, non-Gaussian noises, and measurement censoring. A random access protocol is used to alleviate data collision over the networks, and two expressions of the modified likelihood function are established to weaken the adverse effects from measurement censoring. A protocol-based filter is designed in the auxiliary particle filtering framework to generate new particles and assign weights based on the derived likelihood function. The developed filtering scheme is demonstrated to be practicable and effective through a multi-target tracking application.
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING
(2023)
Article
Engineering, Multidisciplinary
Bogang Qu, Zidong Wang, Bo Shen, Hongli Dong, Hongjian Liu
Summary: This paper investigates the joint state and unknown input estimation (SUIE) problem for multi-machine power grids within energy networks under the event-triggered mechanism. It develops easy-to-implement algorithms to estimate the field voltage and mechanical torque of the synchronous generator (SG), which are generally difficult to be measured in engineering practice. An event-based transmission strategy is used to coordinate the massive PMU-based signal transmissions, and an event-based joint SUIE algorithm is designed to guarantee and minimize the estimation error covariances of both the unknown input and the state. Simulation experiments on the IEEE 39-bus system validate the developed estimation algorithm.
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING
(2023)
Article
Automation & Control Systems
Bogang Qu, Zidong Wang, Bo Shen, Hongli Dong
Summary: This article studies the problem of state estimation in a class of renewable-electricity-generation-based microgrids with measurement outliers. A state-space system model is proposed for microgrids using the physical laws of power systems, without considering prior knowledge of the measurement outliers. To enhance insensitivity against measurement outliers, an outlier-resistant SE algorithm is developed with two distinct features: adopting a saturation function to constrain the innovation term in the state estimator and minimizing the estimation error covariance by selecting proper gain parameters. Simulation studies on a benchmark islanded microgrid with two renewable-electricity-generation units are conducted to illustrate the validity of the developed algorithm.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Engineering, Electrical & Electronic
Junwan Zhu, Jingrui Duan, Zhigang Lu, Zhanliang Wang, Huarong Gong, Yubin Gong
Summary: To improve the performance of sine waveguide traveling wave tubes, a new elliptical beam-tunnel sine waveguide slow wave structure (EBTSW-SWS) is proposed. Compared to the traditional SW-SWS, the EBTSW-SWS has a wider passband and higher interaction impedance. Particle-in-cell simulation results show that the EBTSW-TWT can provide more than 70 W of output power with a maximum efficiency of 4.85% in the frequency range of 200-260 GHz.
JOURNAL OF ELECTROMAGNETIC WAVES AND APPLICATIONS
(2023)
Article
Automation & Control Systems
Hengli Cheng, Bo Shen, Jie Sun
Summary: In this paper, the distributed fusion filtering issue is investigated for multi-sensor systems with the constraints from both time-correlated fading channels and energy harvesters. A dynamic energy-allocated rule is proposed to properly deal with the energy supply relationship between a battery and multiple sensors. The local filter is designed to minimize the upper bound of the local filtering error covariance under the effects of the time-correlated fading channels and energy harvesters, and the fusion estimates are obtained using the covariance intersection approach.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2023)
Article
Chemistry, Analytical
Yang Dong, Shaomeng Wang, Jingyu Guo, Zhanliang Wang, Huarong Gong, Zhigang Lu, Zhaoyun Duan, Yubin Gong
Summary: This paper proposes an angular radial extended interaction amplifier (AREIA) that consists of a pair of angular extended interaction cavities, which show potential in improving the beam-wave interaction capability of W-band extended interaction klystrons (EIKs). Compared to conventional radial cavities, the angular cavities greatly reduce ohmic loss area and increase characteristic impedance. Particle-in-cell (PIC) results demonstrate the superiority of the proposed design in terms of output power and beam-wave interaction capacity compared to conventional EIAs under certain conditions.
Article
Chemistry, Multidisciplinary
Qinghua Tang, Demin Li, Yihong Zhang, Xuemin Chen
Summary: With the growing popularity of AEVs, optimizing path-planning and charging strategy is crucial. This paper proposes a joint push-pull communication mode to obtain real-time traffic conditions and charging infrastructure information. Dynamic optimization algorithms are used to minimize travel and charging costs.
APPLIED SCIENCES-BASEL
(2023)
Article
Chemistry, Analytical
Tian Xia, Xuemin Chen, Jiacun Wang, Feng Qiu
Summary: This study proposes a hybrid model that addresses the issue of informal writing in short texts by weighting new words. The model consists of an artificial neural network and a hidden Markov model, used for new word weighting and spam filtering, respectively, achieving fast and accurate short text filtering.
Article
Chemistry, Multidisciplinary
Chang Guo, Demin Li, Xuemin Chen
Summary: Analysis of traffic flow signals is important for traffic prediction and management. This paper proposes an improved wavelet transform to detect singular points in real-time traffic flow signals. A weighted similarity measurement of historical traffic flow signals is used to predict the next singular point, which determines the duration of prediction adaptively. The results show that the proposed algorithm outperforms existing approaches with high prediction accuracy and lower computing cost.
APPLIED SCIENCES-BASEL
(2023)
