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大规模群体决策，水下机器人，信息物理系统，自适应控制，神经网络，多智能体系统，无人机，非线性系统...

全球科研机构

美国Purdue University、Louisiana State University、Rochester Institute of Technology；英国Brunel University London；瑞典Chalmers University of Technology；瑞士ETH Zurich；西班牙University of Jaen；清华大学、中科院自动化所、浙江大学、北京理工大学、中国科学技术大学、电子科技大学...

 

D. García-Zamora, á. Labella, W. Ding, R. M. Rodríguez, and  L. Martínez,  “Large-scale group decision making: A systematic review and a critical analysis,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 6, pp. 949–966, Jun. 2022. 

doi: 10.1109/JAS.2022.105617

> Analysis of the current state of art about the existing trends related to the Large-scale Group Decision Making.

> Critical discussion about the main limitations of present proposals.

> Redirection of current research towards new trends which face real-world needs demanded by large-scale contexts.

H. S. Xia, M. A. Khan, Z. J. Li, and  M. C. Zhou,  “Wearable robots for human underwater movement ability enhancement: A survey,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 6, pp. 967–977, Jun. 2022. 

doi: 10.1109/JAS.2022.105620 

> Analyzes the state-of-the-art of underwater exoskeleton for human enhancement.

> Challenges: underwater motion intention perception, underwater exoskeleton modeling and control.

> Future direction: novel structures, sensors & fusion, underwater dynamic models.

H. Geng, Z. D. Wang, Y. Chen, X. J. Yi, and  Y. H. Cheng,  “Variance-constrained filtering fusion for nonlinear cyber-physical systems with the denial-of-service attacks and stochastic communication protocol,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 6, pp. 978–989, Jun. 2022. 

doi: 10.1109/JAS.2022.105623 

> A new filtering fusion problem is studied for nonlinear cyber-physical systems.

> Error-variance constraints and denial-of-service attacks are investigated.

> A fusion estimator is proposed under the federated fusion rule.

X. Ge, Q.-L. Han, J. Wang, and  X.-M. Zhang,  “A scalable adaptive approach to multi-vehicle formation control with obstacle avoidance,”IEEE/CAA J. Autom. Sinica, vol. 9, no. 6, pp. 990–1004, Jun. 2022.  doi: 10.1109/JAS.2021.1004263

> A scalable and collision-free adaptive formation tracking control protocol.

> An efficient built-in collision avoidance mechanism.

> A design algorithm of the desired adaptive formation tracking control laws.

H. F. Min, S. Y. Xu, B. Y. Zhang, Q. Ma, and  D. M. Yuan,  “Fixed-time Lyapunov criteria and state-feedback controller design for stochastic nonlinear systems,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 6, pp. 1005–1014, Jun. 2022. doi: 10.1109/JAS.2022.105539 

> An improved fixed-time Lyapunov theorem with a more rigorous and reasonable proof procedure is proposed.

> A less conservative upper-bound estimate of the settling time is proved and obtained.

> A state-feedback controller is constructed by using backstepping, the adding a power integrator method and the fixed-time control procedure.

J. F. Hu, Z. Chen, X. Ma, H. Lai, and  B. Yao,  “A telepresence-guaranteed control scheme for teleoperation applications of transferring weight-unknown objects,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 6, pp. 1015–1025, Jun. 2022. doi: 10.1109/JAS.2022.105626 

> A teleoperation control scheme for transferring weight-unknown objects.

> Master-side force feedback to reproduce the object’s weight.

> Slave-side accurate trajectory following under dynamic uncertainties.

J. Mao, X. Meng, and D. Ding, “Fuzzy set-membership filtering for discrete-time nonlinear systems,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 6, pp. 1026–1036, Jun. 2022. 

doi: 10.1109/JAS.2022.105416 

> State estimation problem was studied for discrete-time nonlinear systems subject to unknown-but-bounded noises.

> An improved T-S fuzzy model was introduced to achieve highly accurate approximation. Two fuzzy set-membership filters, namely, FSMF1 and FSMF2, were proposed that consider both the prediction and the filtering.

> Some features of the membership functions were utilized in the filter design so that the stability of the estimation error system can be ensured.

C. Liu, B. Jiang, X. F. Wang, H. L. Yang, and  S. R. Xie,  “Distributed fault-tolerant consensus tracking of multi-agent systems under cyber-attacks,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 6, pp. 1037–1048, Jun. 2022. doi: 10.1109/JAS.2022.105419 

> Attempts to combine network anti-attack and fault-tolerant control technologies effectively.

> It is a brand-new attempt to address the different types of constraints of self-dynamics in physical hierarchy and maintained/paralyzed links in networked hierarchy.

> A novel control structure is proposed with the effective combination of local fault/state estimation in decentralized FE and adjacent output information in distributed FCTC.

M. Ballesteros, R. Q. Fuentes-Aguilar, and I. Chairez, “Exponential continuous non-parametric neural identifier with predefined convergence velocity,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 6, pp. 1049–1060, Jun. 2022. 

doi: 10.1109/JAS.2022.105650 

> Addresses the design of an exponential function-based learning law for differential neural networks.

> Two novel adaptive algorithms with predefined exponential convergence rate adjust the weights of the neural network.

> The application of the invariant ellipsoid method yields to obtain an algorithm to reduce the volume of the convergence region for the identification error.

X. Li, H. B. Duan, Y. L. Tian, and  F.-Y. Wang,  “Exploring image generation for UAV change detection,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 6, pp. 1061–1072, Jun. 2022. 

doi: 10.1109/JAS.2022.105629 

> Present a typical inland-water scenario and generates simulated multi-challenge sequences for testing the visual intelligence of UAV.

> An image translation network is proposed to synthesize photo-realistic images. All generation datasets are public available on the website, which may have a large potential to benefit the change detection community in the future.

> Utilize synthetic datasets and corresponding real datasets to conduct change detection experiments. The experimental results demonstrate that synthetic datasets can effectively improve deep learning-based detectors.

H. F. Ye and  Y. D. Song,  “Adaptive control with guaranteed transient behavior and zero steady-state error for systems with time-varying parameters,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 6, pp. 1073–1082, Jun. 2022. 

doi: 10.1109/JAS.2022.105608

> Provides an adaptive PPC scheme for parameter-varying nonlinear systems.

> Proposed error transformation is new, resulting in a global solution independent of accurate initial error.

> Zero-error regulation is achieved for parameter-varying nonlinear systems in the absence of PE condition.

J. Liang, Y. X. Wang, Y. J. Chen, B. J. Yang, and D. F. Liu, “A triangulation-based visual localization for field robots,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 6, pp. 1083–1086, Jun. 2022. doi: 10.1109/JAS.2022.105632 

K. N. Zhang, J. Y. Ma, and J. J. Jiang, “Loop closure detection with reweighting NetVLAD and local motion and structure consensus,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 6, pp. 1087–1090, Jun. 2022. doi: 10.1109/JAS.2022.105635 

H. C. Ji and Z. Y. Zuo, “Multiview locally linear embedding for spectral-spatial dimensionality reduction of hyperspectral imagery,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 6, pp. 1091–1094, Jun. 2022. doi: 10.1109/JAS.2022.105638 

Y. Yuan, L. Y. Shi, and W. L. He, “A linear algorithm for quantized event-triggered optimization over directed networks,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 6, pp. 1095–1098, Jun. 2022. doi: 10.1109/JAS.2022.105614 

B. Chen, Y. W. Tan, Z. Sun, and L. Yu, “Attack-resilient control against FDI attacks in cyber-physical systems,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 6, pp. 1099–1102, Jun. 2022. doi: 10.1109/JAS.2022.105641 

B. Jiang, H. L. Dong, Y. X. Shen, and S. J. Mu, “Encoding-decoding-based recursive filtering for fractional-order systems,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 6, pp. 1103–1106, Jun. 2022. doi: 10.1109/JAS.2022.105644 

S. Li, Y. Liu, and X. B. Qu, “Model controlled prediction: A reciprocal alternative of model predictive control,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 6, pp. 1107–1110, Jun. 2022. doi: 10.1109/JAS.2022.105611 

L. Yang, Z. W. Liu, T. F. Zhou, and Q. Song, “Part decom- position and refinement network for human parsing,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 6, pp. 1111–1114, Jun. 2022. doi: 10.1109/JAS.2022.105647