International Journal of Adaptive Control and Signal Processing最新文献_第8页

Robust Time-Varying Estimators Using Weighted Fusion for Multisensor Networked Systems With Missing Observations and Two-Step Random Delays 基于加权融合的缺失观测和两步随机延迟多传感器网络系统鲁棒时变估计

IF 3.8 4区计算机科学

International Journal of Adaptive Control and Signal Processing Pub Date : 2026-03-16 Epub Date: 2025-11-30 DOI: 10.1002/acs.4105

Zhenzhen Gu, Wenqiang Liu

{"title":"Robust Time-Varying Estimators Using Weighted Fusion for Multisensor Networked Systems With Missing Observations and Two-Step Random Delays","authors":"Zhenzhen Gu, 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}

引用次数: 0

Quantized Iterative Learning Control for Consensus of Nonlinear Impulsive Multi-Agent Systems With Inter-Channel Encoding-Decoding Mechanisms and Packet Dropouts 具有信道间编解码机制和丢包的非线性脉冲多智能体系统一致性的量化迭代学习控制

IF 3.8 4区计算机科学

International Journal of Adaptive Control and Signal Processing Pub Date : 2026-03-16 Epub Date: 2025-12-01 DOI: 10.1002/acs.70017

Hongjin Zhang, JinRong Wang, Dong Shen

{"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, JinRong Wang, 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}

引用次数: 0

Adaptive Fuzzy Fixed-Time Attitude Tracking Control for Rigid Spacecraft With Input Delay 具有输入延迟的刚性航天器自适应模糊定时姿态跟踪控制

IF 3.8 4区计算机科学

International Journal of Adaptive Control and Signal Processing Pub Date : 2026-03-16 Epub Date: 2025-12-03 DOI: 10.1002/acs.70012

Huanqing Wang, Yaqi Feng

引用次数: 0

Secure Adaptive Trajectory Tracking Control for Spacecraft Attitude Systems With Unknown FDI Attacks and Disturbance 具有未知FDI攻击和干扰的航天器姿态系统安全自适应轨迹跟踪控制

IF 3.8 4区计算机科学

International Journal of Adaptive Control and Signal Processing Pub Date : 2026-03-16 Epub Date: 2025-12-07 DOI: 10.1002/acs.70020

Kangkang Sun, Zhiheng Liang, Qi Zheng

引用次数: 0

A Bayesian Approach to Total Least-Squares in Perturbed Compressive Sensing 扰动压缩感知中总最小二乘的贝叶斯方法

IF 3.8 4区计算机科学

International Journal of Adaptive Control and Signal Processing Pub Date : 2026-03-16 Epub Date: 2025-11-25 DOI: 10.1002/acs.70006

Junlin Li, Weiwei Sun, Feng Ding, Renzhi Lu

引用次数: 0

Formation Control Design and Optimization for Uncertain Underactuated Robots Team Using Cooperative Game Theory 基于合作博弈论的不确定欠驱动机器人群体控制设计与优化

IF 3.8 4区计算机科学

International Journal of Adaptive Control and Signal Processing Pub Date : 2026-03-16 Epub Date: 2025-12-07 DOI: 10.1002/acs.70019

Chunsheng He, Jian Zhao, Ke Shao

{"title":"Formation Control Design and Optimization for Uncertain Underactuated Robots Team Using Cooperative Game Theory","authors":"Chunsheng He, Jian Zhao, Ke Shao","doi":"10.1002/acs.70019","DOIUrl":"https://doi.org/10.1002/acs.70019","url":null,"abstract":"<div>\u0000 \u0000 <p>This manuscript introduces an innovative approach to address the formation control challenges for a specific category of underactuated robots (UARs). It transforms the formation control problem into a constraint-following problem with three basic constraints: (1) The configuration constraints (CCs), which are to induce behaviors for each UAR. (2) The formation constraints (FCs), which are used to maintain the team formation. (3) The trajectory constraints (TCs), which are imposed to make the team move with a predefined formation. The UAR team is assumed to suffer from time-varying while bounded uncertainty, and the uncertainty bound is unknown. Invoking an interval method, we build a dynamic model for the uncertain UAR team. Considering the three basic constraints in a unified form, we then employ an improved adaptive formation controller based on an extended Udwadia-control (UC) scheme for the uncertain UAR team. The controller can guarantee the UAR team deterministic convergence, which is verified by an explicit Lyapunov analysis. As there are three tunable control gains in the proposed controller, this paper adopts a cooperative game (CG) algorithm to obtain the optimal formation control. Regarding the tunable parameters as three players in a CG with their cost functions representing the tracking performance or the energy cost, the optimal formation control is realized by achieving an equilibrium among players. Simulations on a UAR team consisting of three inverted pendulum robots (IPRs) are performed to test the designed optimal formation controller.</p>\u0000 </div>","PeriodicalId":50347,"journal":{"name":"International Journal of Adaptive Control and Signal Processing","volume":"40 3","pages":"692-709"},"PeriodicalIF":3.8,"publicationDate":"2026-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147563873","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

Finite-Time Sliding Mode Control for IT2 Fuzzy Nonhomogeneous Markovian Switched Systems: An Asynchronous Observer-Based Approach IT2模糊非齐次马尔可夫切换系统的有限时间滑模控制：基于异步观测器的方法

IF 3.8 4区计算机科学

International Journal of Adaptive Control and Signal Processing Pub Date : 2026-03-16 Epub Date: 2025-11-25 DOI: 10.1002/acs.70005

Yao Wang

引用次数: 0

Adaptive Neural Fixed-Time Command Filtered Control for Stochastic Nonlinear Systems With Input Quantization 输入量化随机非线性系统的自适应神经定时命令滤波控制

IF 3.8 4区计算机科学

International Journal of Adaptive Control and Signal Processing Pub Date : 2026-03-16 Epub Date: 2025-11-30 DOI: 10.1002/acs.70009

Long Gu, Lidong Wang, Xiaoping Liu, Aosen Zhang

{"title":"Adaptive Neural Fixed-Time Command Filtered Control for Stochastic Nonlinear Systems With Input Quantization","authors":"Long Gu, Lidong Wang, Xiaoping Liu, Aosen Zhang","doi":"10.1002/acs.70009","DOIUrl":"https://doi.org/10.1002/acs.70009","url":null,"abstract":"<div>\u0000 \u0000 <p>In this paper, an adaptive neural network fixed-time control method based on command filtering is proposed for stochastic nonlinear systems with input quantization. The method utilizes a radial basis function neural network (RBFNN) to approximate the unknown nonlinear terms in the system. The jitter problem is effectively solved in the controller design by configuring the hysteresis quantization inputs as two bounded nonlinear functions. In addition, the fixed-time control strategy is combined with the command filtering method to probabilistically guarantee the fixed-time stability of the closed-loop system, which overcomes the “complexity explosion” and “singularity” problems in the classical backstepping design. Meanwhile, a new error compensation mechanism is designed to effectively compensate for the filtering error. The theoretical analysis proves that the control scheme can ensure that the output tracking error of the closed-loop system converges to a sufficiently small neighborhood of the origin in a fixed time and that all signals within the closed-loop system are bounded. Finally, the feasibility and effectiveness of the scheme are verified by two simulation examples.</p>\u0000 </div>","PeriodicalId":50347,"journal":{"name":"International Journal of Adaptive Control and Signal Processing","volume":"40 3","pages":"565-578"},"PeriodicalIF":3.8,"publicationDate":"2026-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147570269","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 Optimal Control of Nonlinear Systems via Disturbance Observer-Based Reinforcement Learning Backstepping Approach 基于扰动观测器的非线性系统强化学习反步方法的事件触发最优控制

IF 3.8 4区计算机科学

International Journal of Adaptive Control and Signal Processing Pub Date : 2026-03-16 Epub Date: 2025-11-30 DOI: 10.1002/acs.70014

Ming-Yue Li, Bo Xu, Zhongsheng Hou

引用次数: 0

Predictive Optimal Iterative Learning Control for Nonlinear Systems Using the Koopman Operator 基于Koopman算子的非线性系统预测最优迭代学习控制

IF 3.8 4区计算机科学

International Journal of Adaptive Control and Signal Processing Pub Date : 2026-03-16 Epub Date: 2025-11-26 DOI: 10.1002/acs.70008

Xinyue Tao, Hongfeng Tao, Luyuan Gao, Wojciech Paszke, Eric Rogers

引用次数: 0