A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Advanced Search

Early Access

Display Method:
Attribute-Augmented PPR Meets Self-Loops: Simple Yet Effective Defending Graph Neural Networks
Zeli Wang, Jie Li, Zhiqiu Ye, Longlong Lin, Mengdi Wang, Guoyin Wang
, Available online  , doi: 10.1109/JAS.2026.125990
[Abstract](38) [FullText HTML] (0) [PDF 2116KB](11)
Abstract:
Graph Neural Networks (GNNs) are effective in processing graph-structured data but are also known to be vulnerable to adversarial attacks. Limitations of existing defense methodologies include a suboptimal approach to edge and node weight assignment, as such methods generally focus on the graph’s topological structure or node attributes. This imbalance can lead to inaccurate assessments of the importance of neighboring nodes, especially those targeted by attacks. We posit that the model should diminish the influence of insignificant and contaminated neighboring nodes on graph representation learning, thereby mitigating the adverse effects of compromised neighbors. Hence, we propose a versatile GNN adversarial defense framework by combining a novel attribute-augmented Personalized PageRank (PPR) with an adaptive self-loop weight adjustment mechanism, coined GPROP, to defend against adversarial attacks. Specifically, the attribute-augmented PPR helps compute the node similarity more accurately based on global semantics, by considering multi-hop graph topology and node attributes. According to the similarity scores, GPROP assigns corresponding weights to edges and simultaneously prunes insignificant ones. When iteratively aggregating the neighbor information, the proposed self-loop mechanism dynamically adjusts the weight ratio of neighboring nodes and the nodes themselves based on the node’s degree. This strategy reduces the influence of neighboring nodes that may introduce unreliable or harmful information. Moreover, GPROP is model-agnostic and can be readily embedded into different GNN backbones, leading to a substantial improvement in resistance to adversarial perturbations. Experimental results across multiple benchmark datasets show that our proposal consistently outperforms existing approaches, providing enhanced resilience and accuracy.
Deep Domain Adaptation for Turbofan Engine Remaining Useful Life Prediction: Methodologies, Evaluation, and Future Trends
Yucheng Wang, Mohamed Ragab, Yubo Hou, Min Wu, Xiaoli Li, Zhenghua Chen
, Available online  , doi: 10.1109/JAS.2025.125843
[Abstract](66) [FullText HTML] (0) [PDF 1794KB](12)
Abstract:
Remaining Useful Life (RUL) prediction for turbofan engines plays a vital role in predictive maintenance, ensuring operational safety and efficiency in aviation. Although data-driven approaches using machine learning and deep learning have shown potential, they face challenges such as limited data and distribution shifts caused by varying operating conditions. Domain Adaptation (DA) has emerged as a promising solution, enabling knowledge transfer from source domains with abundant data to target domains with scarce data while mitigating distributional shifts. Given the unique properties of turbofan engines—such as complex operating conditions, high-dimensional sensor data, and slower-changing signals—it is essential to conduct a focused review of DA techniques specifically tailored to turbofan engines. To address this need, this paper provides a comprehensive review of DA solutions for turbofan engine RUL prediction, analyzing key methodologies, challenges, and recent advancements. A novel taxonomy tailored to turbofan engines is introduced, organizing approaches into methodology-based (how DA is applied), alignment-based (where distributional shifts occur due to operational variations), and problem-based (why certain adaptations are needed to address specific challenges). This taxonomy offers a multidimensional view that goes beyond traditional classifications by accounting for the distinctive characteristics of turbofan engine data and the standard process of applying DA techniques to this area. Additionally, we evaluate selected DA techniques on turbofan engine datasets, providing practical insights for practitioners and identifying key challenges. Future research directions are identified to guide the development of more effective DA techniques, advancing the state of RUL prediction for turbofan engines.
Construction of Conflict-Free and Efficient Cross-Organization Emergency Response Processes: A Petri Net-Based Approach
Qi Mo, Shichao Wei, Yuhang Zuo, Chengting Jiang, Fei Dai, Cong Liu
, Available online  , doi: 10.1109/JAS.2026.125873
[Abstract](175) [FullText HTML] (0) [PDF 50918KB](24)
Abstract:
Usually, the disposal of the emergency is organized as a cross-organization emergency response process (CERP), where various resources are involved. The lack of these resources may cause resource conflicts that can delay or even suspend the CERP, thereby increasing the risk imposed on life, property, and the environment. In this paper, we propose a novel approach to construct conflict-free and efficient CERPs. This approach first presents a branching place-based method to decompose a CERP into a set of execution paths. In essence, an execution path refers to a process fragment without choice structures corresponding to some kind of process instance in the CERP. In practice, each execution of the CERP can only follow such an execution path. Next, it determines whether each execution path contains resource conflicts. If not, then the execution path is considered conflict-free; otherwise, it will be resolved using a delay-based strategy. Lastly, it introduces an execution path-oriented strategy to merge all originally conflict-free and resolved execution paths to form a resolved CERP, in which each execution of it is conflict-free and efficient. The proposed approach is implemented in the tool RCTool, and a group of experiments conducted on actual CERPs demonstrates that it is more effective in constructing conflict-free and efficient CERPs compared to existing proposals, and its computation overhead is also acceptable in practice.
A Hybrid Encoding-Based Coordinated Optimization Method for Charging Matrix Design in the Blast Furnace Ironmaking Process
Jicheng Zhu, Zhaohui Jiang, Dong Pan, Haoyang Yu, Chuan Xu, Ke Zhou, Weihua Gui
, Available online  , doi: 10.1109/JAS.2025.126011
[Abstract](269) [FullText HTML] (0) [PDF 3258KB](27)
Abstract:
A well-designed charging matrix (CM) is crucial for advancing green and low-carbon production in the blast furnace (BF) ironmaking process. Over recent years, metaheuristics algorithms have been applied to optimize CM, partially reducing reliance on on-site workers. However, CM optimization is a challenging mixed-variable constraint optimization problem. Prior studies predominantly simplify CM to either continuous or discrete forms via variable fixation or type conversion, which hinders the efficient joint optimization of heterogeneous variables, limiting optimization accuracy and search efficiency. To tackle this barrier, this study proposes a novel method named Hybrid Encoding-based Adaptive Coordinated Differential Evolution (HE-ACoDE), marking the first attempt to optimize CM from a mixed-variable perspective. First, a hybrid encoding scheme is devised to provide a unified representation for the mixed variables in CM. Then, a coordinated mixed-variable mutation strategy is developed, effectively facilitating the synchronized evolution of continuous and discrete variables. Moreover, a constraint-aware selection operator and a weight-guided parameter adaptation strategy are proposed, which collaboratively guide the population toward feasible, high-quality solutions across different evolutionary stages and problem landscapes. Extensive comparison experiments on two industrial scenarios demonstrate that HE-ACoDE outperforms state-of-the-art CM optimization and mixed-variable optimization methods in terms of accuracy, stability, and convergence performance.
Cartesian Space Control and Joint Tracking Control for a Robotic Arm System with Explicit-time Proportional Convergence
Wen Yan, Tao Zhao, Ben Niu, Zhiyi Shi, Edmond Q. Wu
, Available online  , doi: 10.1109/JAS.2026.125963
[Abstract](402) [FullText HTML] (0) [PDF 8510KB](62)
Abstract:
The Lyapunov synthesis method is a common controller design strategy in robotic arm motion control. However, it is difficult for this method to achieve fixed-time control without a nonlinear feedback design, whose nonlinearity may cause chattering in the robotic motion. To address this problem, a novel explicit-time control method is proposed using proportional feedback. Not only can the proposed method be applied to the Cartesian space control of the robotic arm system, but it can also be used for the joint-space tracking control. More specifically, under bounded initial condition, the origin of system is attracted to a predefined neighborhood of zero within an explicit fixed-time boundary. Based on that, a robust fixed-time tracking controller of robot is designed by using this linear time-invariant feedback. Besides, compared with other related methods, the proposed method has smoother and lower control input under the same initial condition. In particular, this method enables the robotic arm to achieve a tracking accuracy of 0.1 millimeters and 0.1 degrees within as short as 1.5 seconds, while the repeat positioning accuracy approaches the hardware limit, reaching 0.001 millimeters (±0.03 millimeters) and 0.001 degrees (±0.05 degrees). Theoretical analysis, simulation and experiment verify the main results. Code, data and video are also available, the corresponding links are printed in the relevant places.
Reinforcement Learning-Based Adaptive Optimal Control for a Snake Robot
Yang Xiu, Zhiyi Shi, Guanghong Liu, Rob Law, Dongfang Li, Aiguo Song, Edmond Q. Wu
, Available online  , doi: 10.1109/JAS.2025.125762
[Abstract](433) [FullText HTML] (0) [PDF 6395KB](24)
Abstract:
Due to the difficulty of accurately modeling snake robots, model-based control schemes are ineffective, and the constraints of motion velocity and energy consumption pose challenges to meandering gait. In this work, a two-layer reinforcement learning-based adaptive optimal control framework for snake robots is proposed to achieve trajectory tracking motion of optimal energy efficiency gait. A multi-objective problem for gait amplitude, frequency, and phase is established in the optimization layer, which balances minimizing energy consumption and maximizing velocity by weighted summation. Multiple matching results of gait parameters and performance are obtained through proximal policy optimization, allowing users to select the optimal combination. In the control layer, an actor-critic-identifier neural network-based reinforcement learning optimal controller is designed by considering the difficulty in solving dynamics unknowns and Bellman equation. It adaptively fits the cost function and control policy, reducing the dependence on an accurate model and avoiding computational complexity. Theoretical analysis demonstrates that the proposed method can guarantee stability of tracking errors for snake robots, with optimal cost. Comparative simulation experiment results show the effectiveness and superiority of this method.
Adaptive Dynamic Trade-off Optimization between Manipulability and Sparsity for Redundant Manipulators
Zhaoyang Song, Wei Chen, Huichao Cao
, Available online  , doi: 10.1109/JAS.2026.125759
[Abstract](454) [FullText HTML] (0) [PDF 842KB](15)
Abstract:
Data-Driven Distributed Model Predictive Control for Large-Scale Systems with Actuator Faults
Yan Li, Hao Zhang, Huaicheng Yan, Yongxiao Tian, Yanfei Zhu
, Available online  , doi: 10.1109/JAS.2025.125858
[Abstract](584) [FullText HTML] (0) [PDF 431KB](35)
Abstract:
Majorization-Minimization-Based Neural Dynamics for Time-Variant Optimization Under Multi-Set Constraints
Ying Liufu, Yongji Guan
, Available online  , doi: 10.1109/JAS.2026.125768
[Abstract](597) [FullText HTML] (0) [PDF 678KB](13)
Abstract:
Distributed Optimal Consensus Control of Multi-Agent Systems Under Indifferent and Self-Sacrificing Alienation
Yue Zhang, Yan-Wu Wang, Xiao-Kang Liu
, Available online  , doi: 10.1109/JAS.2025.125861
[Abstract](809) [FullText HTML] (0) [PDF 1254KB](96)
Abstract:
Knowledge-Assistant Deep Reinforcement Learning for Multi-Agent Region Protection
Siqing Sun, Tianbo Li
, Available online  , doi: 10.1109/JAS.2025.125912
[Abstract](1068) [FullText HTML] (0) [PDF 1320KB](43)
Abstract:
A New Parameter Estimation Methodology Using Steady State Yaw Rate Measurements for Lateral Vehicle Dynamics
Zhihong Man, Mingcong Deng, Zenghui Wang, Qing-long Han
, Available online  , doi: 10.1109/JAS.2025.125366
[Abstract](884) [FullText HTML] (0) [PDF 5749KB](102)
Abstract:
In this paper, the lateral dynamics of road vehicles (LDRV) is further studied from the viewpoint of vehicle informatics. It is seen that LDRV is first decoupled and the vehicle slip angle is proved to be observable from the yaw rate measurements. A new methodology of parameter estimation using steady-state yaw rate measurements (PESYRM) is then developed to accurately estimate the parameters of LDRV. The important characteristics of PESYRM comprise four parts: ( i ) The steering angle input to LDRV is chosen as the linear combination of sinusoids; ( ii ) Only the steady state information of yaw rate in any fundamental period is required to accurately estimate the unknown parameters of LDRV; ( iii ) Unlike many existing parameter estimation methods, the time consuming computing of the inverse of high-dimensional data matrix is avoided by making full use of the orthogonal properties of trigonometric base functions; ( iv ) All of system information of LDRV is embedded in the measurements of the steady state yaw rate in any fundamental period. A simulation example is carried out to show the advantages and effectiveness of the new research findings for LDRV.
Multi-Agent Swarm Optimization With Contribution-Based Cooperation for Distributed Multi-Target Localization and Data Association
Tai-You Chen, Xiao-Min Hu, Qiuzhen Lin, Wei-Neng Chen
, Available online  , doi: 10.1109/JAS.2025.125150
[Abstract](2478) [FullText HTML] (5) [PDF 1876KB](161)
Abstract:
With the development of communication and computation capabilities on terminal hardware, it is promising to apply distributed optimization methods to wireless sensor networks to improve the autonomous collaboration ability of sensors. In this work, we study distributed optimization for multi-target localization with measurement-to-measurement association (DM2M), where each sensor only accesses its own measurement data without the association of measurements from other sensors. We first reformulate DM2M into a distributed bilevel optimization problem to reduce the search space of negotiated variables caused by the data association among sensors. Then, we propose a multi-agent swarm optimization method with contribution-based cooperation (MASTER). In MASTER, each sensor maintains a particle swarm to represent candidate solutions of target positions. Sensors evolve their particle swarms through two phases of local optimization and neighbor cooperation to locate the target cooperatively. To address the bilevel local objective function, we combine the Kuhn-Munkres algorithm and the competitive swarm optimization for local optimization. To promote sensors to optimize the global objective, we design a contribution-based cooperation method to guide sensors to learn from their neighbors. Through localization experiments for different target numbers and localization dimensions, the proposed algorithm achieves smaller localization errors and more stable consensus than existing algorithms.
KT-RC: Kernel Time-Delayed Reservoir Computing for Time Series Prediction
Heshan Wang, Mengmeng Chen, Kunjie Yu, Jing Liang, Zhaomin Lv, Zhong Zhang
, Available online  , doi: 10.1109/JAS.2024.124986
[Abstract](977) [FullText HTML] (0) [PDF 9327KB](74)
Abstract:
Reservoir computing (RC) is an efficient recurrent neural network (RNN) method. However, the performance and prediction results of traditional RCs are susceptible to several factors, such as their network structure, parameter setting, and selection of input features. In this study, we employ a kernel time-delayed RC (KT-RC) method for time series prediction. The KT-RC transforms input vectors linearly to obtain a high-dimensional set of time-delayed linear eigenvectors, which are then transformed by various kernel functions to represent the nonlinear characteristics of the input signal. Finally, the Bayesian optimization algorithm adjusts the few remaining weights and kernel parameters to minimize the manual adjustment process. The advantages of KT-RC can be summarized as follows: 1) KT-RC solves the problems of uncertainty in weight matrices and difficulty in large-scale parameter selection in the input and hidden layers of RCs. 2) The KT module can avoid massive reservoir hyperparameters and effectively reduce the hidden layer size of the traditional RC. 3) The proposed KT-RC shows good performance, strong stability, and robustness in several synthetic and real-world datasets for one-step-ahead and multistep-ahead time series prediction. The simulation results confirm that KT-RC not only outperforms some gate-structured RNNs, kernel vector regression models, and recently proposed prediction models but also requires fewer parameters to be initialized and can reduce the hidden layer size of the traditional RCs. The source code is available at https://github.com/whs7713578/RC.
Efficient Centralized Traffic Grid Signal Control Based on Meta-Reinforcement Learning
Jia Wu, Yican Lou
, Available online  , doi: 10.1109/JAS.2023.123270
[Abstract](1453) [FullText HTML] (0) [PDF 703KB](83)
Abstract:
Supplementary File of “Push-Sum Based Algorithm for Constrained Convex Optimization Problem and Its Potential Application in Smart Grid”
Qian Xu, Zao Fu, Bo Zou, Hongzhe Liu, Lei Wang
, Available online  
[Abstract](1077) [FullText HTML] (0) [PDF 131KB](1)
Abstract:
Supplementary Material for “Collision and Deadlock Avoidance in Multi-Robot Systems Based on Glued Nodes”
Zichao Xing, Xinyu Chen, Xingkai Wang, Weimin Wu, Ruifen Hu
, Available online  
[Abstract](1523) [FullText HTML] (0) [PDF 8129KB](6)
Abstract: