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International Journal of Adaptive Control and Signal Processing最新文献

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Adaptive IDA-PBC for Underactuated Mechanical Systems With Unknown Time-Varying Disturbances 未知时变扰动欠驱动机械系统的自适应IDA-PBC
IF 3.8 4区 计算机科学
International Journal of Adaptive Control and Signal Processing Pub Date : 2026-07-07 Epub Date: 2026-03-08 DOI: 10.1002/acs.70076
Jose Guadalupe Romero, Emmanuel Nuño
{"title":"Adaptive IDA-PBC for Underactuated Mechanical Systems With Unknown Time-Varying Disturbances","authors":"Jose Guadalupe Romero,&nbsp;Emmanuel Nuño","doi":"10.1002/acs.70076","DOIUrl":"https://doi.org/10.1002/acs.70076","url":null,"abstract":"<div>\u0000 \u0000 <p>Interconnection and Damping Assignment Passivity-Based Control (IDA-PBC) has been one of the most employed algorithms to stabilize underactuated port-Hamiltonian systems. However, IDA-PBC cannot handle input disturbances. Disturbances can cause a drift in the desired equilibrium and can generate unstable behaviors. In this paper, we propose to include an adaptive term to estimate the disturbance in the IDA-PBC for underactuated mechanical systems. Under the assumption that the disturbance is composed of a finite sum of sinusoidal functions with <i>unknown</i> frequencies and amplitudes, we design an estimation algorithm that is based on the internal model for which we estimate, exponentially, the frequencies and the magnitudes of the disturbances. As it is typical in IDA-PBC, we show that the desired control objective is achieved provided that the desired output is detectable.</p>\u0000 </div>","PeriodicalId":50347,"journal":{"name":"International Journal of Adaptive Control and Signal Processing","volume":"40 7","pages":"1401-1410"},"PeriodicalIF":3.8,"publicationDate":"2026-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148395174","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
Auxiliary Model Hierarchical Iterative Parameter Estimation for Autoregressive Output-Error Models 自回归输出误差模型的辅助模型层次迭代参数估计
IF 3.8 4区 计算机科学
International Journal of Adaptive Control and Signal Processing Pub Date : 2026-07-07 Epub Date: 2026-03-18 DOI: 10.1002/acs.70081
Feng Ding, Hao Fang, Ling Xu
{"title":"Auxiliary Model Hierarchical Iterative Parameter Estimation for Autoregressive Output-Error Models","authors":"Feng Ding,&nbsp;Hao Fang,&nbsp;Ling Xu","doi":"10.1002/acs.70081","DOIUrl":"https://doi.org/10.1002/acs.70081","url":null,"abstract":"<div>\u0000 \u0000 <p>By means of the auxiliary model identification idea and the multi-innovation identification theory, this paper investigates iterative parameter identification methods for autoregressive output-error models, and proposed an auxiliary model (hierarchical) gradient-based iterative (GI) algorithm, an auxiliary model (hierarchical) least-squares-based iterative (LSI) algorithm, an auxiliary model (hierarchical) multi-innovation GI algorithm, an auxiliary model (hierarchical) multi-innovation LSI algorithm by using the hierarchical identification principle. The proposed hierarchical iterative identification methods can be extended to other linear and nonlinear scalar and multivariable stochastic systems with colored noises.</p>\u0000 </div>","PeriodicalId":50347,"journal":{"name":"International Journal of Adaptive Control and Signal Processing","volume":"40 7","pages":"1486-1513"},"PeriodicalIF":3.8,"publicationDate":"2026-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148395201","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
Robust Adaptive Prescribed Performance Control for Manipulators With Actuator Saturation and Uncertain Surging Disturbance 具有不确定脉动扰动和执行器饱和的机械臂鲁棒自适应预定性能控制
IF 3.8 4区 计算机科学
International Journal of Adaptive Control and Signal Processing Pub Date : 2026-07-07 Epub Date: 2026-03-23 DOI: 10.1002/acs.70085
Xinwei Wang, Chenglin Liu
{"title":"Robust Adaptive Prescribed Performance Control for Manipulators With Actuator Saturation and Uncertain Surging Disturbance","authors":"Xinwei Wang,&nbsp;Chenglin Liu","doi":"10.1002/acs.70085","DOIUrl":"https://doi.org/10.1002/acs.70085","url":null,"abstract":"<div>\u0000 \u0000 <p>This paper proposes a fixed-time sliding mode prescribed performance control for robotic manipulators with actuator saturation, parametric uncertainties, and external disturbance. Taking the situation that the disturbance may surge temporarily into consideration, we propose a revised performance function capable of adapting according to the saturation status. It overcomes the inherent defect of the traditional prescribed performance function, that is, the singularity problem occurs when the constrained control input fails to maintain the tracking error within the performance funnel. Finally, simulation results are presented to validate and assess the effectiveness of the proposed method.</p>\u0000 </div>","PeriodicalId":50347,"journal":{"name":"International Journal of Adaptive Control and Signal Processing","volume":"40 7","pages":"1535-1546"},"PeriodicalIF":3.8,"publicationDate":"2026-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148395181","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
Enhanced Fault Detection in Induction Motors via Complex-Value Spatio-Temporal Graph Convolutional Neural Networks and High Level Target Navigation Pigeon Inspired Optimization 基于复值时空图卷积神经网络和高阶目标导航鸽优化的异步电机故障检测
IF 3.8 4区 计算机科学
International Journal of Adaptive Control and Signal Processing Pub Date : 2026-07-07 Epub Date: 2026-03-10 DOI: 10.1002/acs.70025
M. S. Saranya, P. Vijayapriya
{"title":"Enhanced Fault Detection in Induction Motors via Complex-Value Spatio-Temporal Graph Convolutional Neural Networks and High Level Target Navigation Pigeon Inspired Optimization","authors":"M. S. Saranya,&nbsp;P. Vijayapriya","doi":"10.1002/acs.70025","DOIUrl":"https://doi.org/10.1002/acs.70025","url":null,"abstract":"<div>\u0000 \u0000 <p>Induction Motors (IM) are ideal for a wide variety of industrial applications because they require less maintenance and run with strength and durability. Fault detection in IM is critical for ensuring industrial system dependability and preventing unexpected downtime and expensive repairs. Traditional fault detection approaches frequently struggle to capture the complex geographical and temporal patterns inherent in motor fault signals, which limit their efficiency. To address these challenges, this manuscript proposes a novel approach that integrates Complex-Value Spatio-Temporal Graph Convolutional Neural Networks (CVSTGCNN) with High-level Target Navigation Pigeon Inspired Optimization (HTNPIO) for optimized fault detection and classification, named CVSTGCNN-HTNPIO. The CVSTGCNN effectively models the intricate Spatio-temporal dependencies in induction motor fault data, while HTNPIO optimizes the network parameters to enhance detection accuracy and efficiency. The major goal of the proposed technique is to differentiate between electrical issues that arise in IM under both faulty and healthy circumstances. When tested on a benchmark induction motor fault dataset, the proposed CVSTGCNN-HTNPIO method achieves a high classification accuracy of 99.8% and reduces the root mean square error (RMSE) to 0.02%, outperforming existing techniques such as Spatial–Temporal Recurrent Graph Neural Networks (STRGNN), Multi-Parallel Graph Convolutional Network (MGCN), Particle Swarm Optimization (PSO), Back Propagation Neural Networks (BPNN), and Artificial Neural Networks (ANN). These findings illustrate the method's enhanced capacity to identify various fault types with more precision, allowing for more reliable and timely motor fault diagnostics. This development has the potential to greatly improve motor operational safety, lower maintenance costs, and increase equipment longevity.</p>\u0000 </div>","PeriodicalId":50347,"journal":{"name":"International Journal of Adaptive Control and Signal Processing","volume":"40 7","pages":"1424-1440"},"PeriodicalIF":3.8,"publicationDate":"2026-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148395196","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 Proportional-Derivative Sliding Mode Control for a Quadrotor With Suspended Double-Pendulum Payload 悬挂式双摆载荷四旋翼自适应比例导数滑模控制
IF 3.8 4区 计算机科学
International Journal of Adaptive Control and Signal Processing Pub Date : 2026-07-07 Epub Date: 2026-03-06 DOI: 10.1002/acs.70074
Bing Yan, Yun Zou, Guangyu Wei, Hong Lin, Xiaoshan Ma
{"title":"Adaptive Proportional-Derivative Sliding Mode Control for a Quadrotor With Suspended Double-Pendulum Payload","authors":"Bing Yan,&nbsp;Yun Zou,&nbsp;Guangyu Wei,&nbsp;Hong Lin,&nbsp;Xiaoshan Ma","doi":"10.1002/acs.70074","DOIUrl":"https://doi.org/10.1002/acs.70074","url":null,"abstract":"<div>\u0000 \u0000 <p>Precise quadrotor positioning and payload swing suppression play an important part in a quadrotor aerial transportation system. However, in most practical scenarios, existing control methods fail to achieve satisfactory performance, mainly because these methods often neglect the double-pendulum effect caused by the relative rotational motion of the hook and the cargo. To address this characteristic of the suspended double-pendulum payload, we first propose an estimator to online estimation uncertain system parameters without knowing exact system knowledge. Then, we develop an adaptive proportional-derivative sliding mode control, where an additional term is introduced in the sliding surface to suppress payload swing and enhance coupling between states. Finally, based on the Lyapunov method and LaSalle's invariance principle, the asymptotic stability of the closed-loop system is proven without linearizing the original nonlinear dynamics or neglecting certain coupling terms.</p>\u0000 </div>","PeriodicalId":50347,"journal":{"name":"International Journal of Adaptive Control and Signal Processing","volume":"40 7","pages":"1386-1400"},"PeriodicalIF":3.8,"publicationDate":"2026-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148395025","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
A Novel State Estimation Approach for Non-Smooth Sandwich System With Rate-Dependent Hysteresis 一种具有速率相关滞后的非光滑夹层系统状态估计新方法
IF 3.8 4区 计算机科学
International Journal of Adaptive Control and Signal Processing Pub Date : 2026-07-07 Epub Date: 2026-03-16 DOI: 10.1002/acs.70077
Haifen Li, Zixuan Han, Lianwei Ma, Dequan Liu
{"title":"A Novel State Estimation Approach for Non-Smooth Sandwich System With Rate-Dependent Hysteresis","authors":"Haifen Li,&nbsp;Zixuan Han,&nbsp;Lianwei Ma,&nbsp;Dequan Liu","doi":"10.1002/acs.70077","DOIUrl":"https://doi.org/10.1002/acs.70077","url":null,"abstract":"<div>\u0000 \u0000 <p>Piezoelectric actuators exhibit dependency of the output displacement on the rate of change of the applied input voltage. In this paper, a new rate-dependent hysteresis (dynamic hysteresis) model for piezoelectric actuators is proposed. The proposed dynamic hysteresis model can accurately approximate the hysteretic characteristics of the system when the input signal frequency changes. Subsequently, based on the dynamic hysteresis model, a novel non-smooth Kalman filter (NKF) is designed for sandwich systems with dynamic hysteresis so as to estimate system states. This method can automatically switch between linear area and memory area according to operating state. Additionally, the stability of the proposed non-smooth Kalman filter is discussed. Finally, experimental results are presented to validate the effectiveness of the proposed state estimation method.</p>\u0000 </div>","PeriodicalId":50347,"journal":{"name":"International Journal of Adaptive Control and Signal Processing","volume":"40 7","pages":"1463-1472"},"PeriodicalIF":3.8,"publicationDate":"2026-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148395056","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 Fuzzy Reinforcement Learning-Based Optimal Backstepping Control for Fractional-Order Nonlinear Systems With Full-State Constraints and Input Saturation 具有全状态约束和输入饱和的分数阶非线性系统的自适应模糊强化学习最优反演控制
IF 3.8 4区 计算机科学
International Journal of Adaptive Control and Signal Processing Pub Date : 2026-07-07 Epub Date: 2026-03-09 DOI: 10.1002/acs.70079
Chunzhi Yang, Qingni Yang, Funing Lin
{"title":"Adaptive Fuzzy Reinforcement Learning-Based Optimal Backstepping Control for Fractional-Order Nonlinear Systems With Full-State Constraints and Input Saturation","authors":"Chunzhi Yang,&nbsp;Qingni Yang,&nbsp;Funing Lin","doi":"10.1002/acs.70079","DOIUrl":"https://doi.org/10.1002/acs.70079","url":null,"abstract":"<div>\u0000 \u0000 <p>This paper presents an adaptive fuzzy backstepping control approach utilizing reinforcement learning (RL) for fractional-order (FO) strict-feedback nonlinear systems with full-state constraints and input saturation. The RL-based identifier-critic-actor architecture is constructed via fuzzy logic systems (FLSs), which is applied to induce the approximate optimal controller. The proposed RL algorithm not only ensures rapid convergence of the adaptive updating weight parameters, but also removes the rigorous requirement for parameter adaptation under persistent excitation condition. Compared to the classic gradient descent method for training the weight update laws of FLSs, the proposed scheme simplifies the computational complexity. An improved FO filter is introduced to overcome the “complexity explosion” problem caused by the traditional backstepping designs. A barrier Lyapunov function is introduced at each step of the recursive process to avoid states exceeding the desired constraints. According to the FO Lyapunov stability theory, it is proven that all states remain within the required constraints and that all closed-loop signals are bounded. The feasibility of the presented algorithm is further verified through comparative simulation.</p>\u0000 </div>","PeriodicalId":50347,"journal":{"name":"International Journal of Adaptive Control and Signal Processing","volume":"40 7","pages":"1411-1423"},"PeriodicalIF":3.8,"publicationDate":"2026-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148395193","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
Predefined-Time Control for PMSM Systems With Full-State Error Constraints 带全状态误差约束的永磁同步电机系统的预定义时间控制
IF 3.8 4区 计算机科学
International Journal of Adaptive Control and Signal Processing Pub Date : 2026-07-07 Epub Date: 2026-03-24 DOI: 10.1002/acs.70083
Weiqi Liu, Shuai Sui, C. L. Philip Chen
{"title":"Predefined-Time Control for PMSM Systems With Full-State Error Constraints","authors":"Weiqi Liu,&nbsp;Shuai Sui,&nbsp;C. L. Philip Chen","doi":"10.1002/acs.70083","DOIUrl":"https://doi.org/10.1002/acs.70083","url":null,"abstract":"<div>\u0000 \u0000 <p>This paper investigates the problem of fuzzy adaptive predefined time full-state error constraints for permanent magnet synchronous motor systems. This study employs fuzzy logic systems (FLSs) to handle unknown nonlinear dynamic functions. A state observer based on fuzzy logic systems is utilized to estimate unmeasurable states. By utilizing a general potential Lyapunov function, predefined-time stability theory, combined with dynamic surface control (DSC) technique and backstepping recursive technique, a predefined time full-state error constraints control strategy is proposed. By applying predefined time Lyapunov stability theory, it is proven that all signals in the closed-loop systems remain bounded and the tracking error converges within a predefined time. Finally, the effectiveness and feasibility of the proposed method are verified through simulation results.</p>\u0000 </div>","PeriodicalId":50347,"journal":{"name":"International Journal of Adaptive Control and Signal Processing","volume":"40 7","pages":"1547-1559"},"PeriodicalIF":3.8,"publicationDate":"2026-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148395005","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
A New Adaptive Tracking Control Strategy of Systems With Nontriangular Structure, Full State Constraints, and Dead-Zone Input 非三角形结构、全状态约束和死区输入系统的自适应跟踪控制策略
IF 3.8 4区 计算机科学
International Journal of Adaptive Control and Signal Processing Pub Date : 2026-07-07 Epub Date: 2026-03-19 DOI: 10.1002/acs.70084
Zhuang Wen, Ya-Zhu Zhao, Zhen-Guo Liu, Zong-Yao Sun
{"title":"A New Adaptive Tracking Control Strategy of Systems With Nontriangular Structure, Full State Constraints, and Dead-Zone Input","authors":"Zhuang Wen,&nbsp;Ya-Zhu Zhao,&nbsp;Zhen-Guo Liu,&nbsp;Zong-Yao Sun","doi":"10.1002/acs.70084","DOIUrl":"https://doi.org/10.1002/acs.70084","url":null,"abstract":"<div>\u0000 \u0000 <p>This work addresses a challenging adaptive tracking issue for uncertain nonlinear systems, featuring a nontriangular structure, full state constraints, and dead-zone input. A new constrained adaptive control approach is presented by exploring the adding one power integration and fuzzy control. With the selection of a suitable barrier Lyapunov function, we design an adaptive controller, which guarantees the desired tracking accuracy and the boundedness of all system signals. Besides, the system states do not violate the constraint. The proposed method is verified using numerical and practical examples.</p></div>","PeriodicalId":50347,"journal":{"name":"International Journal of Adaptive Control and Signal Processing","volume":"40 7","pages":"1524-1534"},"PeriodicalIF":3.8,"publicationDate":"2026-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148395034","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
Interval Estimation for Switched Systems With Measurement-Dependent Mode Uncertainty 具有测量依赖模式不确定性的切换系统的区间估计
IF 3.8 4区 计算机科学
International Journal of Adaptive Control and Signal Processing Pub Date : 2026-06-07 Epub Date: 2026-02-25 DOI: 10.1002/acs.70062
Xinyang Liu, Zhenhua Wang, Tarek Raïssi, Yi Shen
{"title":"Interval Estimation for Switched Systems With Measurement-Dependent Mode Uncertainty","authors":"Xinyang Liu,&nbsp;Zhenhua Wang,&nbsp;Tarek Raïssi,&nbsp;Yi Shen","doi":"10.1002/acs.70062","DOIUrl":"https://doi.org/10.1002/acs.70062","url":null,"abstract":"<div>\u0000 \u0000 <p>This paper proposes an interval estimation method for discrete-time switched systems with measurement-dependent mode uncertainty. The proposed method is realized based on reachability analysis and measurement update. In order to solve the inaccurate measurement value judgment mode problem, a new reachability analysis algorithm, based on zonotopes and boxes, is proposed to obtain the guaranteed mode and guaranteed state estimation. To improve interval estimation accuracy, a 1-norm optimization method is proposed to reduce the conservatism of computing intersections, and a method based on boxes is proposed to reduce the interval conservatism caused by the union computation process.</p>\u0000 </div>","PeriodicalId":50347,"journal":{"name":"International Journal of Adaptive Control and Signal Processing","volume":"40 6","pages":"1337-1347"},"PeriodicalIF":3.8,"publicationDate":"2026-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148194209","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
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