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Iterative learning based convergence analysis for nonlinear impulsive differential inclusion systems with randomly varying trial lengths Int. J. Adapt. Control Signal Process. (IF 3.1) Pub Date : 2024-03-17 Wanzheng Qiu, JinRong Wang, Dong Shen
This paper studies the finite-time tracking problem for nonlinear impulsive differential inclusion systems with randomly varying trial lengths. First, we convert the set-valued mapping in the differential inclusion systems to single-valued mapping by a Steiner-type selector. For the tracking problem of random discontinuous output trajectories, this paper defines a piecewise continuous variable by zero-order
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Cluster synchronization of stochastic two‐layer networks with infinite distributed delays via delayed pinning impulsive control Int. J. Adapt. Control Signal Process. (IF 3.1) Pub Date : 2024-03-16 Chuan Zhang, Junchao Wei, Fei Wang, Yi Liang
SummaryThe cluster synchronization problem of stochastic two‐layer networks with infinite distributed delays is concerned. Firstly, we study the cluster synchronization of the first layer (leader‐layer) network with the average state of each cluster of sets as synchronization target. Secondly, we design a delayed pinning impulsive controller to synchronize the second layer (follower‐layer) network
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Adaptive finite-time optimal time-varying formation control for second-order stochastic nonlinear multiagent systems Int. J. Adapt. Control Signal Process. (IF 3.1) Pub Date : 2024-03-13 Jiaxin Zhang, Yue Fu, Jun Fu
This work addresses a fuzzy-based finite-time optimal time-varying formation (TVF) control issue for a class of second-order stochastic multi-agent systems (SMASs) with unknown nonlinearities. First, novel optimal cost functions with exponential power terms are constructed, which enables the SMASs to achieve finite-time stability in the mean square sense with minimum cost. Then, based on the cost functions
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Robust adaptive control for a class of autonomous vehicle platoons Int. J. Adapt. Control Signal Process. (IF 3.1) Pub Date : 2024-03-12 Tianqun Ren, Xiang Chen, Guoxiang Gu
SummaryThis article studies robust adaptive control for a class of autonomous vehicle platoons. In particular, two innovative adaptive control laws are proposed to address both position and velocity tracking for a vehicle platoon. In addition, it is shown that robust asymptotic string stability can be delivered by the underlying adaptive control laws for the vehicle platoon, in the sense that these
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Interval observer design for nonlinear discrete‐time dynamic systems Int. J. Adapt. Control Signal Process. (IF 3.1) Pub Date : 2024-03-12 Alexey Zhirabok, Alexander Zuev, Vladimir Filaretov
SummaryThe problem of interval observer design for systems described by nonlinear models with uncertainties is considered. The problem is solved based on special mathematical technique (the algebra of functions) which allows obtaining solution for systems containing non‐smooth nonlinearities. The relations to design interval observer insensitive or having minimal sensitivity to the disturbance and
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Intrusion detection system based on the beetle swarm optimization and K‐RMS clustering algorithm Int. J. Adapt. Control Signal Process. (IF 3.1) Pub Date : 2024-03-12 S. Gokul Pran, Sivakami Raja, S. Jeyasudha
SummaryIntrusion detection is a cyber‐security method that is significant for network security. It is utilized to detect behaviors that compromise security and privacy within a network or in the context of a computer system. To enhance the identification, an Intrusion Detection System Based on the Beetle Swarm Optimization and K‐RMS Clustering Algorithm cluster‐based hybrid classifiers is proposed
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Practical finite‐time fuzzy control of uncertain nonstrict‐feedback systems with actuator saturation and output constraints Int. J. Adapt. Control Signal Process. (IF 3.1) Pub Date : 2024-03-11 Mohammed Haddad, Abdesselem Boulkroune, Hongyi Li
SummaryThis article investigates the devise issue of a practical finite‐time output‐tracking control for uncertain nonlinear nonstrict‐feedback systems subject simultaneously to time‐varying output constraints (TVOC), actuator saturation, and bounded unmatched disturbances. An adaptive fuzzy approximator‐based control system is devised using the dynamic surface control (DSC) concept. System output
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Supervisory adaptive control revisited: Linear‐like convolution bounds and tolerance of slow time‐variations Int. J. Adapt. Control Signal Process. (IF 3.1) Pub Date : 2024-03-11 Craig J. Lalumiere, Daniel E. Miller
SummarySupervisory control has been shown to be a very effective approach to adaptive control which ensures step‐tracking, exponential stability, and a degree of robustness to unmodeled dynamics. Here we apply the technique to the classical ‐step‐ahead adaptive control problem: we not only prove exponential stability and tracking of a general bounded reference signal, but also a never‐before‐seen linear‐like
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Unified finite‐time fault estimation and fault‐tolerant control for Takagi–Sugeno fuzzy singular systems Int. J. Adapt. Control Signal Process. (IF 3.1) Pub Date : 2024-03-11 N. Keerthana, R. Sakthivel, N. Aravinth, S. Marshal Anthoni
SummaryWith the aid of a proportional integral framework, the presented article focuses on the problems of finite‐time boundedness and fault estimation for Takagi–Sugeno fuzzy singular systems subject to time delays, faults and external disturbances. To commence, we conjure up a fuzzy‐dependent intermediate variable and from thereon, a proportional integral‐based fuzzy intermediate estimator is constructed
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Observer‐based hybrid triggered control design for cluster synchronization of nonlinear complex dynamical networks Int. J. Adapt. Control Signal Process. (IF 3.1) Pub Date : 2024-03-08 N. Birundha Devi, R. Sakthivel, M. Vijayakumar, A. Mohammadzadeh
SummaryThe issues of cluster synchronization and state estimation for a class of nonlinear complex dynamical networks are concurrently focused in this study. In precise, led by the design of complex dynamical networks, a proportional integral‐based observer is implemented for achieving robustness, by the virtue of which the states of examined networks are estimated. Thereupon, a proportional integral
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Bipartite containment control of nonlinear multi-agent systems with unknown inputs and state constraints Int. J. Adapt. Control Signal Process. (IF 3.1) Pub Date : 2024-03-05 Younan Zhao, Fanglai Zhu
This article addresses the issue of bipartite containment tracking for nonlinear strict feedback multi-agent systems (MASs) with disturbances and measurement uncertainties. To provides a better performance against strong disturbance, a coordination transformation is applied such that all the nonlinear functions, controller disturbances and measurement uncertainties are lumped into a single disturbance
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Robust attitude tracking control for a variable-pitch quadrotor with uncertainties Int. J. Adapt. Control Signal Process. (IF 3.1) Pub Date : 2024-03-04 Bo Zhao, Dong Yue, Yang Yang
Variable-pitch quadrotors have demonstrated their capability to enhance control performance and overcome limitations compared to conventional fixed-pitch quadrotors, while still facing challenges such as nonlinearity, couplings, uncertainties and so on. This article aims to address the robust attitude control problem of variable-pitch quadrotors afflicted with parametric uncertainties and unpredictable
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Adaptive cluster consensus control of nonlinear multi‐agent system via the dynamic event‐triggered strategy Int. J. Adapt. Control Signal Process. (IF 3.1) Pub Date : 2024-03-05 Yaping Xia, Ruiyu Li, Renfei Liu, Hang Wan, Haiyan Zhao
SummaryIn this article, the cluster consensus problems of two classes of nonlinear multi‐agent systems (NMASs) are studied under the dynamic event‐triggered control (ETC) strategy, where cooperative‐competitive interactions among agents and high‐order dynamics are considered. First, two dynamic ETC protocols are developed to realize the cluster synchronization of NMASs. Different from the static ETC
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Fixed‐time distributed adaptive optimization for third‐order nonlinear fully heterogeneous vehicular platoon systems Int. J. Adapt. Control Signal Process. (IF 3.1) Pub Date : 2024-03-05 Jiayi Lei, Yuan‐Xin Li
SummaryIn this article, the problem of fixed‐time distributed optimization is researched for third‐order fully heterogeneous nonlinear connected and autonomous vehicles. To address this problem, a fixed‐time distributed optimization algorithm is proposed via a two‐step strategy. First, an optimization algorithm is proposed to generate a virtual reference signal that can converge to the optimal solution
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DDoS attack detection in cloud using ensemble model tuned with optimal hyperparameter Int. J. Adapt. Control Signal Process. (IF 3.1) Pub Date : 2024-03-02 K. Balachandra Reddy, S. Meera
SummaryDDoS attacks are a type of cloud incursion that lessen service degradation. DDoS attacks target the cloud network with invalid requests, rejecting legitimate requests. Such attacks disrupt the entire cloud architecture, thus it needs efficient detection methods to spot their presence. This study proposes a novel ensemble classification model for DDoS incursion detection. Pre‐processing, feature
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Dynamic triggered‐based tracking approach for connected vehicle system via extended observer Int. J. Adapt. Control Signal Process. (IF 3.1) Pub Date : 2024-02-28 Zheyu Tan, Meng Li, Yong Chen
SummaryThis paper investigates the tracking control problem of connected vehicle system suffer from disturbance and communication load. A new dynamic triggered‐based tracking approach was presented. First, an extended state observer is designed to estimate the equivalent disturbance, and the convergence of observed error is proved. Then, a tracking approach based on backstepping control is proposed
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Online reinforcement learning control via discontinuous gradient Int. J. Adapt. Control Signal Process. (IF 3.1) Pub Date : 2024-02-28 Carlos A. Arellano‐Muro, Bernardino Castillo‐Toledo, Stefano Di Gennaro, Alexander G. Loukianov
SummaryThis work proposes a reinforcement learning control scheme for systems affected by persistent external perturbations. This scheme relies on and high‐order sliding mode control techniques combined to estimate the parameters with a certain degree of precision and simultaneously attenuate persistent and state‐dependent perturbations. The proposed solution is a novel design technique based on the
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Sliding mode control for Markovian jump systems under a switched scheduling protocol Int. J. Adapt. Control Signal Process. (IF 3.1) Pub Date : 2024-02-28 Ying Zhang, Bei Chen
SummaryThis article is concerned with the sliding mode control for Markovian jump systems under transmission constraints. To deal with node congestions, a switched channel scheduling scheme integrating round‐robin (RR) protocol and weight try‐once‐discard (WTOD) protocol is proposed to orchestrate the priority of multiple sensor nodes. At each transmission instant, a system performance‐based detector
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Multi‐model predictive control of converter inlet temperature in the process of acid production with flue gas Int. J. Adapt. Control Signal Process. (IF 3.1) Pub Date : 2024-02-27 Minghua Liu, Xiaoli Li, Kang Wang, Zhiqiang Liu, Guihai Li
SummaryThe smelting of non‐ferrous metals produces substantial quantities of sulfur dioxide (SO)‐laden flue gas, which is seriously harmful to environment and humans. To improve the conversion ratio of SO and minimize environmental pollution, controlling converter inlet temperature during acid production has proven to be an efficient approach. However, unsteadiness of smelting procedure leads to frequent
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Robust and quantized repetitive tracking control for fractional‐order fuzzy large‐scale systems Int. J. Adapt. Control Signal Process. (IF 3.1) Pub Date : 2024-02-24 V. Tharanidharan, T. Saravanakumar, S. Marshal Anthoni
SummaryIn this article, the decentralized repetitive tracking controller design for fractional‐order large‐scale Takagi–Sugeno fuzzy system with time delays is developed. We mainly focus on the design of a decentralized repetitive tracking controller based on the Lyapunov stability theory, by which the addressed large‐scale system asymptotically stabilized with performance index. Further, the repetitive
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A distributed randomized method for the identification of switched ARX systems Int. J. Adapt. Control Signal Process. (IF 3.1) Pub Date : 2024-02-23 Miao Yu, Federico Bianchi, Luigi Piroddi
SummaryThe identification of switched systems amounts to a mixed integer nonlinear optimization problem, where the continuous variables are associated to the model parameterizations of the different modes, and the discrete ones are related to the switching signal (each data sample is assigned to a mode, and switching occurs when the mode assignment changes over time). In the batch form of the identification
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Adaptive fast finite‐time control for nonlinear systems subject to output hysteresis by fuzzy approach Int. J. Adapt. Control Signal Process. (IF 3.1) Pub Date : 2024-02-23 Zheng Li, Xueyi Li, Fang Wang
SummaryThis article investigates an adaptive fast finite‐time tracking control problem for a class of uncertain nonlinear systems with output hysteresis. The idea of output hysteresis compensation is skillfully extended to adaptive design by employing a hysteresis inverse transformation and barrier Lyapunov function. The backstepping technique is adopted to establish the fast finite‐time control strategy
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Dual cerebella model neural networks based robust adaptive output feedback control for electromechanical actuator with anti‐parameter perturbation and anti‐disturbance Int. J. Adapt. Control Signal Process. (IF 3.1) Pub Date : 2024-02-21 Jian Hu, Mengmeng Cao, Yanchun Bai, Qiuyu Song, Jianyong Yao
SummaryTo realize a high‐accuracy tracking control of an electromechanical actuator in which only position signal is available, a new robust adaptive output feedback control strategy based on dual CMAC neural networks is proposed in this article. A high‐gain observer and a neural network are combined to estimate the unmeasured system states, in which a neural network is designed to estimate and compensate
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Application of terminal region enlargement approach for discrete time quasi infinite horizon nonlinear model predictive control Int. J. Adapt. Control Signal Process. (IF 3.1) Pub Date : 2024-02-17 Sowmya Gupta, Chinmay Rajhans
Ensuring nominal asymptotic stability of the nonlinear model predictive control (NMPC) controller is not trivial. Stabilizing ingredients such as terminal penalty term and Terminal Region (TR) are crucial in establishing the asymptotic stability. Approaches available in the literature provide limited degrees of freedom for the characterization of the TR for the discrete time quasi infinite horizon
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An event-triggered method to distributed filtering for nonlinear multi-rate systems with random transmission delays Int. J. Adapt. Control Signal Process. (IF 3.1) Pub Date : 2024-02-15 Zehao Li, Jun Hu, Cai Chen, Hui Yu, Xiaojian Yi
In this article, an event-triggered recursive filtering problem is studied for a class of nonlinear multi-rate systems (MRSs) with random transmission delays (RTDs). The RTDs are described by utilizing random variables with a known probability distribution and the Kronecker δ$$ \delta $$ function. To facilitate further study, the MRS is converted into a single-rate one by adopting an iteration equation
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A general update rule for Lyapunov-based adaptive control of mobile robots with wheel slip Int. J. Adapt. Control Signal Process. (IF 3.1) Pub Date : 2024-02-15 T. B. Burghi, J. G. Iossaqui, J. F. Camino
In this article, we introduce a novel family of Lyapunov-based adaptive kinematic control laws developed to solve the trajectory tracking problem for a differential-drive mobile robot under the influence of both longitudinal and lateral wheel slip. Each adaptive controller in this family is constructed by augmenting a nonadaptive nominal controller, originally designed for the slip-free case, with
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Neural network‐based output‐feedback adaptive control for a class of uncertain strict feedback fractional‐order nonlinear systems subject to input saturation Int. J. Adapt. Control Signal Process. (IF 3.1) Pub Date : 2024-02-20 Zoubir Rabahi, Islem Daoudi, Mohamed Chemachema
SummaryThis paper presents neural networks (NNs) adaptive controller for an uncertain fractional‐order nonlinear system in strict‐feedback form, subject to input saturation, unavailable states for measurement, and external disturbances. The fractional‐order adaptive laws are derived based only on the output tracking error thanks to the implementation of the strictly positive real (SPR) property, differently
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A hybrid approach for PV based grid tied intelligent controlled water pump system Int. J. Adapt. Control Signal Process. (IF 3.1) Pub Date : 2024-02-07 S. Selvakumaran, K. Baskaran
This article proposes a hybrid POA-RBFNN approach for PV (photovoltaic) based grid tied intelligent controlled water pump system. The proposed method is the hybrid wrapper of Pelican Optimization Algorithm (POA) and Radial Basis Function Neural Network (RBFNN) and later it is termed as POA-RBFNN method. On the basis of bidirectional power grids, this research presents bidirectional power flow control
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A new Q-function structure for model-free adaptive optimal tracking control with asymmetric constrained inputs Int. J. Adapt. Control Signal Process. (IF 3.1) Pub Date : 2024-02-06 Mingming Zhao, Ding Wang, Menghua Li, Ning Gao, Junfei Qiao
This article aims to design a model-free adaptive tracking controller for discrete-time nonlinear systems with unknown dynamics and asymmetric control constraints. First, a new Q-function structure is designed by introducing the control input into the tracking error of the next moment, in order to eliminate the final tracking error, avoid the steady control, and ignore the discount factor. Second,
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Finite-dimensional, output-predictor-based, adaptive observer for heat PDEs with sensor delay Int. J. Adapt. Control Signal Process. (IF 3.1) Pub Date : 2024-02-05 H. Rafia, A. Benabdelhadi, F. Giri, H. Ouadi, F. Z. Chaoui
We are considering the problem of designing observers for heat partial differential equations (PDEs) that are subject to sensor delay and parameter uncertainty. In order to get finite-dimensional observers, described by ordinary differential equations (ODE), we develop a design method based on the modal decomposition approach. The approach is extended so that both parameter uncertainty and sensor delay
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Q-learning based adaptive Kalman filtering for partial model-free dynamic systems Int. J. Adapt. Control Signal Process. (IF 3.1) Pub Date : 2024-02-04 Kun Tang, Xiaoli Luan, Feng Ding, Fei Liu
In this article, we propose an adaptive Kalman filtering based on Q-learning for partial model-free dynamic systems. First, a cost function is defined to iteratively update the prior state value when the model parameters are unknown. Then, the observations in a period of time are utilized to improve the accuracy and updating speed of the prior state estimation by means of the multi-innovation least
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Decentralized event-triggered output feedback adaptive neural network control for a class of MIMO uncertain strict-feedback nonlinear systems with input saturation Int. J. Adapt. Control Signal Process. (IF 3.1) Pub Date : 2024-01-30 Oussama Bey, Mohamed Chemachema
For a class of multiple-input multiple-output large-scale nonlinear systems in strict-feedback form with input saturation, external disturbances and immeasurable states, an adaptive decentralized neural network (NN) control strategy on the basis of event triggered mechanism is investigated in this article. In contrast to the literature, the proposed method is centered on the control-error as a replacement
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Event-triggered model reference adaptive control system design for SISO plants using meta-learning-based physics-informed neural networks without labeled data and transfer learning Int. J. Adapt. Control Signal Process. (IF 3.1) Pub Date : 2024-01-28 Worrawat Duanyai, Weon Keun Song, Poom Konghuayrob, Manukid Parnichkun
This paper examines the controllability of a novel Lyapunov-based model reference adaptive control (MRAC) system designed with a meta-learning-based physics-informed neural network (MLPINN) for linear and nonlinear single-input and single-output (SISO) plants without labeled data (MLPINN-MRAC system). It is devised with the benefits of several techniques: the integration of the identification process
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Distributed joint parameter and state estimation algorithm for large-scale interconnected systems Int. J. Adapt. Control Signal Process. (IF 3.1) Pub Date : 2024-01-28 Mounira Hamdi, Samira Kamoun, Lhassane Idoumghar, Mondher Chaoui, Abdenaceur Kachouri
This paper proposes a distributed joint parameter and state variables estimation algorithm for large-scale state-space interconnected systems. In this distributed estimation scheme, each interconnected sub-system is described by a linear discrete-time state space mathematical model. Each sub-system is supposed to be controlled by an intelligent controller that can communicate with its interconnected
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An adaptive trajectory tracking control scheme for a novel fully actuated multirotor unmanned aerial vehicle without using velocity signals Int. J. Adapt. Control Signal Process. (IF 3.1) Pub Date : 2024-01-22 Panfeng Shu, Xiaomin Xie, Feng Li, Lijuan Jia, Wenjia Xu, Masahiro Oya
This paper introduces a novel fully actuated multirotor unmanned aerial vehicle (UAV) and develops an adaptive control scheme for it without relying on velocity signals. Conventional multirotors are limited by their strong coupling between translation and rotation motion, which makes it difficult to operate one motion without affecting the other. However, this novel multirotor's fully actuated design
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Event-triggered adaptive output feedback regulation of a chain of integrators with an unknown time-varying delay in the input Int. J. Adapt. Control Signal Process. (IF 3.1) Pub Date : 2024-01-22 Sang-Young Oh, Ho-Lim Choi
We consider a global regulation problem for a chain of integrators with an unknown time-varying input delay by event-triggered output feedback control. We show that the considered system is globally regulated by the proposed event-triggered output feedback controller with a dynamic gain-scaling factor. Moreover, we show that interexecution times are lower bounded and these lower bounds can be increased
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Multiple models for decentralised adaptive control of discrete-time systems Int. J. Adapt. Control Signal Process. (IF 3.1) Pub Date : 2024-01-21 Rajini Makam, Koshy George
This paper investigates decentralised adaptive control of discrete-time systems consisting of linear time-invariant systems and a class of nonlinear systems. The parameters of the subsystems and the interconnection strengths between the subsystems are assumed unknown. Multiple adaptive prediction models with switching are used in this paper to address the relatively poor transient performance that
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Ensemble average of enhanced generalized envelope spectrum for fault detection of rolling element bearings Int. J. Adapt. Control Signal Process. (IF 3.1) Pub Date : 2024-01-21 Danchen Zhu, Yinan Liu, Qunwei Zhu
Since bearing defects usually occur which threaten the stable operation of the machinery, bearing fault detection is of great importance. However, the bearing fault signals inevitably exhibit strong interference components due to the complex structures of the real equipment, which leads to difficulty in fault feature detection. To address the problem, a fault diagnosis method based on the ensemble
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Tracking control design for cyber-physical systems with disturbances and input delays: An interval type-2 fuzzy approach Int. J. Adapt. Control Signal Process. (IF 3.1) Pub Date : 2024-01-21 R. Sakthivel, S. Anusuya, F. Kong, Wenbin Chen
The underlying intention of this work is to devise a tracking control protocol for the nonlinear cyber-physical systems that are prone to external disturbances, time-varying input delays and deception attacks. To be more precise, the modified repetitive tracking controller is formulated for ensuring the asymptotic tracking outcomes of the assayed system with the aim of addressing the impacts caused
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Adaptive prescribed-time tracking control for uncertain nonlinear systems with full state constraints Int. J. Adapt. Control Signal Process. (IF 3.1) Pub Date : 2024-01-21 Bo Liu, Jiayi Li, Ruicheng Ma, Jun Fu
This paper investigates the adaptive tracking control in a prescribed-time for a class of uncertain nonlinear systems with a full state constraint. Firstly, in order to restrict the state constraints, the barrier Lyapunov function is employed and parameter uncertainty is handled based on linear-in-the-parameters (LIP). A prescribed-time adaptive controller is then designed by backstepping to ensure
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Predefined-time adaptive backstepping control for a class of nonlinear dynamical systems with parametric uncertainties Int. J. Adapt. Control Signal Process. (IF 3.1) Pub Date : 2024-01-18 Vijay Kumar Singh, Shyam Kamal, Sandip Ghosh
In this article, we address the problem of achieving predefined-time control for a class of nonlinear dynamical systems with parameter uncertainties. We introduce an adaptive predefined-time control algorithm based on integrator backstepping for n $$ n $$ th order nonlinear systems. Using Lyapunov's stability theory, we establish that the proposed predefined time control approach, combined with an
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Improved maximum correntropy cubature Kalman and information filters with application to target tracking under non-Gaussian noise Int. J. Adapt. Control Signal Process. (IF 3.1) Pub Date : 2024-01-17 Tao Lu, Weidong Zhou, Shun Tong
The cubature Kalman filter (CKF) based on the maximum correntropy criterion (MCC) has been widely used in the target tracking. However, numerical problems usually occur when there are outliers in the measurement noise. In order to solve the problems of state estimation under the non-Gaussian measurement noise, a new combined cost function is defined based on the weighted least squares (WLS) method
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Distributed finite-time adaptive fault-tolerant consensus control of second-order multi-agent systems under deception attacks Int. J. Adapt. Control Signal Process. (IF 3.1) Pub Date : 2024-01-16 Chun Liu, Wanyi Wang, Ziquan Yu, Ron J. Patton
This study addresses the issue of distributed fault-tolerant consensus control for second-order multi-agent systems subject to simultaneous actuator bias faults in the physical layer and deception attacks in the cyber layer. Cyber-physical threats (malicious state-coupled nonlinear attacks and physical deflection faults), unknown control gains, external disturbances and uncertainties force the failure
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Adaptive consensus tracking control for robotic manipulators with nonlinear time-varying fault-tolerant actuator and unknown control input directions Int. J. Adapt. Control Signal Process. (IF 3.1) Pub Date : 2024-01-14 Qianyi Zhang, Qingzhen Zhang, Jinkun Liu
The consensus tracking mechanism of a multi-agent system consisting of third-order nonlinear single joint manipulators was investigated in this study based on the pertinent industrial context. Unlike many previous studies on consensus control that presumed a known control direction, this investigation addressed the complex challenges posed by unknown control directions and time-varying actuators. Specifically
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Adaptive trajectory tracking control for VTOL UAVs with unknown time-varying mass using an extended I&I estimator Int. J. Adapt. Control Signal Process. (IF 3.1) Pub Date : 2024-01-14 Qi Han, Zhitao Liu, Hongye Su, Xiangbin Liu
This article presents an adaptive control scheme for vertical takeoff and landing unmanned aerial vehicles subject to time-varying mass based on the immersion and invariance scheme in an extended form. For the varying mass parameter in the position control, a generalized I&I adaptive law is proposed with higher dimension to estimate the mass and its derivatives. The stability of the resulting estimation
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Addressing asymmetric saturation in chaotic Chua's circuit switched system: A combined adaptive sliding mode strategy Int. J. Adapt. Control Signal Process. (IF 3.1) Pub Date : 2024-01-10 Saeed Amiri, Seyed Mohsen Seyed Moosavi, Mehdi Forouzanfar, Ebrahim Aghajari
The purpose of this research is to develop a combined adaptive nonlinear feedback integral sliding mode (CANFISM) for the Chua's switched nonlinear circuit systems. The suggested adaptive composite nonlinear technique is improved to provide a robust controller with enhanced performance in the face of uncertainty and asymmetric saturation. The proposed technique has been theoretically validated, and
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Cardiac abnormalities from 12-Lead ECG signals prediction based on deep convolutional neural network optimized with nomadic people optimization algorithm Int. J. Adapt. Control Signal Process. (IF 3.1) Pub Date : 2024-01-04 S. V. Evangelin Sonia, R. Nedunchezhian, M. Rajalakshmi
Cardiovascular disease (CVD) is a most dangerous disease in the world. Early accurate and automated identification helps the medical professional make a correct diagnosis and administer fast treatment and saving many lives. Several studies have been suggested in this area, but no one yield the expected outcomes owing to data imbalance issue in the medical and healthcare industries. To overcome this
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Parameters self-turning controller fused with an adaptive nominal model for disturbance observer: Application to direct drive manipulator with significant payload changes Int. J. Adapt. Control Signal Process. (IF 3.1) Pub Date : 2024-01-07 Yang Liu, Songlin Chen, Cheng Xie
The direct drive manipulator, which directly links the joint shaft to the motor rotor, is susceptible to external disturbances and model perturbations. When the direct drive manipulator operates with varying payloads, the disturbance observer (DOB) with a constant nominal model struggles to achieve satisfactory performance. To minimize estimation errors caused by disturbance, enhance trajectory tracking
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Exponentially stable adaptive optimal control of uncertain LTI systems Int. J. Adapt. Control Signal Process. (IF 3.1) Pub Date : 2023-12-29 Anton Glushchenko, Konstantin Lastochkin
A novel method of an adaptive linear quadratic (LQ) regulation of uncertain continuous linear time-invariant systems is proposed. Such an approach is based on the direct self-tuning regulators design framework and the exponentially stable adaptive control technique developed earlier by the authors. Unlike the known solutions, a procedure is proposed to obtain a non-overparametrized regression equation
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Model-free based adaptive finite time control with multilayer perceptron neural network estimation for a 10 DOF lower limb exoskeleton Int. J. Adapt. Control Signal Process. (IF 3.1) Pub Date : 2023-12-29 Farid Kenas, Nadia Saadia, Amina Ababou, Noureddine Ababou
This article presents a Model-Free Adaptive Nonsingular Fast Terminal Sliding Mode Controller with Super Twisting and Multi-Layer Perceptron (MLP) neural network for motion control of a 10 DOFs lower limb exoskeleton used in rehabilitation. The proposed controller employs a second-order ultra-local model to replace the complex dynamics of the exoskeleton and uses an MLP neural network to estimate the
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Fuzzy adaptive time-varying formation control of USVs with actuator faults Int. J. Adapt. Control Signal Process. (IF 3.1) Pub Date : 2023-12-28 Kelin Feng, Kewen Li, Yongming Li
This paper investigates the fuzzy adaptive time-varying formation control problem for fully-actuated unmanned surface vehicles (USVs) systems. The studied USVs systems include uncertain dynamics and actuator faults. Fuzzy logic systems (FLSs) and disturbance observer are employed to estimate uncertain nonlinear dynamics in system model and external time-varying disturbances, respectively. Meanwhile
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Fast finite-time adaptive backstepping containment control for high-order stochastic nonlinear multi-agent systems Int. J. Adapt. Control Signal Process. (IF 3.1) Pub Date : 2023-12-27 Yuewei Song, Lin Zhao
In this paper, the fast finite-time backstepping containment control strategy is considered for high-order stochastic multi-agent systems. The addition of the finite-time command filter avoids calculating explosion which occurs in the differential process of virtual control signals for the high-order system on the traditional backstepping and makes convergence speed of control system faster. The influence
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Robust fuzzy model predictive control for nonlinear discrete-time systems Int. J. Adapt. Control Signal Process. (IF 3.1) Pub Date : 2023-12-27 Parvin Mahmoudabadi, Alireza Naderi Akhormeh
This paper studies the issue of Robust Fuzzy Model Predictive Control (RFMPC) of nonlinear discrete-time systems in the presence of disturbance. To this end, Takagi-Sugeno (T-S) fuzzy model is adopted in order to characterize uncertain nonlinear systems and facilitate providing controller for such systems. Non-parallel distributed compensation (non-PDC) technique is applied to design improved RFPMC
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Inverse optimally adaptive neural output-feedback control of stochastic nonlinear systems Int. J. Adapt. Control Signal Process. (IF 3.1) Pub Date : 2023-12-25 Xinyi Lu, Fang Wang, Jing Zhang
In this article, for a class of stochastic nonlinear systems with non-strict feedback, a neural adaptive inverse optimal output feedback control design scheme is presented. First, according to the existing inverse optimal criterion, an auxiliary system is established. On this basis, a novel observer is built to evaluate the unpredictable states. Second, in the control process, neural networks (NNs)
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Fuzzy inference based feature selection and optimized deep learning for Advanced Persistent Threat attack detection Int. J. Adapt. Control Signal Process. (IF 3.1) Pub Date : 2023-12-25 Anil Kumar, Amandeep Noliya, Ritu Makani
One of the attacks that have rapidly happen is Advanced Persistent Threat (APT). APT attacks contain different sophisticated approaches and methods of attacking targets for stealing confidential as well as sensitive information. This research introduced novel and effective APT attack detection techniques, namely Smart Flower Cosine Algorithm-driven Deep Convolutional Neural Network (SFCA-DeepCNN).
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Parameters estimation for the Hammerstein-Wiener models with colored noise based on hybrid signals Int. J. Adapt. Control Signal Process. (IF 3.1) Pub Date : 2023-12-22 Feng Li, Jiahu Han
A three-stage estimation approach of the Hammerstein-Wiener model with colored noise using hybrid signals is considered in this article. The Hammerstein-Wiener model where a linear dynamic block is embedded between two static nonlinear elements, in which two nonlinear elements represented by two independent neural fuzzy models and a linear element represented by autoregressive exogenous model. The
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Event-triggered distributed consensus control of nonlinear multi-agent systems with unknown Bouc–Wen hysteresis input and DoS attacks Int. J. Adapt. Control Signal Process. (IF 3.1) Pub Date : 2023-12-20 Shiyu Guo, Ning Xu, Ben Niu, Xudong Zhao, Adil M. Ahmad
In this paper, the event-triggered distributed consensus tracking control problem for a class of multi-agent systems (MASs) is studied, where denial-of-service (DoS) attacks and Bouc-Wen hysteresis inputs are considered in the communication channel and the actuator, respectively. First, when communication networks are subject to malicious DoS attacks, the connection weights of the communication topology
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Dynamic event-triggered asynchronous security load frequency control for semi-Markov jump multi-area power systems against deception attacks Int. J. Adapt. Control Signal Process. (IF 3.1) Pub Date : 2023-12-14 Yulong Li, Xiaoqing Li, Jun Cheng, Kaibo Shi, Wei Sun
This paper focuses on the issue of dynamic event-triggered (DET) asynchronous security load frequency control (LFC) for uncertain semi-Markov jump interconnected multi-area power systems (IMAPSs). Initially, the semi-Markov process which in the presence of partially unknown transition rates (TRs) is exactly introduced to model the switching behaviors of the IMAPSs. Subsequently, the DET mechanism is
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Command filter and high gain observer based adaptive output feedback control for stochastic nonlinear systems with prescribed performance and input quantization Int. J. Adapt. Control Signal Process. (IF 3.1) Pub Date : 2023-12-13 Hailin Tang, Tianping Zhang, Meizhen Xia
In this paper, an adaptive output feedback dynamic surface control (DSC) strategy is proposed for strict-feedback stochastic nonlinear systems with input quantization, prescribed performance and dynamic uncertainties. A new quantizer is used to process the input signal, which can avoid the chattering of the quantization signal and keep the upper bound of the quantization error constant. Radial basis