IEEE Transactions on Cybernetics最新文献

{"title":"Safe Multistage Reconfiguration Motion Control for Self-Reconfigurable Swarm.","authors":"Biao Liu, Rongqin Mo, Tao Jiang, Jiangshuai Huang, Zhenshan Bing, Alois Knoll","doi":"10.1109/TCYB.2026.3732237","DOIUrl":"10.1109/TCYB.2026.3732237","url":null,"abstract":"<p><p>Modular self-reconfigurable robot (MSRR) swarms offer significant potential for structural adaptation in unstructured environments. However, achieving efficient and precise reconfiguration is challenging, as the process must transform scattered individuals into cohesive assemblies while satisfying decentralized coordination, nonholonomic motion constraints, and strict terminal pose requirements. To address these challenges, this work presents a systematic three-stage distributed framework for autonomous reconfiguration. First, feasible assembly poses are generated and assigned through a decentralized negotiation mechanism, enabling the swarm to determine target configurations without a central controller. Second, a safety-aware multirobot trajectory planning method is formulated to generate collision-free trajectories under nonholonomic kinematic constraints and terminal position-orientation constraints. Finally, a vision-based alignment strategy is designed to ensure high-precision docking at the terminal stage. Simulation and real-world experimental results demonstrate the practical feasibility, favorable scalability, and reliable reconfiguration performance of the proposed framework under the tested conditions.</p>","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"PP ","pages":""},"PeriodicalIF":11.3,"publicationDate":"2026-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148989892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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":"Stability Analysis and Asynchronous Control of Almost Periodic Piecewise Linear Systems With Nonfixed Periods and Uncertain Subinterval Lengths.","authors":"Zhuolin Tan, Xiaochen Xie, Jiantai Huang","doi":"10.1109/TCYB.2026.3731270","DOIUrl":"10.1109/TCYB.2026.3731270","url":null,"abstract":"<p><p>This article investigates the stability analysis and asynchronous control issues of almost periodic piecewise linear systems (APPLSs) with nonfixed periods and uncertain subinterval lengths. Unlike strictly periodic piecewise dynamic systems in previous studies, we establish new sufficient conditions using Lyapunov stability theory because there is no fixed fundamental period and each subinterval length may vary. These conditions constrain the proportion of stable and unstable subintervals to guarantee stability. Given a maximum switching delay between the controller and the subsystems, we propose a state-feedback asynchronous control approach to ensure that the closed-loop system achieves exponential stability, even when a larger proportion of subsystems are unstable. This article also considers the possibility of rapid switching between subsystems. We develop three asynchronous controller design (ACD) algorithms for cases where some subsystems are unstabilizable, all subsystems are stabilizable, and all subsystems are unstabilizable. Finally, illustrative simulations using numerical examples and a tunnel diode circuit system verify the rationality and effectiveness of the proposed approach.</p>","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"PP ","pages":""},"PeriodicalIF":11.3,"publicationDate":"2026-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149038355","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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":"Worst Case Learning for H _∞ Consensus for Heterogeneous Multiagent Systems: A Resource-Constrained Game Approach.","authors":"Xingyu Gao, Zhihong Liu, Choon Ki Ahn, Zhengrong Xiang","doi":"10.1109/TCYB.2026.3730815","DOIUrl":"10.1109/TCYB.2026.3730815","url":null,"abstract":"<p><p>This article investigates heterogeneous high-order nonlinear multiagent systems (MASs) under an event-triggered mechanism (ETM). A reinforcement learning (RL)-based zero-sum game (ZSG) approach is employed to solve the robust consensus problem. To handle heterogeneity, unified-dimension state variables are constructed, auxiliary variables are defined, and consensus errors are reformulated accordingly. By applying the min-max principle, the optimal $H_{infty } $ consensus problem is transformed into a ZSG problem, and an approximately optimal consensus protocol is derived by solving the Hamilton-Jacobi-Isaacs (HJI) equation with the proposed actor-critic-identifier (ACI) learning framework. In particular, a novel ACI network is developed to approximate the HJI solution, while a hybrid distributed ETM with adaptive dynamic thresholds is designed to avoid redundant communication and computation. An error-oriented Lyapunov method is used to establish convergence bounds for the consensus error and to exclude the Zeno behavior. Numerical simulations validate the effectiveness of the proposed algorithm.</p>","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"PP ","pages":""},"PeriodicalIF":11.3,"publicationDate":"2026-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149038388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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":"Saturated Precise Robust Singular Spectrum Analysis-Diffusion Model Predictive Control of AUVs.","authors":"Lie Xu, Daxiong Ji, Huiping Li","doi":"10.1109/TCYB.2026.3731311","DOIUrl":"10.1109/TCYB.2026.3731311","url":null,"abstract":"<p><p>Accurate path following by autonomous underwater vehicles (AUVs) in wave-dominated environments is limited by two coupled engineering constraints: feedback-driven disturbance observers (DOBs) exhibit phase lag, whereas aggressive cancellation of high-frequency components can exceed the vehicle and actuator bandwidths. This article develops a closed-loop predictive compensation architecture, termed singular spectrum analysis-diffusion (S²D)-model predictive control (MPC) that integrates a S²D observer, a dynamics-informed adaptive filter (DIAF), and MPC. The observer combines instantaneous Kalman-filter (KF) estimates, singular spectrum analysis (SSA)-based trend-residual decomposition, and conditional skip-step diffusion to forecast the disturbance sequence over the MPC horizon. The DIAF then applies saturation and bandwidth-aware filtering so that only physically actionable components enter the feedforward channel. This perception-prediction-decision-action loop retains constraint handling while converting delayed disturbance estimates into phase-leading compensation. Regional input-to-state practical stability (RISPS) is established for the resulting closed loop. Under JONSWAP wave disturbances, the proposed method yields the lowest integrated absolute error (2.09 m $cdot $ s), an average execution time of 33.25 ms for a 50-ms sampling interval, and integral absolute error (IAE) values of 2.054-2.136 across the tested sea states.</p>","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"PP ","pages":""},"PeriodicalIF":11.3,"publicationDate":"2026-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149038427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 Knowledge-Data Design of Fuzzy Rule-Based Models.","authors":"Witold Pedrycz","doi":"10.1109/TCYB.2026.3729902","DOIUrl":"10.1109/TCYB.2026.3729902","url":null,"abstract":"<p><p>Machine learning (ML) models, including rule-based architectures dominant in the technology of fuzzy sets, are constructed with the aid of intensive usage of data. Quite often, knowledge about the system/problem is available and offers some tangible benefits to the construction of the models. The relevance of knowledge becomes of paramount importance when the data do not fully represent the problem and are not distributed across the overall input space. Under these circumstances, data-based ML models will very likely fail when being deployed in a real-world environment. The current intensively studied paradigm of physics-informed ML has emerged to support the design of models augmented by available knowledge describing some global relationships, invariants, and regularities stemming from the underlying physics of the phenomenon/system. It has already been shown that through such holistic data-knowledge perception and ensuing synergistic development, the performance of resulting ML models visibly improved. The impactful and far-reaching objective of this study is to establish an original knowledge-data unified design environment. We demonstrate and quantify how ML models are built by minimizing a carefully generalized (extended) loss function involving both data and knowledge (with the latter being conveyed in the form of a so-called reference model). We develop an additive loss function that exhibits flexibility, and its hyperparameter controls a tradeoff between the reliance on data and knowledge. A systematic way of optimizing the hyperparameter in the loss function and striking a balance between the guidance delivered by data and knowledge is investigated. Along with a general development methodology, the study introduces an original and detailed way of constructing fuzzy rule-based models when taking advantage of data and knowledge. The detailed design processes of building conditions of rules with the aid of the modified fuzzy clustering and an extended optimization of linear functions forming the conclusions of the rules are discussed. Illustrative examples are covered, showcasing the substantial reduction of the values of the loss function to about 25%-30% over the performance of only data-based constructed ML models.</p>","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"PP ","pages":""},"PeriodicalIF":11.3,"publicationDate":"2026-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149038245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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":"Frequency-Shaped Natural Actor-Critic for Online Optimal Tracking Control of Unknown Stochastic Systems.","authors":"Haoran Zhang, Chunhui Zhao, Zheng-Guang Wu","doi":"10.1109/TCYB.2026.3729224","DOIUrl":"10.1109/TCYB.2026.3729224","url":null,"abstract":"<p><p>In industrial and robotic control tasks, reference trajectories often concentrate within specific frequency bands, whereas noise and disturbances may dominate other frequency ranges. Conventional reinforcement learning (RL) methods typically employ time-domain cost functions that weight all frequency components equally, leading to excessive sensitivity to high-frequency noise and limited tracking accuracy. This limitation restricts the applicability of RL to time-varying trajectory-tracking problems. To address this issue, we propose a frequency-shaped implicit online natural actor-critic (FS-IONAC) approach that incorporates frequency-domain specifications into the RL framework. First, we theoretically show that the standard quadratic cost function inherently assigns uniform weighting across the frequency spectrum. To overcome this drawback, we introduce stable shaping filters centered on task-relevant frequencies and embed their dynamics into an augmented Markov decision process (MDP), thereby inducing a frequency-shaped objective while preserving the Markov property. Then, we develop a model-free natural actor-critic algorithm with implicit critic updates to learn the optimal tracking policy online without requiring knowledge of the system dynamics. We further prove that the proposed algorithm converges almost surely under two-timescale stochastic approximation conditions. The effectiveness of FS-IONAC is validated through illustrative examples, including a robotic control task, where it achieves improved tracking performance and sample efficiency compared with conventional baselines.</p>","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"PP ","pages":""},"PeriodicalIF":11.3,"publicationDate":"2026-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149038348","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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":"Fast Fixed-Time Scaled Bipartite Consensus Control for High-Order Multiagent Systems With Time-Varying Extended State Observer.","authors":"Shaoping Chang, Jiayi Wu, Xiaoyuan Luo, Xinping Guan","doi":"10.1109/TCYB.2026.3731052","DOIUrl":"10.1109/TCYB.2026.3731052","url":null,"abstract":"<p><p>This article focuses on the scaled bipartite consensus control for high-order multiagent systems (MASs) based on fast fixed-time stability with a novel active disturbance rejection control (ADRC) strategy. First, a time-varying fast fixed-time extended state observer (TVFFxTESO) is designed to observe the unknown states and disturbances, which can increase the observation speed and accuracy while reducing the peak value (PV). Moreover, a fast fixed-time distributed observer (FFxTDO) is proposed to realize the estimation of the leader's state by each follower, to obtain its expected reference trajectory. Furthermore, a fast fixed-time control protocol is constructed for each follower to achieve scaled bipartite consensus. Simulation results, including a numerical example and an application to wheeled mobile robots (WMRs), validate that the proposed method can effectively improve the dynamic and steady-state control performance.</p>","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"PP ","pages":""},"PeriodicalIF":11.3,"publicationDate":"2026-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149038295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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":"Data-Driven Trajectory Tracking Control Design for Unknown Linear Systems via a Nonminimal State-Space Model.","authors":"Xinyu Wang, Fei Dong, Qinglei Hu, Jianying Zheng, Dongyu Li","doi":"10.1109/TCYB.2026.3713789","DOIUrl":"10.1109/TCYB.2026.3713789","url":null,"abstract":"<p><p>This work investigates the zero-error tracking problem for linear systems with unknown dynamics. The proposed method integrates a data-driven system model with an auxiliary system for error characterization. First, a nonminimal state-space (NMSS) model is constructed from historical input-output data. Then, an auxiliary system is formulated by leveraging the common minimal polynomial shared by the disturbance and reference trajectory. The stabilizability and detectability of the auxiliary system are rigorously verified, thereby guaranteeing the existence of a unique positive semidefinite solution to the Riccati equation. Inspired by reinforcement learning (RL) principles, a data-driven output-feedback optimal tracking controller is developed by iteratively learning the optimal Q-function. The theoretical superiority of the proposed controller over existing approaches is established. Simulation results further demonstrate its effectiveness in addressing the tracking problem subject to measurement delays and unmeasurable disturbances.</p>","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"PP ","pages":""},"PeriodicalIF":11.3,"publicationDate":"2026-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148887295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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":"Event-Triggered Self-Learning Parallel Tracking Control for Continuous-Time Nonlinear Systems With Actuator Faults.","authors":"Wendi Chen, Qinglai Wei, Shanshan Jiao, Yu Liu, Jun Xiao","doi":"10.1109/TCYB.2026.3725996","DOIUrl":"10.1109/TCYB.2026.3725996","url":null,"abstract":"<p><p>This article explores an event-triggered self-learning parallel tracking control strategy for continuous-time nonlinear systems with actuator faults. To mitigate the impact of actuator faults, an enhanced performance index function is developed based on the unknown upper bound of such faults. Furthermore, an augmented error nonlinear system is constructed by incorporating the tracking error and control input of the original system, so as to implement parallel tracking control via the adaptive dynamic programming (ADP) approach. By designing an adaptive law, the unknown upper bound of faults is estimated online, which adjusts the control parameters in real-time and compensates for the loss of control effectiveness induced by faults. A critic network is employed to approximate the optimal value function within an event-triggered framework, while a dynamic triggering mechanism is adopted to reduce communication overhead. Through the design of appropriate Lyapunov functions, the asymptotic stability of the closed-loop system is rigorously proven, and the weight estimation error is demonstrated to be uniformly ultimately bounded (UUB). Finally, the simulation results are provided to validate the effectiveness of the proposed control strategy.</p>","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"PP ","pages":""},"PeriodicalIF":11.3,"publicationDate":"2026-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148887367","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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":"Safety-Aware Optimal Tracking Control of Stochastic Systems With Obstacle Avoidance via Adaptive Dynamic Programming.","authors":"Lulu Zhang, Huaguang Zhang, Xiaohui Yue, Yushan Wang","doi":"10.1109/TCYB.2026.3724282","DOIUrl":"10.1109/TCYB.2026.3724282","url":null,"abstract":"<p><p>Safe trajectory tracking for nonlinear stochastic systems operating in obstacle-cluttered environments remains a significant challenge, as random disturbances and obstacle-induced constraints can simultaneously degrade tracking accuracy and threaten system safety. To overcome this issue, this article develops a safety-aware optimal tracking control framework that integrates stochastic control barrier functions (CBFs) with adaptive dynamic programming (ADP) for obstacle avoidance. To ensure safety, a logarithmic-type stochastic CBF is constructed to enforce obstacle-avoidance constraints, and theoretical guarantees are provided for the stochastic forward invariance (SFI) of the safe set. Furthermore, based on the integral reinforcement learning (RL) framework, an ADP algorithm is developed to relax the need for system dynamics. A critic-only neural network (NN) scheme is employed to approximate the solution of the Hamilton-Jacobi-Bellman (HJB) equation, with a fixed-time weight update rule established to guarantee convergence independent of initial conditions. Meanwhile, an experience replay mechanism is incorporated to relax the persistent excitation condition. It is further shown that the estimation error of the NN weights is fixed-time stable (FxTS). Finally, simulation results demonstrate that the designed method achieves optimal trajectory tracking while ensuring safety under stochastic dynamics, even in scenarios involving multiple obstacles.</p>","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"PP ","pages":""},"PeriodicalIF":11.3,"publicationDate":"2026-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148880068","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}