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

Vol. 10,  No. 6, 2023

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Editorial: Driving into Future with Reliable, Secure, Efficient and Intelligent MetaVehicles
Qing-Long Han
2023, 10(6): 1355-1356. doi: 10.1109/JAS.2023.123621
Abstract(173) HTML (28) PDF(41)
ChatGPT Chats on Computational Experiments: From Interactive Intelligence to Imaginative Intelligence for Design of Artificial Societies and Optimization of Foundational Models
Xiao Xue, Xiangning Yu, Fei-Yue Wang
2023, 10(6): 1357-1360. doi: 10.1109/JAS.2023.123585
Abstract(505) HTML (87) PDF(214)
Machine Learning With Data Assimilation and Uncertainty Quantification for Dynamical Systems: A Review
Sibo Cheng, César Quilodrán-Casas, Said Ouala, Alban Farchi, Che Liu, Pierre Tandeo, Ronan Fablet, Didier Lucor, Bertrand Iooss, Julien Brajard, Dunhui Xiao, Tijana Janjic, Weiping Ding, Yike Guo, Alberto Carrassi, Marc Bocquet, Rossella Arcucci
2023, 10(6): 1361-1387. doi: 10.1109/JAS.2023.123537
Abstract(877) HTML (89) PDF(157)

Data assimilation (DA) and uncertainty quantification (UQ) are extensively used in analysing and reducing error propagation in high-dimensional spatial-temporal dynamics. Typical applications span from computational fluid dynamics (CFD) to geoscience and climate systems. Recently, much effort has been given in combining DA, UQ and machine learning (ML) techniques. These research efforts seek to address some critical challenges in high-dimensional dynamical systems, including but not limited to dynamical system identification, reduced order surrogate modelling, error covariance specification and model error correction. A large number of developed techniques and methodologies exhibit a broad applicability across numerous domains, resulting in the necessity for a comprehensive guide. This paper provides the first overview of state-of-the-art researches in this interdisciplinary field, covering a wide range of applications. This review is aimed at ML scientists who attempt to apply DA and UQ techniques to improve the accuracy and the interpretability of their models, but also at DA and UQ experts who intend to integrate cutting-edge ML approaches to their systems. Therefore, this article has a special focus on how ML methods can overcome the existing limits of DA and UQ, and vice versa. Some exciting perspectives of this rapidly developing research field are also discussed.

Proximal Alternating-Direction-Method-of- Multipliers-Incorporated Nonnegative Latent Factor Analysis
Fanghui Bi, Xin Luo, Bo Shen, Hongli Dong, Zidong Wang
2023, 10(6): 1388-1406. doi: 10.1109/JAS.2023.123474
Abstract(386) HTML (146) PDF(71)

High-dimensional and incomplete (HDI) data subject to the nonnegativity constraints are commonly encountered in a big data-related application concerning the interactions among numerous nodes. A nonnegative latent factor analysis (NLFA) model can perform representation learning to HDI data efficiently. However, existing NLFA models suffer from either slow convergence rate or representation accuracy loss. To address this issue, this paper proposes a proximal alternating-direction-method-of-multipliers-based nonnegative latent factor analysis (PAN) model with two-fold ideas: 1) adopting the principle of alternating-direction-method-of-multipliers to implement an efficient learning scheme for fast convergence and high computational efficiency; and 2) incorporating the proximal regularization into the learning scheme to suppress the optimization fluctuation for high representation learning accuracy to HDI data. Theoretical studies verify that PAN converges to a Karush-Kuhn-Tucker (KKT) stationary point of its nonnegativity-constrained learning objective with its learning scheme. Experimental results on eight HDI matrices from real applications demonstrate that the proposed PAN model outperforms several state-of-the-art models in both estimation accuracy for missing data of an HDI matrix and computational efficiency.

Dual-Prior Integrated Image Reconstruction for Quanta Image Sensors Using Multi-Agent Consensus Equilibrium
Dan Zhang, Qiusheng Lian, Yueming Su, Tengfei Ren
2023, 10(6): 1407-1420. doi: 10.1109/JAS.2023.123390
Abstract(299) HTML (54) PDF(66)

Quanta image sensors (QIS) are a new type of single-photon imaging device that can oversample the light field to generate binary bit-streams. The reconstruction for QIS refers to the recovery of original scenes from these binary measurements. Conventional reconstruction algorithms for QIS generally depend solely on one instantiated prior and are certainly insufficient for capturing the statistical properties over high-dimensional space. On the other hand, deep learning-based methods have shown promising performance, due to their excellent ability to learn feature representations from relevant databases. However, most deep models only focus on exploring local features while generally overlooking long-range similarity. In view of this, a dual-prior integrated reconstruction algorithm for QIS (DPI-QIS) is proposed, which combines a deep prior with a non-local self-similarity one using the multi-agent consensus equilibrium (MACE) framework. In comparison to the approaches that utilize a single prior, DPI-QIS fits the reconstruction model sufficiently by leveraging the respective merits of both priors. An effective yet flexible MACE framework is employed to integrate the physical forward model allying with the two prior-based models to achieve an overall better result. Extensive experiments demonstrate that the proposed algorithm achieves state-of-the-art performance in terms of objective and visual perception at multiple oversampling factors, while having stronger robustness to noise.

Lyapunov-Based Output Containment Control of Heterogeneous Multi-Agent Systems With Markovian Switching Topologies and Distributed Delays
Haihua Guo, Min Meng, Gang Feng
2023, 10(6): 1421-1433. doi: 10.1109/JAS.2023.123198
Abstract(519) HTML (88) PDF(128)

This paper considers the mean square output containment control problem for heterogeneous multi-agent systems (MASs) with randomly switching topologies and nonuniform distributed delays. By modeling the switching topologies as a continuous-time Markov process and taking the distributed delays into consideration, a novel distributed containment observer is proposed to estimate the convex hull spanned by the leaders’ states. A novel distributed output feedback containment controller is then designed without using the prior knowledge of distributed delays. By constructing a novel switching Lyapunov functional, the output containment control problem is then solved in the sense of mean square under an easily-verifiable sufficient condition. Finally, two numerical examples are given to show the effectiveness of the proposed controller.

Fully Distributed Nash Equilibrium Seeking for High-Order Players With Actuator Limitations
Maojiao Ye, Qing-Long Han, Lei Ding, Shengyuan Xu
2023, 10(6): 1434-1444. doi: 10.1109/JAS.2022.105983
Abstract(343) HTML (96) PDF(89)

This paper explores the problem of distributed Nash equilibrium seeking in games, where players have limited knowledge on other players’ actions. In particular, the involved players are considered to be high-order integrators with their control inputs constrained within a pre-specified region. A linear transformation for players’ dynamics is firstly utilized to facilitate the design of bounded control inputs incorporating multiple saturation functions. By introducing consensus protocols with adaptive and time-varying gains, the unknown actions for players are distributively estimated. Then, a fully distributed Nash equilibrium seeking strategy is exploited, showcasing its remarkable properties: 1) ensuring the boundedness of control inputs; 2) avoiding any global information/parameters; and 3) allowing the graph to be directed. Based on Lyapunov stability analysis, it is theoretically proved that the proposed distributed control strategy can lead all the players’ actions to the Nash equilibrium. Finally, an illustrative example is given to validate effectiveness of the proposed method.

Augmented Industrial Data-Driven Modeling Under the Curse of Dimensionality
Xiaoyu Jiang, Xiangyin Kong, Zhiqiang Ge
2023, 10(6): 1445-1461. doi: 10.1109/JAS.2023.123396
Abstract(25) HTML (1) PDF(0)
The curse of dimensionality refers to the problem of increased sparsity and computational complexity when dealing with high-dimensional data. In recent years, the types and variables of industrial data have increased significantly, making data-driven models more challenging to develop. To address this problem, data augmentation technology has been introduced as an effective tool to solve the sparsity problem of high-dimensional industrial data. This paper systematically explores and discusses the necessity, feasibility, and effectiveness of augmented industrial data-driven modeling in the context of the curse of dimensionality and virtual big data. Then, the process of data augmentation modeling is analyzed, and the concept of data boosting augmentation is proposed. The data boosting augmentation involves designing the reliability weight and actual-virtual weight functions, and developing a double weighted partial least squares model to optimize the three stages of data generation, data fusion, and modeling. This approach significantly improves the interpretability, effectiveness, and practicality of data augmentation in the industrial modeling. Finally, the proposed method is verified using practical examples of fault diagnosis systems and virtual measurement systems in the industry. The results demonstrate the effectiveness of the proposed approach in improving the accuracy and robustness of data-driven models, making them more suitable for real-world industrial applications.
Group-Consensus of Hierarchical Containment Control for Linear Multi-Agent Systems
Jingshu Sang, Dazhong Ma, Yu Zhou
2023, 10(6): 1462-1474. doi: 10.1109/JAS.2023.123528
Abstract(384) HTML (151) PDF(130)

The existing containment control has been widely developed for several years, but ignores the case for large-scale cooperation. The strong coupling of large-scale networks will increase the costs of system detection and maintenance. Therefore, this paper is concerned with an extensional containment control issue, hierarchical containment control. It aims to enable a multitude of followers achieving a novel cooperation in the convex hull shaped by multiple leaders. Firstly, by constructing the three-layer topology, large-scale networks are decoupled. Then, under the condition of directed spanning group-tree, a class of dynamic hierarchical containment control protocol is designed such that the novel group-consensus behavior in the convex hull can be realized. Moreover, the definitions of coupling strength coefficients and the group-consensus parameter in the proposed dynamic hierarchical control protocol enhance the adjustability of systems. Compared with the existing containment control strategy, the proposed hierarchical containment control strategy improves dynamic control performance. Finally, numerical simulations are presented to demonstrate the effectiveness of the proposed hierarchical control protocol.

Sliding-Mode-Based Attitude Tracking Control of Spacecraft Under Reaction Wheel Uncertainties
Wei Chen, Qinglei Hu
2023, 10(6): 1475-1487. doi: 10.1109/JAS.2022.105665
Abstract(693) HTML (81) PDF(137)

The attitude tracking operations of an on-orbit spacecraft with degraded performance exhibited by potential actuator uncertainties (including failures and misalignments) can be extraordinarily challenging. Thus, the control law development for the attitude tracking task of spacecraft subject to actuator (namely reaction wheel) uncertainties is addressed in this paper. More specially, the attitude dynamics model of the spacecraft is firstly established under actuator failures and misalignment (without a small angle approximation operation). Then, a new non-singular sliding manifold with fixed time convergence and anti-unwinding properties is proposed, and an adaptive sliding mode control (SMC) strategy is introduced to handle actuator uncertainties, model uncertainties and external disturbances simultaneously. Among this, an explicit misalignment angles range that could be treated herein is offered. Lyapunov-based stability analyses are employed to verify that the reaching phase of the sliding manifold is completed in finite time, and the attitude tracking errors are ensured to converge to a small region of the closest equilibrium point in fixed time once the sliding manifold enters the reaching phase. Finally, the beneficial features of the designed controller are manifested via detailed numerical simulation tests.

Intermittent Control for Fixed-Time Synchronization of Coupled Networks
Yongbao Wu, Ziyuan Sun, Guangtao Ran, Lei Xue
2023, 10(6): 1488-1490. doi: 10.1109/JAS.2023.123363
Abstract(331) HTML (27) PDF(93)
Recurrent ConFormer for WiFi Activity Recognition
Miao Shang, Xiaopeng Hong
2023, 10(6): 1491-1493. doi: 10.1109/JAS.2023.123291
Abstract(216) HTML (52) PDF(20)
Multi-ASV Collision Avoidance for Point-to-Point Transitions Based on Heading-Constrained Control Barrier Functions With Experiment
Yanping Xu, Lu Liu, Nan Gu, Dan Wang, Zhouhua Peng
2023, 10(6): 1494-1497. doi: 10.1109/JAS.2022.105995
Abstract(281) HTML (45) PDF(71)
A Multi-Objective and Multi-Constraint Optimization Model for Cyber-Physical Power Systems Considering Renewable Energy and Electric Vehicles
Yu Zhang, Minrui Fei, Qing Sun, Dajun Du, Aleksandar Rakić, Kang Li
2023, 10(6): 1498-1500. doi: 10.1109/JAS.2022.106037
Abstract(237) HTML (29) PDF(56)
Joint Slot Scheduling and Power Allocation in Clustered Underwater Acoustic Sensor Networks
Zhi-Xin Liu, Xiao-Cao Jin, Yuan-Ai Xie, Yi Yang
2023, 10(6): 1501-1503. doi: 10.1109/JAS.2022.106031
Abstract(205) HTML (54) PDF(42)
A Simple Framework to Generalized Zero-Shot Learning for Fault Diagnosis of Industrial Processes
Jiacheng Huang, Zuxin Li, Zhe Zhou
2023, 10(6): 1504-1506. doi: 10.1109/JAS.2023.123426
Abstract(160) HTML (53) PDF(36)
MCNet: Multiscale Clustering Network for Two-View Geometry Learning and Feature Matching
Gang Wang, Yufei Chen
2023, 10(6): 1507-1509. doi: 10.1109/JAS.2023.123144
Abstract(216) HTML (23) PDF(30)
Wood Crack Detection Based on Data-Driven Semantic Segmentation Network
Ye Lin, Zhezhuang Xu, Dan Chen, Zhijie Ai, Yang Qiu, Yazhou Yuan
2023, 10(6): 1510-1512. doi: 10.1109/JAS.2023.123357
Abstract(239) HTML (53) PDF(42)