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Online Fault-Tolerant Tracking Control With Adaptive Critic for Nonaffine Nonlinear systems

IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 215-227页

作者： Ding Wang Lingzhi Hu Xiaoli Li Junfei Qiao IEEE the School of Information Science and Technology Beijing Key Laboratory of Computational Intelligence and Intelligent SystemBeijing Laboratory of Smart Environmental Protection and Beijing Institute of Artificial Intelligence Beijing University of Technology

In this paper, a fault-tolerant-based online critic learning algorithm is developed to solve the optimal tracking control issue for nonaffine nonlinear systems with actuator ***, a novel augmented plant is constructed by fusing the system state and the reference trajectory, which aims to transform the optimal fault-tolerant tracking control design with actuator faults into the optimal regulation problem of the conventional nonlinear error system. Subsequently, in order to ensure the normal execution of the online learning algorithm, a stability criterion condition is created to obtain an initial admissible tracking policy. Then, the constructed model neural network(NN) is pretrained to recognize the system dynamics and calculate trajectory control. The critic and action NNs are constructed to output the approximate cost function and approximate tracking control,respectively. The Hamilton-Jacobi-Bellman equation of the error system is solved online through the action-critic framework. In theoretical analysis, it is proved that all concerned signals are uniformly ultimately bounded according to the Lyapunov *** tracking control law can approach the optimal tracking control within a finite approximation error. Finally, two experimental examples are conducted to indicate the effectiveness and superiority of the developed fault-tolerant tracking control scheme.

关键词： Adaptive critic control neural network (NN) nonaffine nonlinear systems online fault-tolerant tracking design uniform ultimate boundedness

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Design of a Bio-inspired Extensible Continuum Manipulator with Variable Stiffness

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Journal of Bionic Engineering 2025年第1期22卷 181-194页

作者： Dongbao Sui Sikai Zhao Tianshuo Wang Yubin Liu Yanhe Zhu Jie Zhao State Key Laboratory of Robotics and System Harbin Institute of TechnologyHarbin150001China

This paper presents a continuum manipulator inspired by the anatomical characteristics of the elephant ***,the manipulator mimics the conoid profile of the elephant trunk,which helps to enhance its *** design features two concentric parts:inner pneumatically actuated bellows and an outer tendon-driven helical *** tendons control the omnidirectional bending of the manipulator,while the fusion of the pneumatic bellows with the tendon-driven spring results in an antagonistic actuation mechanism that provides the manipulator with variable stiffness and *** paper presents a new design for extensible manipulator and analyzes its stiffness and motion *** results are consistent with theoretical analysis,thereby demonstrating the validity of the theoretical approach and the versatile practical mechanical properties of the continuum *** impressive extensibility and variable stiffness of the manipulator were further demonstrated by performing a pin-hole assembly task.

关键词： Bioinspired robots Continuum robots Soft robotics Variable stiffness

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Fractal autoencoder with redundancy regularization for unsupervised feature selection

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Science China(Information Sciences) 2025年第2期68卷 89-102页

作者： Meiting SUN Fangyu LI Honggui HAN School of Information Science and Technology Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Engineering Research Center of Digital Community Ministry of Education Beijing Institute of Artificial Intelligence

Feature selection is a crucial step in data preprocessing because feature selection reduces the dimensionality of data by eliminating irrelevant and redundant features. Since manual labeling is expensive, unsupervised feature selection has received increasing attention in recent years. However, existing unsupervised feature selection methods tend to prioritize selecting highly correlated features over exploring feature diversity. Thus, a regularized fractal autoencoder(RFAE) method is proposed to select informative features in an unsupervised way. Specifically, the fractal autoencoder network extends autoencoders to construct a correspondence neural network and a selection neural network. The correspondence neural network exploits interfeature correlations and the selection neural network selects the informative features. A redundancy regularization strategy consists of a redundancy elimination regularization term based on the dependency between features and a sparse regularization term based on the group lasso. The redundancy regularization strategy eliminates feature subset redundancy and enhances network generalization ability. Extensive experimental results on six publicly available datasets show that the proposed RFAE outperforms the compared methods regarding clustering accuracy and classification accuracy. Moreover, the proposed RFAE achieves acceptable computation efficiency.

关键词： unsupervised feature selection fractal autoencoder correspondence neural network selection neural network redundancy regularization strategy

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Hierarchical Learning-Based Integrated Robust Optimal Control for Nonlinear systems

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IEEE Transactions on systems, Man, and Cybernetics: systems 2025年第5期55卷 3119-3129页

作者： Zhang, Jiacheng Wang, Jingjing Han, Honggui Hou, Ying Huang, Yanting Beijing University of Technology Faculty of Information Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing100124 China

The optimal control of nonlinear systems is crucial to improve system performance. However, the uncertainties of cost functions and systems dynamics make it difficult to solve the optimal control laws. To cope with this problem, a hierarchical learning-based integrated robust optimal control (HL-IROC) method is proposed in this article. The merits of the proposed HL-IROC method are three aspects: First, a hierarchical learning-based integrated optimal control (HL-IOC) scheme, contains a system dynamic learning layer and a cost function learning layer, is designed to transform the original optimal control problem into an integrated optimization problem with control laws as decision variables. Then, the relationships between cost functions and control laws are captured to overcome the difficulties brought by uncertainties in the optimal control process. Second, a global-local cooperative robust evolutionary optimization (GL-CREO) algorithm is proposed to obtain the optimal control laws. Then, a global-local robust searching strategy is employed to deal with two types of uncertainties for improving the robustness of control laws. Third, the convergence analysis of HL-IOC and GL-CREO is discussed in theory. In the experiments, the effectiveness of HL-IROC is illustrated with a nonlinear system and a wastewater treatment process. © 2013 IEEE.

关键词： Optimal control systems

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Unifying Fixed Time and Prescribed Time Control for Strict-Feedback Nonlinear systems

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IEEE/CAA Journal of Automatica Sinica 2025年第2期12卷 347-355页

作者： Xiang Chen Yujuan Wang Yongduan Song the State Key Laboratory of Power Transmission Equipment & System Security and New Technology and School of Automation Chongqing University IEEE

This paper investigates the prescribed-time tracking control problem for a class of multi-input multi-output(MIMO)nonlinear strict-feedback systems subject to non-vanishing uncertainties. The inherent unmatched and non-vanishing uncertainties make the prescribed-time control problem become much more nontrivial. The solution to address the challenges mentioned above involves incorporating a prescribed-time filter, as opposed to a finite-time filter, and formulating a prescribed-time Lyapunov stability lemma(Lemma 5). The prescribed-time Lyapunov stability lemma is based on time axis shifting time-varying yet bounded gain, which establishes a novel link between the fixed-time and prescribed-time control method. This allows the restriction condition that the time-varying gain function must satisfy as imposed in most exist prescribed-time control works to be removed. Under the proposed control method, the desire trajectory is ensured to closely track the output of the system in prescribed time. The effectiveness of the theoretical results are verified through numerical simulation.

关键词： Prescribed-time control prescribed-time filter robust control state feedback strict-feedback systems

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Event-Triggered Robust Parallel Optimal Consensus Control for Multiagent systems

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 40-53页

作者： Qinglai Wei Shanshan Jiao Qi Dong Fei-Yue Wang IEEE the Institute of Systems Engineering Macau University of Science and Technology the State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences the School of Artificial Intelligence University of Chinese Academy of Sciences China Academy of Electronics and Information Technology the State Key Laboratory of Multimodal Artificial Intelligence Systems the School of Artificial Intelligence University of Chinese Academy of Sciences

This paper highlights the utilization of parallel control and adaptive dynamic programming(ADP) for event-triggered robust parallel optimal consensus control(ETRPOC) of uncertain nonlinear continuous-time multiagent systems(MASs).First, the parallel control system, which consists of a virtual control variable and a specific auxiliary variable obtained from the coupled Hamiltonian, allows general systems to be transformed into affine systems. Of interest is the fact that the parallel control technique's introduction provides an unprecedented perspective on eliminating the negative effects of disturbance. Then, an eventtriggered mechanism is adopted to save communication resources while ensuring the system's stability. The coupled HamiltonJacobi(HJ) equation's solution is approximated using a critic neural network(NN), whose weights are updated in response to events. Furthermore, theoretical analysis reveals that the weight estimation error is uniformly ultimately bounded(UUB). Finally,numerical simulations demonstrate the effectiveness of the developed ETRPOC method.

关键词： Adaptive dynamic programming (ADP) critic neural network (NN) event-triggered control optimal consensus control robust control

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Lightweight multifunctional metamaterial with low-frequency vibroacoustic reduction and load-bearing performances

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Applied Mathematics and Mechanics(English Edition) 2025年第3期46卷 403-422页

作者： Qi JIA Dianlong YU Donghai HAN Jihong WEN National Key Laboratory of Equipment State Sensing and Smart Support College of Intelligence Science and TechnologyNational University of Defense TechnologyChangsha 410073China

Metamaterials can control and manipulate acoustic/elastic waves on a subwavelength scale using cavities or additional ***,the large cavity and weak stiffness components of traditional metamaterials may cause a conflict between vibroacoustic reduction and load-bearing capacity,and thus limit their ***,we propose a lightweight multifunctional metamaterial that can simultaneously achieve low-frequency sound insulation,broadband vibration reduction,and excellent load-bearing performance,named as vibroacoustic isolation and bearing metamaterial(VIBM).The advent of additive manufacturing technology provides a convenient and reliable method for the fabrication of VIBM *** results show that the compressive strength of the VIBM is as high as 9.71 MPa,which is nearly 87.81%higher than that of the conventional grid structure(CGS)under the same volume ***,the vibration and sound transmission are significantly reduced over a low and wide frequency range,which agrees well with the experimental data,and the reduction degree is obviously larger than that obtained by the *** design strategy can effectively realize the key components of metamaterials and improve their application scenarios.

关键词： multifunctional metamaterial low-frequency sound insulation broadband vibration reduction load bearing

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MetaCoorNet: an improved generated residual network for grasping pose estimation

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Science China(Information Sciences) 2025年第3期68卷 242-257页

作者： Hejia GAO Chuanfeng HE Junjie ZHAO Changyin SUN School of Artificial Intelligence Anhui University Engineering Research Center of Autonomous Unmanned System Technology Ministry of Education Anhui Provincial Key Laboratory of Security Artificial Intelligence Anhui University School of Automation Southeast University

Robotic grasping presents significant challenges due to variations in object properties, environmental complexities,and the demand for real-time operation. This study proposes the MetaCoorNet(MCN), which is a novel deep learning architecture specifically designed to address these challenges in robotic grasping pose estimation. By combining spatial and channel operators, the MetaCoor block is utilized to extract features efficiently. This architecture enhances feature selectivity by embedding location information into channel attention using a positional embedding technique within the coordinate attention mechanism. Consequently, the proposed MCN can focus on pertinent grasp-related regions. Furthermore, convolutional fusion blocks seamlessly integrate spatial and channel features, resulting in enhanced feature resolution and representation *** innovative design enables the proposed MCN to achieve state-of-the-art performance on the Cornell and Jacquard datasets,attaining accuracies of 98% and 91.2%, respectively. The effectiveness and robustness of MCN are further validated through real-world experiments conducted using a seven-degree-of-freedom Kinova manipulator.

关键词： generative ResNet meta light block coordinate attention feature resolution robot grasping

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Robust fully spin-polarized nodal chain in 3D metal-organic framework

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材料科学技术（英文版） 2025年第5期208卷 104-110页

作者： Xiaoyu Wei Ying Liu Lei Jin Cong Liu Xuefang Dai Guodong Liu Xiaoming Zhang State Key Laboratory of Reliability and Intelligence of Electrical Equipment and School of Materials Science and EngineeringHebei University of TechnologyTianjin 300130China

The identification of fully spin-polarized topological phases in magnetic inorganic materials has attracted significant *** this study,through first-principles calculations,we characterize CrCl2(pyz)2,a metal-organic framework(MOF),as a nodal chain *** results reveal a ferromagnetic ground state in this material,presenting as a half-metal with a single spin channel near the Fermi ***-ically,the spin-down states form a nodal chain close to the Fermi level,consisting of three nodal loops protected by glide mirror symmetry on distinct ***,fully spin-polarized drumhead sur-face states corresponding to these nodal loops are identified on the material's ***,we observe the persistence of the fully spin-polarized nodal chain even when tuning the ligand rotation an-gle of the ***,our investigation delves into the influence of spin-orbit coupling(SOC)on the system,revealing that it has minimal impact on the nodal *** robustness of the nodal chain in the presence of SOC underscores its intriguing and resilient nature,indicating its potential utility in various electronic ***,the robust realization of a fully spin-polarized nodal chain in this magnetic MOF system holds promise for applications in the realm of spintronics.

关键词： Topology MOFS Half-metal Fully spin-polarized Nodal chain

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Bilevel Planning of Distribution Networks with Distributed Generation and Energy Storage: A Case Study on theModified IEEE 33-Bus system

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Energy Engineering 2025年第4期122卷 1337-1358页

作者： Haoyuan Li Lingling Li State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of TechnologyTianjin300130China

Rational distribution network planning optimizes power flow distribution,reduces grid stress,enhances voltage quality,promotes renewable energy utilization,and reduces *** study establishes a distribution network planning model incorporating distributed wind turbines(DWT),distributed photovoltaics(DPV),and energy storage systems(ESS).K-means++is employed to partition the distribution network based on electrical *** the spatiotemporal correlation of distributed generation(DG)outputs in the same region,a joint output model of DWT and DPV is developed using the *** to the model’s high dimensionality,multiple constraints,and mixed-integer characteristics,bilevel programming theory is utilized to structure the *** model is solved using a mixed-integer particle swarmoptimization algorithm(MIPSO)to determine the optimal location and capacity of DG and ESS integrated into the distribution network to achieve the best economic benefits and operation *** proposed bilevel planning method for distribution networks is validated through simulations on the modified IEEE 33-bus *** results demonstrate significant improvements,with the proposedmethod reducing the annual comprehensive cost by 41.65%and 13.98%,respectively,compared to scenarios without DG and ESS or with only DG ***,it reduces the daily average voltage deviation by 24.35%and 10.24%and daily network losses by 55.72%and 35.71%.

关键词： Distribution network planning frank-copula joint output model bilevel programming theory

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