本期导读

主题

强化学习、仿人机器人、智能电网、区块链、无人机、自适应控制、非线性离散时间系统、图像融合、分布式控制、迭代学习、优化、神经网络...

全球科研机构

美国University of California、University of Texas at Arlington、University of Virginia、Stony Brook University、New Jersey Institute of Technology、North Carolina State University；加拿大University of Alberta；澳大利亚University of Adelaide；新加坡Nanyang Technological University、National University of Singapore、Singapore University of Technology and Design；墨西哥Autonomous University of Nuevo Leon；日本University of Toyama、Shibaura Institute of Technology；浙江大学、复旦大学、同济大学、哈尔滨工业大学、北京航空航天大学、北京理工大学、吉林大学、厦门大学、武汉大学、湖南大学、深圳大学、西北工业大学...

L. Fan, C. Zeng, Y. Wang, J. Ma, F.-Y. Wang, “Social radars: finding targets in cyberspace for cybersecurity,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 2, pp. 279–282, Feb. 2024. doi: 10.1109/JAS.2024.124251 

O. Dogru, J. Xie, O. Prakash, R. Chiplunkar, J. Soesanto, H. Chen, K. Velswamy, F. Ibrahim, and  B. Huang,  “Reinforcement learning in process industries: Review and perspective,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 2, pp. 283–300, Feb. 2024. doi: 10.1109/JAS.2024.124227 

> Reviews the link between modern reinforcement learning techniques and process industries by considering the control hierarchy holistically.

> Presents the state-of-the-art theoretical advancements in the theory while presenting the relevant applications in numerous industries.

> Discusses limitations, advantages, trends, new applications, opportunities, and future prospects that can help the researchers and practitioners.

Y. Tong, H. Liu, and  Z. Zhang,  “Advancements in humanoid robots: A comprehensive review and future prospects,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 2, pp. 301–328, Feb. 2024. doi: 10.1109/JAS.2023.124140 

> Current state, advancements and future prospects of humanoid robots are outlined.

> Fundamental techniques including structure, control, learning and perception are investigated.

> Highlights the potential applications of humanoid robots.

Y. Xie, Y. Zhang, W.-J. Lee, Z. Lin, and  Y. Shamash,  “Virtual power plants for grid resilience: A concise overview of research and applications,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 2, pp. 329–343, Feb. 2024. doi: 10.1109/JAS.2024.124218 

> Provides review of concept, development and latest progresses in VPP.

> Lists challenges faced in centralized VPP operation.

> Discusses opportunities of MAS-based VPP operation.

Y. Hu, C. Zhang, B. Wang, J. Zhao, X. Gong, J. Gao, and  H. Chen,  “Noise-tolerant ZNN-Based data-driven iterative learning control for discrete nonaffine nonlinear MIMO repetitive systems,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 2, pp. 344–361, Feb. 2024. doi: 10.1109/JAS.2023.123603 

> Novel data-driven iterative learning decoupling control for complex MIMO systems.

> ILC based on NT-ZNNs: global convergence, noise sensitivity reduction.

> Elimination of the assumption about initialization conditions.

X. Ge, Q.-L. Han, X.-M. Zhang, and  D. Ding,  “Communication resource-efficient vehicle platooning control with various spacing policies,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 2, pp. 362–376, Feb. 2024. doi: 10.1109/JAS.2023.123507 

> A dynamic event-triggered scheduling and platoon control co-design approach.

> A refined constant time headway spacing policy.

> An event-driven cooperative adaptive cruise control law.

H. Zhu, M. C. Zhou, Y. Xie, and  A. Albeshri,  “A self-adapting and efficient dandelion algorithm and its application to feature selection for credit card fraud detection,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 2, pp. 377–390, Feb. 2024. doi: 10.1109/JAS.2023.124008 

> Proposing a self-adapting and efficient dandelion algorithm.

> Developing an adaptive seeding radius strategy to reduce the number of DA’s parameters.

> Applying the proposed algorithm to feature selection for accurate and fast credit card fraud detection.

Y. Jia, Q. Hu, R. Dian, J. Ma, and  X. Guo,  “PAPS: Progressive attention-based pan-sharpening,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 2, pp. 391–404, Feb. 2024. doi: 10.1109/JAS.2023.123987 

> A progressive attention-based network for pan-sharpening is designed.

> Detail enhancement module is introduced to provide better multispectral references for fusion.

> Progressive fusion module is proposed to take full advantage of spectral and spatial information.

Y. Yu, G.-P. Liu, Y. Huang, and  P. Shi,  “Optimal cooperative secondary control for islanded DC microgrids via a fully actuated approach,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 2, pp. 405–417, Feb. 2024. doi: 10.1109/JAS.2023.123942 

> Expands the study of modeling and secondary control of DC microgrids.

> Proposed microgrid modeling approach addresses the drawbacks of existing ones.

> Simple structure of the approach allows it to be easily implemented in microgrids.

Y. Zhu, N. Xu, F. Wu, X. Chen, and  D. Zhou,  “Fault estimation for a class of  Markov jump piecewise-affine systems: Current feedback based iterative learning approach,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 2, pp. 418–429, Feb. 2024. doi: 10.1109/JAS.2023.123990 

> Presented Markov jump PWA system contains both the Markov jump linear system and the ordinary PWA system as two special cases.

> To ameliorate the iteration law to estimate faults rapidly with the minimum number of iterations, the current feedback mechanism is introduced into the iterative process.

> Mode-dependent and region-dependent Lyapunov function is constructed to adapt the stochastic stability with guaranteed H_∞ performance.

J. Kang, J. Chen, M. Xu, Z. Xiong, Y. Jiao, L. Han, D. Niyato, Y. Tong, and  S. Xie,  “UAV-assisted dynamic avatar task migration for vehicular metaverse services: A multi-agent deep reinforcement learning approach,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 2, pp. 430–445, Feb. 2024. doi: 10.1109/JAS.2023.123993 

> Introduce a novel avatar task migration framework aimed at achieving continuous user-avatar interaction.

> Model the avatar task migration process as a Partially Observable Markov Decision Process.

> Propose a transformer-based decision-making model based on MAPPO that processes in a sequential manner.

N. Chen, L. Li, and  W.  Mao,  “Equilibrium strategy of the pursuit-evasion game in three-dimensional space,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 2, pp. 446–458, Feb. 2024. doi: 10.1109/JAS.2023.123996 

> Make the first attempt to theoretically investigate the equilibrium strategy for the players conducting realistic motions in three-dimensional space.

> Derive the equilibrium strategy to tackle the realistic pursuit-evasion game by modeling the three-degree-of-freedom kinematics of the pursuer, which is typical in many real-world applications.

> Provide the theoretical derivation of the equilibrium strategy based on the HJBI equation to ensure the minimax property of the equilibrium strategy.

C. Gong and  Y. You,  “Sparse reconstructive evidential clustering for multi-view data,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 2, pp. 459–473, Feb. 2024. doi: 10.1109/JAS.2023.123579 

> Estimate the cluster number for a multi-view clustering problem.

> Identify the cluster center in each cluster of multi-view data.

> Use a more fine-grained partition to cluster multi-view data.

F. Ming, W. Gong, and  Y. Jin,  “Even search in a promising region for constrained multi-objective optimization,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 2, pp. 474–486, Feb. 2024. doi: 10.1109/JAS.2023.123792 

> A promising region concept that includes the CPF and possesses good properties.

> An even search method utilizing valuable solutions in the promising region to search the CPF.

> A new two-stage CMOEA that implements the even search method.

N. Zeng, X. Li, P. Wu, H. Li, and  X. Luo,  “A novel tensor decomposition-based efficient detector for low-altitude aerial objects with knowledge distillation scheme,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 2, pp. 487–501, Feb. 2024. doi: 10.1109/JAS.2023.124029

> A novel low-altitude aerial target detection framework for efficient computation.

> Multi-domain attention mechanisms contribute to key and robust feature extraction.

> Tensor decomposition can optimize convolution operators to reduce model redundancy.

Y. Zhu, Q. Kong, J. Shi, S. Liu, X. Ye, J.-C. Wang, H. Shan, and  J.  Zhang,  “End-to-end paired ambisonic-binaural audio rendering,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 2, pp. 502–513, Feb. 2024. doi: 10.1109/JAS.2023.123969 

> Directly outputs binaural audios and does not rely on HRTFs.

> View different channels of ambisonics as different perspectives of the same sound field.

> Propose to use a channel-shared encoder and a channel-compared decoder to learn the spatial feature.

J. Yang, C. Yang, X. Zhang, and  J. Na,  “Fixed-time sliding mode control with varying exponent coefficient for modular reconfigurable flight arrays,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 2, pp. 514–528, Feb. 2024. doi: 10.1109/JAS.2023.123645 

> A modular reconfigurable flight array with variable topological configurations is designed.

> A center-of-mass-independent modeling approach is studied.

> A novel fixed-time sliding mode control with less constraint is proposed.

S. Feng, L. Zeng, J. Liu, Y. Yang, and  W. Song,  “Multi-UAVs collaborative path planning in the cramped environment,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 2, pp. 529–538, Feb. 2024. doi: 10.1109/JAS.2023.123945 

> A hierarchical planning framework based on the ECBS algorithm is proposed for multi-UAVs cooperative path planning problem in cramped environments, which has solved the problems of size-constrained obstacle avoidance and spatiotemporal conflict avoidance.

> Relying on the path search algorithm based on the kinematic model of UAVs, the smoothness of output paths is improved, which is convenient for the tracking control of UAVs.

> Detailed experiments are conducted in the specially designed cramped environment test set, and the source code and test set are published, available for comparison by relevant researchers.

Z. Zhang, Z. Lei, M. Omura, H. Hasegawa, and S. Gao, “Dendritic learning-incorporated vision transformer for image recognition,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 2, pp. 539–541, Feb. 2024. doi: 10.1109/JAS.2023.123978 

F.-Y. Wang and Y. Shen, “Parallel light fields: A perspective and a framework,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 2, pp. 542–544, Feb. 2024. doi: 10.1109/JAS.2023.123174 

Y. Han, L. Wang, Y. Wang, and Z. Geng, “Intelligent small sample defect detection of concrete surface using novel deep learning integrating improved YOLOv5,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 2, pp. 545–547, Feb. 2024. doi: 10.1109/JAS.2023.124035 

L. Yan, Q. Li, and K. Li, “Object helps U-Net based change detectors,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 2, pp. 548–550, Feb. 2024. doi: 10.1109/JAS.2023.124032 

Y. Li, W. Liu, J. Sun, C. Chen, J. Zhang, and G. Wang, “Online consensus control of nonlinear affine systems from disturbed data,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 2, pp. 551–553, Feb. 2024. doi: 10.1109/JAS.2023.123894 

Y. Cui, Y. Huang, M. Basin, and Z. Wu, “Geometric programming for nonlinear satellite buffer networks with time delays under L1-gain performance,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 2, pp. 554–556, Feb. 2024. doi: 10.1109/JAS.2023.123726 

J. Kuang, Y. Gao, Y. Sun, A. Liu, and J. Liu, “Stabilization with prescribed instant via Lyapunov method,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 2, pp. 557–559, Feb. 2024. doi: 10.1109/JAS.2023.123801 

Z. Luo, B. Zhu, J. Zheng, and Z. Zheng, “Robust distributed model predictive control for formation tracking of nonholonomic vehicles,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 2, pp. 560–562, Feb. 2024. doi: 10.1109/JAS.2023.123732 

Z. Wang, X. Jin, T. Zhang, and D. Yu, “A finite-time convergent analysis of continuous action iterated dilemma,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 2, pp. 563–565, Feb. 2024. doi: 10.1109/JAS.2023.123606 

Y. Tian, M. Liu, S. Zhang, R. Zheng, and S. Dong, “A feature-aided multiple model algorithm for maneuvering target tracking,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 2, pp. 566–568, Feb. 2024. doi: 10.1109/JAS.2023.123939