IEEE/CAA Journal of Automatica Sinica

A New Knowledge Mining and Root Cause Analysis Methodology for Multivariate Time Series

Xiaoliang Wang, Faming Lu, MengChu Zhou, Qingtian Zeng

, Available online , doi: 10.1109/JAS.2026.125837

[Abstract](17) [FullText HTML] (0) [PDF 4116KB](2)

Abstract:
Root cause analysis (RCA) aims to discover the root causes of abnormal events. Causal relations reveal the evolution process of abnormal events, which plays a crucial role in RCA. However, existing temporal causal discovery methods neither explicitly emphasize the “AND/OR” relations among causes, nor consider the synergy effects owned by non-causal variables on causal rules, thereby affecting the credibility of RCA. To address the issues, by fusing Petri nets and Bayesian networks, this study proposes a new knowledge mining and RCA methodology for multivariate time series, called Synergy-incorporated Bayesian Time Petri Net. It integrates the advantages of Petri nets in modeling and analyzing complex temporal dependencies and Bayesian networks in evidence reasoning. It takes into account “AND/OR” relations and synergy effects in temporal knowledge mining and RCA. Two cases are employed to verify the performance of the proposed methodology in knowledge mining and RCA, including a case study of quality anomaly detection of solar panel and the Tennessee Eastman process. The experimental results from both cases indicate that the proposed methodology can effectively consider “AND/OR” relations and synergy effects. In particular, when applied to the diagnosis of quality issues in solar panels, the proposed methodology outperforms the state-of-the-art RCA methods in accuracy by over 11%.

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

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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

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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.

Toward Resilient Vehicle Platooning: A Two-Layer Secure Control Architecture against Hybrid Cyber-Physical Threats

Jian Gong, Lei Ding, Chengfeng Jia, Yutian Liu, Jinde Cao

, Available online , doi: 10.1109/JAS.2026.125909

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Abstract:
This paper presents a hierarchical secure control framework for resilient vehicular platooning under hybrid cyber-physical threats, including coupled false data injection (FDI) and denial-of-service (DoS) attacks, as well as actuator faults. A two-layer architecture is adopted to decouple cyber-layer disruptions from physical-layer execution, thereby enhancing system modularity and fault isolation. At the upper layer, a virtual platoon system is constructed, where a distributed resilient controller integrated with an event-triggered mechanism (ETM) is developed to ensure coordinated behavior while reducing communication overhead. At the lower layer, an adaptive fault-tolerant tracking controller is designed to compensate for actuator degradation and external disturbances, enabling each physical vehicle to follow its virtual reference independently. A layer-wise Lyapunov-based analysis is conducted to guarantee the practical exponential stability of the hierarchical control framework, where tractable LMI conditions are derived for both the cyber coordination and physical tracking components. Simulation results demonstrate that the proposed architecture effectively mitigates fault propagation, maintains robust performance under concurrent cyber and physical threats, and outperforms non-hierarchical benchmarks in terms of system stability.

Gain-Based Neural Secure Protection Control for Feedforward Nonlinear Systems With Unknown Control Coefficients and Impulsive FDI Attacks

Debao Fan, Qingrong Liu, Rong Su, Xianfu Zhang, Wenjie Zhang

, Available online , doi: 10.1109/JAS.2025.125807

[Abstract](412) [FullText HTML] (0) [PDF 1586KB](32)

Abstract:
This paper proposes a gain-based neural secure protection (GBNSP) control scheme for feedforward nonlinear systems subject to unknown control coefficients and impulsive false data injection (FDI) attacks. Notably, the nonlinear functions of the systems are relaxed to any continuous functions and the control coefficients are permitted to be constants with both unknown sizes and signs, a scenario not covered in existing works. Furthermore, the uncertain abrupt changes in system states caused by impulsive FDI attacks inevitably exacerbate the challenges in control design. To this end, this paper integrates the neural network technique and the gain control method to propose a novel GBNSP control scheme. Specifically, the neural network technique effectively compensates for strong nonlinearities and uncertainties, while the gain control method quantifies the tolerable frequency of impulsive FDI attacks and avoids the tedious design procedures. It is shown that, under the designed GBNSP controller, all closed-loop signals remain bounded and the system states eventually converge to an adjustable neighborhood near the origin. Moreover, an enhanced GBNSP control scheme incorporates an improved gain scaling mechanism to withstand unknown external disturbances. In the end, the effectiveness and practicality of the proposed scheme are validated by a theoretical example and a practical example.

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](276) [FullText HTML] (0) [PDF 6395KB](18)

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](329) [FullText HTML] (0) [PDF 842KB](12)

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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](436) [FullText HTML] (0) [PDF 431KB](31)

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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](476) [FullText HTML] (0) [PDF 678KB](12)

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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](670) [FullText HTML] (0) [PDF 1254KB](86)

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Knowledge-Assistant Deep Reinforcement Learning for Multi-Agent Region Protection

Siqing Sun, Tianbo Li

, Available online , doi: 10.1109/JAS.2025.125912

[Abstract](908) [FullText HTML] (0) [PDF 1320KB](43)

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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](761) [FullText HTML] (0) [PDF 5749KB](96)

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](2252) [FullText HTML] (5) [PDF 1876KB](155)

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](839) [FullText HTML] (0) [PDF 9327KB](71)

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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](1315) [FullText HTML] (0) [PDF 703KB](80)

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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](963) [FullText HTML] (0) [PDF 131KB](1)

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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](1353) [FullText HTML] (0) [PDF 8129KB](6)

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