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Parameter adaptive based neural network sliding mode control for electro‐hydraulic system with application to rock drilling jumbo Int. J. Adapt. Control Signal Process. (IF 3.1) Pub Date : 2024-04-18 Xinping Guo, Hengsheng Wang, Hua Liu
SummaryRock drilling jumbo is an important large construction machine used for tunneling construction, and its automation has an urgent demand in engineering. However, the electro‐hydraulic system of the rock drilling jumbo has strong parameters uncertainties and some dynamics that are hard to model accurately, which causes certain challenges for designing model‐based high‐performance control algorithms
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Partial‐state feedback adaptive stabilization for a class of uncertain nonholonomic systems Int. J. Adapt. Control Signal Process. (IF 3.1) Pub Date : 2024-04-18 Jiangbo Yu, Yungang Liu, Chengdong Li, Yuqiang Wu
SummaryIn this paper, we investigate the global adaptive stabilization problem via partial‐state feedback for a class of uncertain chained‐form nonholonomic systems with the dynamic uncertainty and nonlinear parameterization. The notions of Sontag's input‐to‐state stability (ISS) and ISS‐Lyapunov function, together with the changing supply rates technique are used to overcome the dynamic uncertainty
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Event‐triggered adaptive tracking control for stochastic nonlinear systems under predetermined finite‐time performance Int. J. Adapt. Control Signal Process. (IF 3.1) Pub Date : 2024-04-18 Dong‐Mei Wang, Shan‐Liang Zhu, Li‐Ting Lu, Yu‐Qun Han, Wenwu Wang, Qing‐Hua Zhou
SummaryIn this paper, an event‐triggered adaptive tracking control strategy is proposed for strict‐feedback stochastic nonlinear systems with predetermined finite‐time performance. Firstly, a finite‐time performance function (FTPF) is introduced to describe the predetermined tracking performance. With the help of the error transformation technique, the original constrained tracking error is transformed
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Retrospective‐cost‐based model reference adaptive control of nonminimum‐phase systems Int. J. Adapt. Control Signal Process. (IF 3.1) Pub Date : 2024-04-16 Nima Mohseni, Dennis S. Bernstein
This paper presents a novel approach to model reference adaptive control inspired by the adaptive pole‐placement controller (APPC) of Elliot and based on retrospective cost optimization. Retrospective cost model reference adaptive control (RC‐MRAC) is applicable to nonminimum‐phase (NMP) systems assuming that the NMP zeros are known. Under this assumption, the advantage of RC‐MRAC is a reduced need
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Interval parity relations design for fault diagnosis Int. J. Adapt. Control Signal Process. (IF 3.1) Pub Date : 2024-04-15 Alexey Zhirabok, Alexander Zuev, Vladimir Filaretov
SummaryThe problem of interval parity relations design for systems described by linear and nonlinear models under the external disturbances is considered. The problem is solved based on the reduced‐order model of the original system. The relations allowing designing interval parity relations insensitive or having minimal sensitivity to the disturbances are obtained. The obtained interval parity relations
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A novel fault diagnosis method for imbalanced datasets based on MCNN‐Transformer model in industrial processes Int. J. Adapt. Control Signal Process. (IF 3.1) Pub Date : 2024-04-15 Rongyang Lu
SummaryFault diagnosis methods based on deep learning have been extensively applied to the fault classification of rolling bearings, yielding favorable results. However, many of these methods still have substantial room for improvement in practical industrial scenarios. This article addresses the issue of imbalanced fault data categories commonly encountered in real‐world contexts and discusses the
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Fault diagnosis of virtually‐coupled trains by adaptive observer with pattern‐matched detection and reinforced identification Int. J. Adapt. Control Signal Process. (IF 3.1) Pub Date : 2024-04-13 Shigen Gao, Qingchao Zhai, Kaibo Zhao
SummaryVirtual coupling is gaining in popularity as a promising development direction to maximize the rail‐line efficiency by minimizing the headway distance among trains in the presence of potentially encountered traction engines' faults with unknown amplitude, happening time and probability, which would be huge threat to the safety of trains without proper sensing and handling. This paper considers
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Robust fixed‐time synchronization of fuzzy shunting‐inhibitory cellular neural networks with feedback and adaptive control Int. J. Adapt. Control Signal Process. (IF 3.1) Pub Date : 2024-04-13 Zhenjiang Liu, Yi‐Fei Pu, Xiyao Hua, Xingxing You
This article addresses the robust fixed‐time synchronization of fuzzy shunting‐inhibitory cellular neural networks (FSICNNs) with delays by utilizing two different types of control strategies. First, a feedback controller is proposed to achieve fixed‐time synchronization of FSICNNs. Secondly, a novel adaptive controller is designed to guarantee fixed‐time synchronization of FSICNNs, automatically adjusting
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Backstepping based adaptive iterative learning control for non‐strict feedback systems with unknown input nonlinearities Int. J. Adapt. Control Signal Process. (IF 3.1) Pub Date : 2024-04-12 Huihui Shi, Qiang Chen, Yaqian Li, Xiongxiong He
SummaryThe initial state inconsistency and iteration‐varying trajectory problems are considered in adaptive iterative learning control (AILC) to enhance the tracking performance of the non‐strict feedback systems with unknown input nonlinearities. Through constructing an error reference trajectory independence of the reference signal, the restrictions on the initial condition and reference trajectory
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A cooperative neural dynamic model for solving general convex nonlinear optimization problems with fuzzy parameters and an application in manufacturing systems Int. J. Adapt. Control Signal Process. (IF 3.1) Pub Date : 2024-04-06 Mohammadreza Jahangiri, Alireza Nazemi
SummaryIn the presented study, the solution of the fuzzy nonlinear optimization problems (FNLOPs) is calculated using a recurrent neural network (RNN) model. Since there is a few research for solving FNLOP by RNN's, we give a new approach to solve the problem. By reducing the original program to an interval problem and then weighting problem, the Karush–Kuhn–Tucker (KKT) conditions are given. Moreover
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Optimal captured power control of variable speed wind turbine systems: Adaptive dynamic programming approach Int. J. Adapt. Control Signal Process. (IF 3.1) Pub Date : 2024-04-06 Nga Thi‐Thuy Vu
SummaryAn adaptive optimal controller is proposed in this paper to maximize the captured power of a variable speed wind power system. The proposed controller is a combination of optimal and adaptive control components. The Adaptive Dynamic Programming technique is used to design the optimal control component to overcome the nonlinear problem of system dynamics and ensure stability. While the neural
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Fuzzy sliding mode tracking control of discrete‐time nonlinear interconnected systems via a previewable approach Int. J. Adapt. Control Signal Process. (IF 3.1) Pub Date : 2024-04-06 Xianghui Qin, Junchao Ren
SummaryThis article investigates the fuzzy sliding mode tracking problem for discrete‐time nonlinear interconnected systems (ICSs) via a previewable information approach. First, an augmented system containing tracking errors and previewable reference and disturbance signals is established for discrete‐time nonlinear ICSs according to a previewable length. Second, novel sliding surfaces are given and
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Fixed time function combination synchronization of perturbed chaotic systems via adaptive control Int. J. Adapt. Control Signal Process. (IF 3.1) Pub Date : 2024-04-06 Haipeng Su, Runzi Luo
SummaryThe concerns of this article is to investigate the fixed time function combination synchronization (FCS) among three different chaotic systems with disturbances. First, a new stability theorem is presented by virtue of the integral inequality technique and a more accurate upper estimation of the convergence time is given. Next, the fixed time FCS problem is considered for the perturbed chaotic
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Adaptive fixed‐time anti‐synchronization and synchronization control for Liu‐Chen‐Liu chaotic systems with actuator faults Int. J. Adapt. Control Signal Process. (IF 3.1) Pub Date : 2024-04-01 Huanqing Wang, Ge Chai, Shijia Kang
SummaryIn this paper, the fixed‐time anti‐synchronization and synchronization fault‐tolerant control problem is considered for two identical Liu‐Chen‐Liu chaotic systems with uncertain parameters. With the help of fixed‐time stability theory and adaptive backstepping method, we propose two novel adaptive controllers. In the control scheme proposed in this paper, the adaptive backstepping technique
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Breast cancer diagnosis through an optimization‐driven multispectral gamma correction (ODMGC) Int. J. Adapt. Control Signal Process. (IF 3.1) Pub Date : 2024-04-01 Arul Edwin Raj A, Nabihah Binti Ahmad, Ananiah Durai S
SummaryThe Optimization‐Driven Multispectral Gamma Correction (ODMGC) algorithm overcomes challenges in gathering subtle information and detecting cancer in dense breast thermograms. This algorithm enhances the accuracy of true positives and true negatives while minimising false negatives and false positives. The ODMGC involves a multi‐step optimisation process that categorises grey‐scale images of
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Predefined‐time adaptive fuzzy control for a class of stochastic nonlinear uncertain systems Int. J. Adapt. Control Signal Process. (IF 3.1) Pub Date : 2024-04-01 Jingyi Wu, Fang Wang, Jing Zhang
SummaryThis article is mainly concentrated on the predefined‐time adaptive fuzzy control of stochastic nonlinear systems. To handle nonlinear functions that are uncertain, fuzzy logic systems (FLSs) are used to approximate them. Compared with the existing studies, a Lyapunov‐type criterion for practically predefined‐time stochastic stabilization (PPSS) is put forward to guarantee the stabilization
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State saturated recursive filtering for nonlinear complex networks with energy harvesting sensors and false data injection attacks Int. J. Adapt. Control Signal Process. (IF 3.1) Pub Date : 2024-03-29 Long Xu, Xueer Bian, Hui Yu, Ling Hou
SummaryThe state saturated recursive filtering issue is researched in this article for nonlinear complex networks with energy harvesting sensors and false data injection (FDI) attacks. In communication networks, the energy of the sensors is used to transmit data from sensors to remote filters. Due to ample energy being a prerequisite for the transmission of data, the energy harvesting technology is
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A robust adaptive decomposable Volterra filter based on the hyperbolic tangent Leclerc function and its performance analysis Int. J. Adapt. Control Signal Process. (IF 3.1) Pub Date : 2024-03-29 Qianqian Liu, Zhigang Li, Yigang He
SummaryMost of the existing adaptive filter algorithms pay more attention to improving performance while ignoring the computational complexity and the impact of the impulsive environment. When encountering the impulsive noise environments, the performance of traditional nonlinear adaptive filter may be significantly reduced and usually needs high computational cost. Therefore, this article proposes
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Online identification of Hammerstein systems with B‐spline networks Int. J. Adapt. Control Signal Process. (IF 3.1) Pub Date : 2024-03-28 Yanjiao Wang, Yiting Liu, Jiehao Chen, Shihua Tang, Muqing Deng
SummaryNonlinear systems widely exist in real‐word applications and the research for these systems has enjoyed a long and fruitful history, including the system identification community. However, the modeling for nonlinear systems is often quite challenging and still remains many unresolved questions. This article considers the online identification issue of Hammerstein systems, whose nonlinear static
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Event-triggered adaptive secure lateral stabilization for autonomous vehicles under actuator attacks Int. J. Adapt. Control Signal Process. (IF 3.1) Pub Date : 2024-03-24 Hong-Tao Sun, Xinran Chen, Yitao Shen, Chen Peng, Jiwei Zhao
False data injection attacks can disrupt the steering control actions and make a real threat to both the security and safety of autonomous vehicles. In this paper, a secure event-triggered lateral control approach of autonomous vehicles subject to actuator attacks is investigated. Firstly, an arbitrary unknown actuator attacks is considered in the secure lateral steering control of autonomous vehicles
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Design of distributed event-triggered states and unknown disturbances observers for time-varying delay interconnected systems Int. J. Adapt. Control Signal Process. (IF 3.1) Pub Date : 2024-03-24 Dinh Cong Huong
This article considers the problem of designing distributed event-triggered states and unknown disturbances observers for a class of nonlinear interconnected systems where the local state vectors are affected by unknown delays and the nonlinear function satisfying the Lipschitz condition. Distributed observers are designed based on the local input vector and both the local and remote output vectors
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Data‐driven disturbance compensation control for discrete‐time systems based on reinforcement learning Int. J. Adapt. Control Signal Process. (IF 3.1) Pub Date : 2024-03-22 Lanyue Li, Jinna Li, Jiangtao Cao
SummaryIn this article, a self‐learning disturbance compensation control method is developed, which enables the unknown discrete‐time (DT) systems to achieve performance optimization in the presence of disturbances. Different from traditional model‐based and data‐driven state feedback control methods, the developed off‐policy Q‐learning algorithm updates the state feedback controller parameters and
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Binary observation‐based identification for finite impulse response systems under denial of service attacks Int. J. Adapt. Control Signal Process. (IF 3.1) Pub Date : 2024-03-21 Jingliang Wei, Ruizhe Jia, Fengwei Jing, Jin Guo
SummaryThis article considers the parameter identification problem for the finite impulse response system based on binary observations under denial of service attacks. First, in addition to designing the identification algorithm for the unknown parameter, and its convergence is verified, simultaneously obtaining the asymptotic normality. Then, based on the convergence speed of the identification algorithm
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Adaptive control for synchronization of time‐delayed complex networks with multi‐weights based on semi‐linear hyperbolic PDEs Int. J. Adapt. Control Signal Process. (IF 3.1) Pub Date : 2024-03-20 Chengyan Yang, Jianlong Qiu
SummaryThis paper studies the adaptive synchronization of complex spatio‐temporal networks modeled by semi‐linear hyperbolic partial differential equations (CSTNSLHPDEs) as well as considering time‐invariant and time‐varying delays in a one‐dimensional space. Firstly, a distributed adaptive controller is proposed, where different nodes are with different adaptive gains. Secondly, four cases, CSTNSLHPDEs
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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 : 2024-02-23 V. Tharanidharan, T. Saravanakumar, S. Marshal Anthoni
In 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 control
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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
To 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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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 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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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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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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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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Model-reference adaptive and self-tuning controls of telescopic mobile cranes with the impact of elastic cables, viscoelastic cylinders, and winds Int. J. Adapt. Control Signal Process. (IF 3.1) Pub Date : 2024-02-01 Le Anh Tuan
Telescopic truck crane is an under-actuated mechanical system with three actuators controlling five outputs. Its complex structure, shape variation, uncertain parameters, elastic cables, and viscoelastic cylinders are great challenges in control design. This article proposes two adaptive robust controllers for such cranes. A control core assures robustness based on back-stepping and Lyapunov stability
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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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Filtered generalized iterative parameter identification for equation-error autoregressive models based on the filtering identification idea Int. J. Adapt. Control Signal Process. (IF 3.1) Pub Date : 2024-01-28 Feng Ding, Xingling Shao, Ling Xu, Xiao Zhang, Huan Xu, Yihong Zhou
By using the collected batch data and the iterative search, and based on the filtering identification idea, this article investigates and proposes a filtered multi-innovation generalized projection-based iterative identification method, a filtered generalized gradient-based iterative identification method, a filtered generalized least squares-based iterative identification method, a filtered multi-innovation
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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