International Journal of Adaptive Control and Signal Processing最新文献_第3页

Adaptive Fault-Tolerant Control for Stochastic Nonlinear Systems With Unknown Output Dead-Zones 输出死区未知的随机非线性系统的自适应容错控制

IF 3.8 4区计算机科学

International Journal of Adaptive Control and Signal Processing Pub Date : 2026-09-02 Epub Date: 2026-05-04 DOI: 10.1002/acs.70102

Yixuan Yuan, Liping Xie, Junsheng Zhao, Kanjian Zhang

{"title":"Adaptive Fault-Tolerant Control for Stochastic Nonlinear Systems With Unknown Output Dead-Zones","authors":"Yixuan Yuan, Liping Xie, Junsheng Zhao, Kanjian Zhang","doi":"10.1002/acs.70102","DOIUrl":"https://doi.org/10.1002/acs.70102","url":null,"abstract":"<div>\u0000 \u0000 <p>The present paper investigates the fault-tolerant control problem for nonlinear systems with stochastic noise and dead-zone output. In contrast to the extant literature, the present work effectively compensates for the impact of unknown dead-zone output on the system by constructing a dead-zone output approximation model and introducing a Nussbaum-type function. Furthermore, considering the multiple effects of stochastic noise and actuator faults, this paper employs a fault-tolerant control strategy to reduce the risk of system performance degradation and ensure system safety. On this basis, an efficient and readily deployable fault-tolerant control scheme is proposed through further integration of adaptive techniques and fuzzy logic systems. The stability of the system is thoroughly analyzed by constructing a Lyapunov function and incorporating desired performance criteria, which proves the stability of the proposed control scheme. The efficacy and practicability of the proposed control scheme are substantiated by two simulation illustrations.</p>\u0000 </div>","PeriodicalId":50347,"journal":{"name":"International Journal of Adaptive Control and Signal Processing","volume":"40 9","pages":"1794-1806"},"PeriodicalIF":3.8,"publicationDate":"2026-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148872012","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}

引用次数: 0

Fault Estimation and Fault-Tolerant Consensus Tracking Control for Nonlinear Multi-Agent Systems With Exogenous Disturbances 外源扰动非线性多智能体系统的故障估计与容错一致性跟踪控制

IF 3.8 4区计算机科学

International Journal of Adaptive Control and Signal Processing Pub Date : 2026-09-02 Epub Date: 2026-05-16 DOI: 10.1002/acs.70106

Sheng Wang, Xiuhua Liu, Jian Han, Lewei Dong

引用次数: 0

Piecewise Lyapunov Function–Based Robust Model Predictive Control for Asymmetric Input-Saturated Systems with External Disturbances 具有外部干扰的非对称输入饱和系统的分段Lyapunov函数鲁棒模型预测控制

IF 3.8 4区计算机科学

International Journal of Adaptive Control and Signal Processing Pub Date : 2026-09-02 Epub Date: 2026-05-08 DOI: 10.1002/acs.70103

Yuying Wu, Langwen Zhang, Wei Xie

{"title":"Piecewise Lyapunov Function–Based Robust Model Predictive Control for Asymmetric Input-Saturated Systems with External Disturbances","authors":"Yuying Wu, Langwen Zhang, Wei Xie","doi":"10.1002/acs.70103","DOIUrl":"https://doi.org/10.1002/acs.70103","url":null,"abstract":"<div>\u0000 \u0000 <p>Asymmetric input saturation, resulting from physical limits or safety requirements, is prevalent in industrial systems. This poses a significant challenge for control design. However, few studies have been done from the perspective of predictive control for uncertain systems. The conventional symmetric saturation approximation method leads to conservative performance. To address this gap, this paper proposes a novel robust model predictive control (RMPC) scheme for systems with model uncertainty and disturbance under asymmetric saturation. By partitioning the state space according to the control law, the asymmetric input-saturated system is equivalently transformed into a piecewise model, where each sub-model has a symmetric deadzone. This allows for full consideration of the actual boundaries of asymmetric saturation. Then, an infinite-horizon RMPC optimization design is developed, incorporating common or piecewise Lyapunov functions. The stability conditions and constraints are established based on the piecewise model. Due to the exploitation of the asymmetric saturation range, the proposed RMPC approach can achieve less conservative solutions. Finally, recursive feasibility and closed-loop stability of the proposed optimization problems are ensured through set invariance theory and Lyapunov analysis. The results of simulations, including a double integrator and a reactor-separator process, confirm that the proposed approach can effectively improve dynamic performance compared to existing methods.</p>\u0000 </div>","PeriodicalId":50347,"journal":{"name":"International Journal of Adaptive Control and Signal Processing","volume":"40 9","pages":"1818-1835"},"PeriodicalIF":3.8,"publicationDate":"2026-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148871959","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}

引用次数: 0

Adaptive Observer for Coupled Wave PDE and Infinite ODE With Sampled Data and Unknown Input: Application to Brain Hemodynamics Estimation 具有采样数据和未知输入的耦合波偏微分方程和无限偏微分方程的自适应观测器：在脑血流动力学估计中的应用

IF 3.8 4区计算机科学

International Journal of Adaptive Control and Signal Processing Pub Date : 2026-09-02 Epub Date: 2026-06-10 DOI: 10.1002/acs.70104

Zehor Belkhatir, Sarah Mechhoud, Taous-Meriem Laleg-Kirati

{"title":"Adaptive Observer for Coupled Wave PDE and Infinite ODE With Sampled Data and Unknown Input: Application to Brain Hemodynamics Estimation","authors":"Zehor Belkhatir, Sarah Mechhoud, Taous-Meriem Laleg-Kirati","doi":"10.1002/acs.70104","DOIUrl":"https://doi.org/10.1002/acs.70104","url":null,"abstract":"<p>In this article, we investigate the joint estimation problem of the states and the in-domain Partial Differential Equation (PDE) parameters along with the unknown time-varying input for coupled wave PDE and infinite-dimensional Ordinary Differential Equation (ODE) systems. A finite set of measurements described by the sampled-in-space state of the ODE system is available to recover the distributed states along with the unknown parameters and input. Through the proposition of an appropriate Lyapunov functional the convergence proof is shown. The proposed Lyapunov functional considers lifted state solutions belonging to more regularized Hilbert spaces. Sufficient conditions on the observer's and adaptation laws' gains, along with the measurements' maximum sampling interval, are derived to guarantee the practical convergence of state estimation errors (uniform ultimate boundedness of the state estimation errors). The proposed observer is applied to characterize the spatiotemporal hemodynamic response in the brain from voxel-wise functional magnetic resonance imaging (fMRI) measurements. Numerical simulations are provided to demonstrate the efficiency of the theoretical findings.</p>","PeriodicalId":50347,"journal":{"name":"International Journal of Adaptive Control and Signal Processing","volume":"40 9","pages":"1932-1944"},"PeriodicalIF":3.8,"publicationDate":"2026-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/acs.70104","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148872159","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}

引用次数: 0

Fault-Tolerant Control and Disturbance Rejection for Polynomial Fault Systems Under Actuator Saturation 执行器饱和下多项式故障系统的容错控制与抗干扰

IF 3.8 4区计算机科学

International Journal of Adaptive Control and Signal Processing Pub Date : 2026-08-06 Epub Date: 2026-03-26 DOI: 10.1002/acs.70087

Changyuan Ji, Guochen Pang, Xiangyong Chen, Yang Liu, Jianlong Qiu, Jinde Cao

{"title":"Fault-Tolerant Control and Disturbance Rejection for Polynomial Fault Systems Under Actuator Saturation","authors":"Changyuan Ji, Guochen Pang, Xiangyong Chen, Yang Liu, Jianlong Qiu, Jinde Cao","doi":"10.1002/acs.70087","DOIUrl":"https://doi.org/10.1002/acs.70087","url":null,"abstract":"<div>\u0000 \u0000 <p>This paper addresses the fault-tolerant control problem for polynomial fault systems under actuator saturation. The fault is modeled as a polynomial function, while the unknown disturbance is generated by a known external system. An observer is used to simultaneously estimate the disturbance, fault, and system state. Furthermore, since saturation in real-world systems is not solely due to physical limitations of components, an anti-saturation compensator is introduced to compensate for the input and to address the coupling between the fault and saturation. An <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msub>\u0000 <mrow>\u0000 <mi>L</mi>\u0000 </mrow>\u0000 <mrow>\u0000 <mn>2</mn>\u0000 </mrow>\u0000 </msub>\u0000 </mrow>\u0000 <annotation>$$ {L}_2 $$</annotation>\u0000 </semantics></math> gain is used to limit the energy transfer from the saturation error to the system, ultimately achieving anti-saturation fault-tolerant control. Finally, two practical examples validate the effectiveness of the proposed method.</p>\u0000 </div>","PeriodicalId":50347,"journal":{"name":"International Journal of Adaptive Control and Signal Processing","volume":"40 8","pages":"1562-1572"},"PeriodicalIF":3.8,"publicationDate":"2026-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148696346","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}

引用次数: 0

Adaptive Neural Architecture Reduction for Robust Modeling of Complex Systems 复杂系统鲁棒建模的自适应神经结构约简

IF 3.8 4区计算机科学

International Journal of Adaptive Control and Signal Processing Pub Date : 2026-08-06 Epub Date: 2026-04-17 DOI: 10.1002/acs.70094

Kartik Saini, Narendra Kumar, Bharat Bhushan, Rajesh Kumar

{"title":"Adaptive Neural Architecture Reduction for Robust Modeling of Complex Systems","authors":"Kartik Saini, Narendra Kumar, Bharat Bhushan, Rajesh Kumar","doi":"10.1002/acs.70094","DOIUrl":"https://doi.org/10.1002/acs.70094","url":null,"abstract":"<div>\u0000 \u0000 <p>This study presents a novel approach for identifying nonlinear dynamical systems by developing a modified Jordan recurrent neural network (MJRNN) model. The proposed MJRNN is an extended version of the standard Jordan recurrent neural network (JRNN) architecture. In addition, an adaptive pruning strategy based on the significance of hidden neurons is introduced for artificial neural network (ANN) models to reduce computational complexity and enhance learning performance. The proposed pruning algorithm removes less significant hidden neurons during the training process, resulting in a more compact network structure. Two simulation examples are presented to evaluate the effectiveness of the proposed model. The performance of the MJRNN model is compared with conventional ANN architectures, including JRNN, DRNN, and FFNN, under both fixed and adaptive neuron configurations. The simulation results demonstrate that the proposed MJRNN model with the adaptive pruning strategy achieves superior performance compared with the other ANN models.</p>\u0000 </div>","PeriodicalId":50347,"journal":{"name":"International Journal of Adaptive Control and Signal Processing","volume":"40 8","pages":"1640-1655"},"PeriodicalIF":3.8,"publicationDate":"2026-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148696417","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}

引用次数: 0

Modeling and Control of Nonlinear Systems Using an Improved Adaptive Moment Optimization Approach 基于改进自适应矩优化方法的非线性系统建模与控制

IF 3.8 4区计算机科学

International Journal of Adaptive Control and Signal Processing Pub Date : 2026-08-06 Epub Date: 2026-04-21 DOI: 10.1002/acs.70097

Kartik Saini, Narendra Kumar, Bharat Bhushan, Rajesh Kumar

{"title":"Modeling and Control of Nonlinear Systems Using an Improved Adaptive Moment Optimization Approach","authors":"Kartik Saini, Narendra Kumar, Bharat Bhushan, Rajesh Kumar","doi":"10.1002/acs.70097","DOIUrl":"https://doi.org/10.1002/acs.70097","url":null,"abstract":"<div>\u0000 \u0000 <p>A modified diagonal recurrent neural network (MDRNN) model is presented in this paper as a model that allows the modeling and control of nonlinear dynamic systems. In practical applications, neural network models may experience challenges such as slow convergence and limited generalization performance when applied to complex nonlinear dynamic systems. These limitations have been overcome via improvements to the original diagonal recurrent neural network (DRNN), in addition to enhancing adaptation capability and learning performance. Moreover, this paper also presents another better optimization strategy, which is the improved adaptive moment optimization (IAdam) strategy, an enhanced structure of the traditional Adam (adaptive moment estimation) optimization strategy. The proposed IAdam optimization method is able to dynamically adjust the weight variables of the proposed model so that it can be trained efficiently, and the model can be more adaptable under various circumstances in the system. The MDRNN model is analyzed in detail in many nonlinear dynamical systems, where it outperforms the standard neural network models both in terms of its accuracy and convergence rates. An indirect control scheme is used to implement an MDRNN-based controller to operate a nonlinear dynamical system. The dynamics of the proposed controller are simulated by varying the parameters and using a disturbance signal to determine the ability of the controller to recover. The simulation results show that our methodology offers both an accurate and consistent method of identification and control of complex dynamical systems, and therefore, it is a very useful tool in real applications of control systems, signal processing, and time series forecasting.</p>\u0000 </div>","PeriodicalId":50347,"journal":{"name":"International Journal of Adaptive Control and Signal Processing","volume":"40 8","pages":"1709-1730"},"PeriodicalIF":3.8,"publicationDate":"2026-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148696481","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}

引用次数: 0

Event-Triggered Data-Driven Adaptive Iterative Learning Containment Control for Multi-Agent Systems via Fading Channels Under Cyber-Attacks 网络攻击下衰落信道多智能体系统的事件触发数据驱动自适应迭代学习控制

IF 3.8 4区计算机科学

International Journal of Adaptive Control and Signal Processing Pub Date : 2026-08-06 Epub Date: 2026-04-16 DOI: 10.1002/acs.70093

Feng-Shuo Tian, Dong Liu, Xian-Duo Li, Jun-Sheng Wang

{"title":"Event-Triggered Data-Driven Adaptive Iterative Learning Containment Control for Multi-Agent Systems via Fading Channels Under Cyber-Attacks","authors":"Feng-Shuo Tian, Dong Liu, Xian-Duo Li, Jun-Sheng Wang","doi":"10.1002/acs.70093","DOIUrl":"https://doi.org/10.1002/acs.70093","url":null,"abstract":"<div>\u0000 \u0000 <p>This article investigates the problem of model-free adaptive iterative learning containment control for nonlinear multi-agent systems (MASs) with fading measurements against periodic denial-of-service (DoS) attacks. The stochastic process is utilized to model periodic DoS attacks. Based on the historical data, a compensation algorithm is devised to minimize the adverse effects caused by such attacks. The design of the distributed controller only utilizes the data from neighboring nodes of each agent after undergoing random attenuation. Additionally, a dynamic event-triggering mechanism is incorporated along the iterative axis to make communication load less and exhibits adaptability more. Building upon this foundation, a novel model-free adaptive iterative learning containment control method is proposed, which enables all followers to converge within the convex hull formed by the leader even in the presence of DoS attacks. Finally, two simulations are introduced to depict the availability of the provided design approach.</p>\u0000 </div>","PeriodicalId":50347,"journal":{"name":"International Journal of Adaptive Control and Signal Processing","volume":"40 8","pages":"1625-1639"},"PeriodicalIF":3.8,"publicationDate":"2026-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148696563","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}

引用次数: 0

Eigenvalue Characterization of Persistence of Excitation for Convex Adaptive Predictive Control 凸自适应预测控制激励持续性的特征值表征

IF 3.8 4区计算机科学

International Journal of Adaptive Control and Signal Processing Pub Date : 2026-08-06 Epub Date: 2026-04-16 DOI: 10.1002/acs.70092

Bernardo A. Hernandez Vicente

{"title":"Eigenvalue Characterization of Persistence of Excitation for Convex Adaptive Predictive Control","authors":"Bernardo A. Hernandez Vicente","doi":"10.1002/acs.70092","DOIUrl":"https://doi.org/10.1002/acs.70092","url":null,"abstract":"<div>\u0000 \u0000 <p>Adaptive model predictive control (MPC) has emerged as a viable solution to the dual-control problem, which refers to the combined challenges of control and system identification. To guarantee estimates convergence, several architectures employ some form of persistence of excitation (PoE), an input design requirement that translates into a nonlinear time-varying constraint with generally nonconvex solution set. However, this is seldom addressed structurally in the context of adaptive MPC. In this article, we provide new insights over its requirements and time-varying nature, within the context of noise-free linear time-invariant systems in state feedback. We first recast the PoE constraint as a bilinear matrix inequality and implement two distinct convexification approaches that approximate its nonconvex solution. We then employ an eigenvalue analysis to characterize an additional approximation and an explicit geometric index that relates to the achievable size of the solution set within a constrained input space. Numerical simulations show a (saturated) linear relationship between the geometric index and the size of solution sets, both true and approximated. More importantly, the time-varying characteristic of the PoE constraint dominates the size of the convex approximations, independent of the principle behind convexification.</p>\u0000 </div>","PeriodicalId":50347,"journal":{"name":"International Journal of Adaptive Control and Signal Processing","volume":"40 8","pages":"1614-1624"},"PeriodicalIF":3.8,"publicationDate":"2026-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148696370","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}

引用次数: 0

Distributed UIO for Multiple Sensor Systems With Unknown Inputs and Measurement Noise 具有未知输入和测量噪声的多传感器系统的分布式UIO

IF 3.8 4区计算机科学

International Journal of Adaptive Control and Signal Processing Pub Date : 2026-08-06 Epub Date: 2026-04-23 DOI: 10.1002/acs.70089

Fanglai Zhu, Mingwei Li, Tianbiao Shi, Xufeng Ling

{"title":"Distributed UIO for Multiple Sensor Systems With Unknown Inputs and Measurement Noise","authors":"Fanglai Zhu, Mingwei Li, Tianbiao Shi, Xufeng Ling","doi":"10.1002/acs.70089","DOIUrl":"https://doi.org/10.1002/acs.70089","url":null,"abstract":"<div>\u0000 \u0000 <p>In the article, a distributed unknown input observer (DUIO) constructing technique for a system with unknown input (UI) and measurement noise (MN) is come up with. Specially, multiple sensor groups are equipped to measure the output of the target system, and the output information measured by each sensor group is not enough for estimating the system states individually. It is assumed that each sensor group suffers from MN. To begin with, an output transformation and a state shift transformation are made such that the transformed subsystem contains neither UI nor MN. Next, for each transformed subsystem, a distributed state observer is designed to obtain the asymptotic convergent state estimation, and also the MN reconstruction (MNR). Furthermore, an output interval estimation method is proposed, and a correlation of the UI and the state is come up with algebraically. Then, by using the relationship, an asymptotic convergent UI reconstruction (UIR) method is developed. Finally, a simulation example is given to verify the effectiveness of the proposed method.</p>\u0000 </div>","PeriodicalId":50347,"journal":{"name":"International Journal of Adaptive Control and Signal Processing","volume":"40 8","pages":"1743-1754"},"PeriodicalIF":3.8,"publicationDate":"2026-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148696574","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}

引用次数: 0