International Journal of Adaptive Control and Signal Processing最新文献

{"title":"Minimax Robust Kalman Estimation Under Uncertain Noise Variances, Multi-Step Random Measurement Delays and Missing Measurements","authors":"Ying Zhao,&nbsp;Chunshan Yang","doi":"10.1002/acs.70116","DOIUrl":"https://doi.org/10.1002/acs.70116","url":null,"abstract":"<div>\u0000 \u0000 <p>The paper solves the minimax robust Kalman estimation problem for a system under linearly correlated noise, uncertain noise variances, multiplicative noise, multi-step random measurement delays, and missing measurements. The system noise variance is uncertain but bounded above, and a set of Bernoulli distributed random variables with known probability is used to describe the multi-step random measurement delays and missing measurements from sensor to estimator. A novel model transformation method is proposed by using Hadamard product, and then the original system is converted into one only with uncertain fictitious noise variance. The robust time-varying Kalman predictor, filter and smoother are designed in a unified form based on the minimax robust estimation principle. A new robustness proof method, including Gerŝgorin circle theorem, matrix elementary transformation, Hadamard product theorem and generalized Lyapunov equation method, is presented to demonstrate the robustness such that the actual estimation error variance is guaranteed to have minimal upper bound for all admissible uncertainties. The computational complexity is analyzed for the robust time-varying estimator in each step, and the robust steady-state estimator is designed. The convergence in a realization between the time-varying and steady state estimator is proved. A simulation example verifies the correctness and effectiveness of the proposed results.</p>\u0000 </div>","PeriodicalId":50347,"journal":{"name":"International Journal of Adaptive Control and Signal Processing","volume":"40 10","pages":"1984-1996"},"PeriodicalIF":3.8,"publicationDate":"2026-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149125796","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":"Fault Tolerant Control for Spraying Manipulators Considering Actuator Faults Based on Actor-Critic Super Twisting Algorithm","authors":"Jingang Zhao,&nbsp;Yang Wei,&nbsp;Yinghui Li,&nbsp;Yiwen Li,&nbsp;Binbin Pei","doi":"10.1002/acs.70120","DOIUrl":"https://doi.org/10.1002/acs.70120","url":null,"abstract":"<div>\u0000 \u0000 <p>A novel Actor-Critic Algorithm based Super-Twisting Sliding Mode Fault-Tolerant Control (ACSTSM-FTC) Method for spraying manipulators is proposed in this paper under actuator failures and input constraints. The Euler–Lagrange dynamic model for Attitude Adjustment Mechanism (AAM) has been established. A novel fusion framework has been proposed that integrates the Actor-Critic algorithm with Radial Basis Function Neural Networks (RBFNN), which can effectively compensate the uncertainty of model parameters and lumped disturbance induced by actuator faults. Based on Actor-Critic algorithm and super-twisting sliding mode control method, a nonsingular fault-tolerant controller is designed, which effectively suppresses the jitter problem of the traditional control method, realizes the accurate tracking of the desired trajectory, and realizes control energy optimization at the same time. Lyapunov analysis proves error uniformly bounded within fixed time. Simulation and experimental results validate the controller's global convergence, accelerated convergence in 1.5 s, and robustness against disturbances across arbitrary initial states.</p>\u0000 </div>","PeriodicalId":50347,"journal":{"name":"International Journal of Adaptive Control and Signal Processing","volume":"40 10","pages":"2092-2111"},"PeriodicalIF":3.8,"publicationDate":"2026-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149125806","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":"SMS-Based Optimal Control of Completely Unknown Nonlinear Systems With Unknown Actuator Saturation","authors":"Ning Xu,&nbsp;Shuai Yue,&nbsp;Ning Zhao,&nbsp;Ben Niu,&nbsp;Xudong Zhao","doi":"10.1002/acs.70109","DOIUrl":"https://doi.org/10.1002/acs.70109","url":null,"abstract":"<div>\u0000 \u0000 <p>A data-driven optimal control scheme integrating sliding mode surfaces (SMSs) and neural networks (NNs) is proposed for continuous-time nonlinear systems with fully unknown dynamics and actuator saturation. In existing methods, partial system knowledge is typically required, and computationally intensive actor-critic structures are employed. Three contributions are introduced to overcome these limitations. First, a data-driven model is constructed from a recurrent neural network (RNN) with an adaptive compensation term, by which the unknown dynamics are accurately reconstructed without prior model information. Second, an adaptive feedforward compensator based on a backpropagation neural network (BNN) is designed to counteract actuator saturation; no known saturation bounds are assumed. Finally, the conventional actor-critic architecture is replaced by a dedicated critic neural network (CNN) that incorporates SMS information to approximate the Hamilton-Jacobi-Bellman (HJB) equation solution, thereby simplifying the control structure and reducing online computation while preserving performance. By the proposed scheme, restrictive modeling and saturation assumptions are relaxed, and responsiveness and robustness are enhanced through inherent SMS properties. A Lyapunov-based analysis proves that all closed-loop signals remain uniformly ultimately bounded. Numerical simulations and robotic arm experiments confirm the strategy's effectiveness and practical feasibility under simultaneous model uncertainty and actuator saturation.</p>\u0000 </div>","PeriodicalId":50347,"journal":{"name":"International Journal of Adaptive Control and Signal Processing","volume":"40 10","pages":"1956-1971"},"PeriodicalIF":3.8,"publicationDate":"2026-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149125877","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":"Prescribed-Time Composite Learning Control for Uncertain Strict-Feedback Nonlinear Systems","authors":"Zhonghua Wu,&nbsp;Yunru Jia,&nbsp;Xiangwei Bu","doi":"10.1002/acs.70117","DOIUrl":"https://doi.org/10.1002/acs.70117","url":null,"abstract":"<div>\u0000 \u0000 <p>This article proposes a prescribed-time (PT) neural network composite learning tracking control scheme for a class of strict-feedback nonlinear systems with functional uncertainties. Specifically, a PT composite learning framework is constructed to improve the estimation performance of the unknown nonlinear functions under the weaker interval excitation (IE) condition. To guarantee PT tracking performance, a PT controller with time-varying bounded gains is designed to ensure that the tracking error converges to an arbitrarily small neighborhood of zero within the prescribed time, independent of initial conditions. Furthermore, a PT dynamic surface filter is developed to overcome the explosion of complexity inherent in traditional backstepping designs. Finally, numerical simulations and real-time experiments on a permanent magnet synchronous motor (PMSM) platform are conducted to validate the effectiveness and practical applicability of the proposed control scheme.</p>\u0000 </div>","PeriodicalId":50347,"journal":{"name":"International Journal of Adaptive Control and Signal Processing","volume":"40 10","pages":"2051-2063"},"PeriodicalIF":3.8,"publicationDate":"2026-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149125942","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 Event-Triggered Resilient Control for Nonlinear MASs Under Unknown FDI Attacks via Reinforcement Learning","authors":"Hao Wu,&nbsp;Dui Liu","doi":"10.1002/acs.70114","DOIUrl":"https://doi.org/10.1002/acs.70114","url":null,"abstract":"<div>\u0000 \u0000 <p>In this paper, an adaptive resilient control scheme based on reinforcement learning (RL) is proposed for the control of nonlinear multi-agent systems (MASs) under false data injection (FDI) attacks. When the system sensors are subject to unknown FDI attacks, traditional controller design methods are challenged by the inability to directly access all the original state information. To address this challenge, a novel coordinate-error construction and Nussbaum-type functions are incorporated into the control design, effectively attenuating the adverse impact of FDI attacks. Meanwhile, an adaptive event-triggered mechanism (AETM) is developed to substantially reduce network communication burden. The controller is synthesized by integrating a critic function with an actor-critic neural network-based RL algorithm, enabling accurate online estimation of lumped uncertainties. Using Lyapunov stability theory, it is rigorously proven that the closed-loop system is stable and that all signals remain bounded. Simulation results further corroborate the effectiveness and robustness of the proposed scheme, demonstrating improved control performance under FDI attacks with reduced communication load.</p>\u0000 </div>","PeriodicalId":50347,"journal":{"name":"International Journal of Adaptive Control and Signal Processing","volume":"40 10","pages":"1972-1983"},"PeriodicalIF":3.8,"publicationDate":"2026-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149125939","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":"Relaxed Excitation-Based Distributed Adaptive Parameter Estimator With Communication Delays","authors":"Tushar Garg,&nbsp;Sayan Basu Roy","doi":"10.1002/acs.70119","DOIUrl":"https://doi.org/10.1002/acs.70119","url":null,"abstract":"<div>\u0000 \u0000 <p>In this articles, a novel distributed adaptive parameter estimation/identification algorithm for multi-agent system (MAS) architectures is proposed, where parameter convergence is ensured under a relaxed mathematical condition called cooperative initial excitation (C-IE). In this article, a MAS architecture is modeled as a strongly connected digraph network incorporating interagent communication delays. Classical distributed adaptive identifiers require a restrictive mathematical condition called cooperative persistence of excitation (C-PE) for parameter convergence. The C-PE condition is restrictive in contrast to the C-IE since it requires the excitation/richness of information over the entire time span, unlike C-IE needing the excitation only in the initial time span. The proposed algorithm employs a recently introduced weighted integrator dynamics, eliminating the need for computationally complex multiple switching mechanisms found in past literature while still ensuring parameter convergence. It guarantees uniform global exponential stability (without communication delays) and uniform global stability with asymptotic convergence (with communication delays) of the origin of the parameter estimation error dynamics, respectively. Simulation results further validate the efficacy of the proposed algorithm when compared with the state-of-the-art methods. To the best of the authors' knowledge, this is the first work on relaxed excitation based distributed adaptive identification over a strongly connected digraph, offering stability guarantees in the presence of communication delays.</p>\u0000 </div>","PeriodicalId":50347,"journal":{"name":"International Journal of Adaptive Control and Signal Processing","volume":"40 10","pages":"2076-2091"},"PeriodicalIF":3.8,"publicationDate":"2026-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149125805","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":"Minimum-Energy Iterative Learning Control for Multi-Agent Systems With Optimal Intermediate-Point Allocation","authors":"Zhihe Zhuang,&nbsp;Chenhui Zhou,&nbsp;Hongfeng Tao,&nbsp;Yiyang Chen,&nbsp;Wojciech Paszke,&nbsp;Eric Rogers","doi":"10.1002/acs.70113","DOIUrl":"https://doi.org/10.1002/acs.70113","url":null,"abstract":"<div>\u0000 \u0000 <p>Iterative learning control applies to applications in which the same finite-duration task is repeated, with each instance termed a trial. The objective is to track a specified reference trajectory over a finite duration, termed the trial length. In some applications, such as multi-agent systems, tracking at each instant or point over the trial length is not required; only at selected points is it required, known as point-to-point iterative learning control. This article develops a new point-to-point design in which the points requiring tracking vary from trial to trial, and the solution minimizes energy, which is relevant to systems with a limited power budget. Also, an algorithm is developed to improve computational efficiency by sharing the burden among the agents forming the system. A numerical case study highlights the benefits of the new design.</p>\u0000 </div>","PeriodicalId":50347,"journal":{"name":"International Journal of Adaptive Control and Signal Processing","volume":"40 10","pages":"1946-1955"},"PeriodicalIF":3.8,"publicationDate":"2026-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149125789","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 Novel Robust Stability Framework for Impulsive Discrete-Time Switched Stochastic Hopfield Neural Networks","authors":"Rui Wu,&nbsp;Ting Cai,&nbsp;Xiaohui Yan","doi":"10.1002/acs.70115","DOIUrl":"https://doi.org/10.1002/acs.70115","url":null,"abstract":"<div>\u0000 \u0000 <p>This paper investigates robust exponential stability and <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> control for a class of discrete-time switched stochastic Hopfield neural networks with impulsive effects. First, a unified analysis framework is established for discrete-time switched stochastic impulsive systems. By combining Lyapunov theory with the average dwell-time approach, sufficient conditions in terms of linear matrix inequalities (LMIs) are derived to guarantee robust exponential stability and 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> disturbance attenuation. Then, these results are extended to switched stochastic Hopfield neural networks with impulsive effects by incorporating sector conditions on the activation functions, leading to tractable state-feedback controller design criteria. The developed method explicitly captures the coupled influence of switching, stochastic disturbances, and impulsive behavior. Finally, the effectiveness of the proposed method is verified through numerical examples of photovoltaic energy storage charging stations and networked unmanned vehicle systems.</p>\u0000 </div>","PeriodicalId":50347,"journal":{"name":"International Journal of Adaptive Control and Signal Processing","volume":"40 10","pages":"2035-2050"},"PeriodicalIF":3.8,"publicationDate":"2026-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149125941","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":"Neural Network-Based Adaptive Fixed-Time Fault-Tolerant Control for Robotic Manipulators","authors":"Chen Wang,&nbsp;Qixiang Wang,&nbsp;Gang Shen,&nbsp;Tao Chao,&nbsp;Jianhui Wang,&nbsp;Qing Guo","doi":"10.1002/acs.70122","DOIUrl":"https://doi.org/10.1002/acs.70122","url":null,"abstract":"<div>\u0000 \u0000 <p>This paper proposes a neural-network-based adaptive fixed-time fault-tolerant control (AFTFTC) scheme for robotic manipulators with unknown dynamics and actuator faults. A radial basis function neural network (RBFNN) is first employed to approximate the unknown nonlinear dynamics, an adaptive law is designed to estimate an unknown scalar parameter associated with the norm of the ideal neural-network weights. To enhance the fault-tolerance capability, an additional adaptive law is developed to compensate for unknown actuator faults. By integrating these adaptive mechanisms into the backstepping control framework, the proposed AFTFTC controller is constructed to handle both normal and faulty operating conditions. Theoretical analysis shows that both the joint tracking errors and the adaptive parameter estimation errors converge to a small neighborhood of zero within a fixed time, independent of the initial conditions. Comparative experiments on a robotic manipulator are carried out to verify the improved tracking accuracy, robustness, and fault-tolerant performance of the proposed method.</p>\u0000 </div>","PeriodicalId":50347,"journal":{"name":"International Journal of Adaptive Control and Signal Processing","volume":"40 10","pages":"2064-2075"},"PeriodicalIF":3.8,"publicationDate":"2026-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149125767","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":"Fixed-Time Adaptive Learning Control for Output-Constrained Nonlinear Strict-Feedback Systems","authors":"Xinqi Sun,&nbsp;Junqiang Li,&nbsp;Niuniu Ma,&nbsp;Weitian He","doi":"10.1002/acs.70118","DOIUrl":"https://doi.org/10.1002/acs.70118","url":null,"abstract":"<div>\u0000 \u0000 <p>This paper investigates the adaptive dynamics learning and control problem for a class of nonlinear strict-feedback systems subject to output constraints. First, an output constraint transformation framework is introduced to equivalently convert the constrained system into an unconstrained form. Based on this framework, an adaptive fixed-time filtered learning control strategy is developed, which guarantees closed-loop stability while strictly enforcing the prescribed output constraints. Next, under the persistent excitation condition, a radial basis function neural network (RBFNN) is employed to accurately approximate the unknown nonlinear dynamics. Furthermore, the learned dynamic information is incorporated into the control law design to construct a learning-based controller, thereby effectively improving the transient performance and robustness of the system. Rigorous Lyapunov analysis is conducted to prove the uniform boundedness of all closed-loop signals and the strict satisfaction of the output constraints. Finally, simulation results are presented to demonstrate the effectiveness and superiority of the proposed adaptive constrained dynamics learning and control approach.</p>\u0000 </div>","PeriodicalId":50347,"journal":{"name":"International Journal of Adaptive Control and Signal Processing","volume":"40 10","pages":"1997-2008"},"PeriodicalIF":3.8,"publicationDate":"2026-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149125820","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}