International Journal of Adaptive Control and Signal Processing最新文献

{"title":"Adaptive Control for Nonlinear Systems With Input and Output Quantization Under DoS Attacks","authors":"Xin Xie,&nbsp;Fang Wang","doi":"10.1002/acs.70004","DOIUrl":"https://doi.org/10.1002/acs.70004","url":null,"abstract":"<div>\u0000 \u0000 <p>Under intermittent cyber attacks, an adaptive control strategy has been proposed for a class of nonlinear systems with input and output quantization. Unlike existing studies on denial-of-service attacks (DoS), the system dynamics in this paper are unknown. Since the system dynamics are unknown and both input and output signals are affected by DoS attacks, system variables are not directly observable. To address this issue, a fuzzy observer with switchable gains is proposed. In contrast to conventional DoS attacks research, both input and output signals in this work are quantized prior to transmission, rendering traditional backstepping control methods inapplicable. To solve this problem, the following steps are proposed: Firstly, an auxiliary intermediate controller is designed using the unquantized states. Secondly, by replacing the unquantized states with quantized states in the auxiliary intermediate controller, the intermediate controller and the actual controller are obtained. Thirdly, to compensate for the impact of quantization errors, Lemma 4 is introduced, and the control strategies are first proposed to guarantee the stability of the nonlinear system in the presence of DoS attacks. Furthermore, simulation results are presented to demonstrate the efficiency of the proposed control method.</p>\u0000 </div>","PeriodicalId":50347,"journal":{"name":"International Journal of Adaptive Control and Signal Processing","volume":"40 3","pages":"470-485"},"PeriodicalIF":3.8,"publicationDate":"2026-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147568496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Neural Network-Based Adaptive Event-Triggered Control for Virtual Coupling High-Speed Trains With Unknown Parameters","authors":"Hui Zhao,&nbsp;Hanhong Cui,&nbsp;Xuewu Dai,&nbsp;Yuan Zhao","doi":"10.1002/acs.70010","DOIUrl":"https://doi.org/10.1002/acs.70010","url":null,"abstract":"<div>\u0000 \u0000 <p>To cope with the effects of unknown resistance parameters, additional resistance, and high-frequency controller updating in the cooperation control of virtual-coupling high-speed train systems, this paper proposes a neural network-based adaptive event-triggered control scheme for trains. Firstly, with the train-to-train communication mode, a synthetic tracking error and its converted form are proposed to restrain the speed and position errors of trains based on the train model. Then, for the unknown resistance parameters and bounded additional resistance, a radial basis function neural network (RBFNN) based adaptive control scheme is investigated to realize the cooperative operation of trains. By incorporating the event-triggered mechanism, the communication source between the controller and actuator can be saved by reducing unnecessary controller updating. In addition, the stability condition of virtual coupling train systems is presented by the convergence analysis of the synthetic tracking error. Finally, simulation experiments are conducted to verify that the control scheme is able to realize cooperation of virtual coupling train systems in the presence of unknown parameters.</p>\u0000 </div>","PeriodicalId":50347,"journal":{"name":"International Journal of Adaptive Control and Signal Processing","volume":"40 3","pages":"522-533"},"PeriodicalIF":3.8,"publicationDate":"2026-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147568506","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hopfield Neural Networks for Online Constrained Parameter Estimation With Time-Varying Dynamics and Disturbances","authors":"Miguel Pedro Silva","doi":"10.1002/acs.70011","DOIUrl":"https://doi.org/10.1002/acs.70011","url":null,"abstract":"<p>This paper proposes two projector-based Hopfield neural network (HNN) estimators for online, constrained parameter estimation under time-varying data, additive disturbances, and slowly drifting physical parameters. The first is a constraint-aware HNN that enforces linear equalities and inequalities (via slack neurons) and continuously tracks the constrained least-squares target. The second augments the state with compensation neurons and a concatenated regressor to absorb bias-like disturbance components within the same energy function. For both estimators, we establish global uniform ultimate boundedness with explicit convergence rate and ultimate bound, and we derive practical tuning rules that link the three design gains to closed-loop bandwidth and steady-state accuracy. We also introduce an online identifiability monitor that adapts the constraint weight and time step, and, when needed, projects updates onto identifiable subspaces to prevent drift in poorly excited directions. A two-degree-of-freedom mass-spring-damper study with Monte Carlo trials compares the proposed HNN estimators against projector-based recursive least squares, disturbance-aware projector-based Kalman filtering, and disturbance-aware projector-based moving-horizon estimation. The HNN estimators achieve competitive or superior accuracy with zero constraint violations, reduced disturbance-induced bias (especially with compensation), and low per-step computational cost suitable for high-rate deployment.</p>","PeriodicalId":50347,"journal":{"name":"International Journal of Adaptive Control and Signal Processing","volume":"40 3","pages":"544-564"},"PeriodicalIF":3.8,"publicationDate":"2026-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/acs.70011","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147570315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robust Fault \u0000 \u0000 \u0000 \u0000 \u0000 H\u0000 \u0000 \u0000 ∞\u0000 \u0000 \u0000 \u0000 $$ {H}_{infty } $$\u0000 Filtering Design in Finite Frequency Domain for Discrete-Time Switched Singular Systems","authors":"Khalid Saidi,&nbsp;El Houssaine Tissir,&nbsp;Noreddine Chaibi","doi":"10.1002/acs.70015","DOIUrl":"https://doi.org/10.1002/acs.70015","url":null,"abstract":"<div>\u0000 \u0000 <p>The present paper focuses on the <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msub>\u0000 <mrow>\u0000 <mi>H</mi>\u0000 </mrow>\u0000 <mrow>\u0000 <mi>∞</mi>\u0000 </mrow>\u0000 </msub>\u0000 </mrow>\u0000 <annotation>$$ {H}_{infty } $$</annotation>\u0000 </semantics></math> filtering problem for a class of discrete-time switched singular (SS) systems with faults and finite frequency (FF) input signals under an arbitrary switching signal. The frequencies of the unknown input disturbance are assumed to be in a finite range. The objective is to design a linear mode-dependent filter guaranteeing the asymptotic stability of the filtering error system with a prescribed <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msub>\u0000 <mrow>\u0000 <mi>H</mi>\u0000 </mrow>\u0000 <mrow>\u0000 <mi>∞</mi>\u0000 </mrow>\u0000 </msub>\u0000 </mrow>\u0000 <annotation>$$ {H}_{infty } $$</annotation>\u0000 </semantics></math> performance bound in an FF range. Based on the generalized Kalman-Yakubovic-Popov lemma, multiple Lyapunov–Krasovskii functional, and some free weighting matrices are used to provide an additional degree of freedom. Efficient conditions are formulated in terms of linear matrix inequalities. The proposed method is designed in the FF domain to reduce the conservativeness generated by those designed in the entire frequency (EF) domain. Finally, numerical examples are provided to illustrate the advantages and the effectiveness of the proposed approach.</p>\u0000 </div>","PeriodicalId":50347,"journal":{"name":"International Journal of Adaptive Control and Signal Processing","volume":"40 3","pages":"653-673"},"PeriodicalIF":3.8,"publicationDate":"2026-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147562673","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptive Dynamic Surface Fuzzy Tracking Control for Nonlinear Systems With Predefined Time and Error Accuracy","authors":"Guodong You,&nbsp;Xingyun Li,&nbsp;Hailong Zhang,&nbsp;Lu Liu","doi":"10.1002/acs.70007","DOIUrl":"https://doi.org/10.1002/acs.70007","url":null,"abstract":"<div>\u0000 \u0000 <p>A class of strict feedback control systems containing external disturbances is studied, and an adaptive dynamic surface fuzzy tracking control strategy with predefined time and error accuracy is proposed. Firstly, the dynamic surface control is used to solve the computational complexity problem; secondly, the fuzzy logic technique and Young's inequality are used to derive the upper bound containing the unknown nonlinear function and the external disturbance, Then, the first-order nonlinear filter is combined with the adaptive technique, and according to the Lyapunov stability theorem, it is rigorously proved that the proposed predefined time controller can converge the output error to the origin within the predefined time within a small neighborhood near the origin. Finally, the effectiveness of the proposed method in this paper is verified by simulation examples.</p>\u0000 </div>","PeriodicalId":50347,"journal":{"name":"International Journal of Adaptive Control and Signal Processing","volume":"40 3","pages":"486-498"},"PeriodicalIF":3.8,"publicationDate":"2026-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147568914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robust Time-Varying Estimators Using Weighted Fusion for Multisensor Networked Systems With Missing Observations and Two-Step Random Delays","authors":"Zhenzhen Gu,&nbsp;Wenqiang Liu","doi":"10.1002/acs.4105","DOIUrl":"https://doi.org/10.1002/acs.4105","url":null,"abstract":"<div>\u0000 \u0000 <p>This study addresses the challenge of developing a robust, time-varying fusion Kalman estimator for multi-sensor networks with compound indeterminacies, including multiplicative noises, two-step stochastic delays, missing measurements, and variations in uncertain noise. To tackle this problem, a model transformation strategy that includes the augmentation method, the de-randomization method, and the fictional noise method is employed. This approach converts the original multi-sensor system into an equivalent system with constant parameters and unknown noise variances, laying the foundation for subsequent estimator design. Within a unified framework, three robust fusion time-varying Kalman estimators—predictor, filter, and smoother—are derived based on the minimax robust estimation principle and the worst-case system with the conservative upper bounds of the noise variances. The design of this robust fusion Kalman estimator utilizes the robust fusion Kalman predictor, further derives the corresponding weighted fusion filter and smoother, and adopts three weighting rules: matrix weighting, diagonal matrix weighting and scalar weighting. The robustness of the proposed fusion estimators is verified through the augmented noise method, the non-negative definite matrix decomposition method, and the global Lyapunov equation method, which also reveal the accuracy correlation between robust local estimates and fused time-varying Kalman estimates. Specifically, “robustness” herein refers to ensuring that the actual estimation error variances maintain corresponding minimum upper bounds for all admissible uncertainties. Finally, simulation experiments on autoregressive (AR) signal processing and the original system demonstrate that robust fusion state estimation can solve the problem of robust fusion signal estimation and verify the effectiveness and accuracy of the proposed method.</p>\u0000 </div>","PeriodicalId":50347,"journal":{"name":"International Journal of Adaptive Control and Signal Processing","volume":"40 3","pages":"579-596"},"PeriodicalIF":3.8,"publicationDate":"2026-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147570371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantized Iterative Learning Control for Consensus of Nonlinear Impulsive Multi-Agent Systems With Inter-Channel Encoding-Decoding Mechanisms and Packet Dropouts","authors":"Hongjin Zhang,&nbsp;JinRong Wang,&nbsp;Dong Shen","doi":"10.1002/acs.70017","DOIUrl":"https://doi.org/10.1002/acs.70017","url":null,"abstract":"<div>\u0000 \u0000 <p>This paper addresses the consensus problem of impulsive multi-agent systems (MASs) under bandwidth constraints and packet dropouts, and a distributed quantized iterative learning control (ILC) scheme based on intermittent update principles is designed. Unlike traditional packet dropouts models in MASs where dropouts are characterized by simultaneous across the entire network, we have established a novel framework featuring inter-channel independent packet dropouts processes. Based on this framework, a distributed learning control algorithm that leverages the quantized signals from encoding and decoding mechanisms, incorporates an inter-channel quantizer, and accounts for the packet dropouts characteristic. Using mathematical tools such as the Lyapunov equation, impulsive Gronwall inequality, Cauchy inequality, and the fixed-point theorem, we rigorously derive the consensus result of tracking error in the sense of expectation. Furthermore, this paper presents the connections between the scaling sequence, the learning step size, and the packet dropouts rate, avoiding the divergence of the quantized values in the iterative process. Finally, the effectiveness of the theoretical results is verified by the simulation of the multi-pendulum networked system.</p>\u0000 </div>","PeriodicalId":50347,"journal":{"name":"International Journal of Adaptive Control and Signal Processing","volume":"40 3","pages":"627-641"},"PeriodicalIF":3.8,"publicationDate":"2026-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147562340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptive Fuzzy Fixed-Time Attitude Tracking Control for Rigid Spacecraft With Input Delay","authors":"Huanqing Wang,&nbsp;Yaqi Feng","doi":"10.1002/acs.70012","DOIUrl":"https://doi.org/10.1002/acs.70012","url":null,"abstract":"<div>\u0000 \u0000 <p>This paper investigates the fixed-time adaptive fuzzy attitude tracking control problem for rigid spacecraft with input delay. Fuzzy logic systems (FLSs) are applied to perform online estimation of unknown nonlinear functions in a rigid spacecraft system. By introducing the hyperbolic tangent function, the potential singularity issue in the derivative of the virtual controller is solved. Specifically, a fixed-time auxiliary system is designed to eliminate the effect of input delay. To this end, an adaptive fuzzy fixed-time attitude tracking control scheme based on backstepping technology and fixed-time theory is proposed. Under the proposed scheme, the boundedness of all signals in the closed-loop system is ensured, and the tracking error converges to a small neighborhood of zero within a fixed time. Eventually, simulation results illustrate the validity of the scheme.</p>\u0000 </div>","PeriodicalId":50347,"journal":{"name":"International Journal of Adaptive Control and Signal Processing","volume":"40 3","pages":"642-652"},"PeriodicalIF":3.8,"publicationDate":"2026-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147562674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Secure Adaptive Trajectory Tracking Control for Spacecraft Attitude Systems With Unknown FDI Attacks and Disturbance","authors":"Kangkang Sun,&nbsp;Zhiheng Liang,&nbsp;Qi Zheng","doi":"10.1002/acs.70020","DOIUrl":"https://doi.org/10.1002/acs.70020","url":null,"abstract":"<div>\u0000 \u0000 <p>This article considers the secure adaptive trajectory tracking control problem for spacecraft attitude systems with unknown false data injection (FDI) attacks and disturbance. The kinematics and dynamics equations of a 3-1-2 rotation sequence using Euler angles are given to construct a model of the spacecraft attitude systems. A recursive scheme is developed to design virtual and actual control laws. Meanwhile, a parameter adaptive law is utilized to estimate the upper bound of the attack signal. The proposed control method can guarantee that the tracking error of attitude angles converges to a small region of the origin, and a Lyapunov function is utilized to prove the stability of the spacecraft attitude closed-loop systems. Finally, simulation results are provided to verify the effectiveness of the developed approach.</p>\u0000 </div>","PeriodicalId":50347,"journal":{"name":"International Journal of Adaptive Control and Signal Processing","volume":"40 3","pages":"710-718"},"PeriodicalIF":3.8,"publicationDate":"2026-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147563874","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Bayesian Approach to Total Least-Squares in Perturbed Compressive Sensing","authors":"Junlin Li,&nbsp;Weiwei Sun,&nbsp;Feng Ding,&nbsp;Renzhi Lu","doi":"10.1002/acs.70006","DOIUrl":"https://doi.org/10.1002/acs.70006","url":null,"abstract":"<div>\u0000 \u0000 <p>This paper introduces a Bayesian total least-squares (B-TLS) formulation to address the perturbed compressive sensing problem. Due to the presence of a nonconvex nonseparable penalty, applying the traditional alternating minimization method to this formulation poses mathematical and computational challenges. To mitigate this problem, we propose a Bayesian proximal alternating linearized minimization (BPALM) algorithm. In this algorithm, each subproblem is characterized by convexity and yields solutions that are explicit and analytical. We further demonstrate the convergence of the proposed algorithm towards a stationary point of the objective function. Numerical experiments in sparse signal recovery are conducted to exhibit the proposed algorithm's effectiveness and superiority, focusing on its computational efficiency and estimation accuracy.</p>\u0000 </div>","PeriodicalId":50347,"journal":{"name":"International Journal of Adaptive Control and Signal Processing","volume":"40 3","pages":"511-521"},"PeriodicalIF":3.8,"publicationDate":"2026-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147569544","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}