本期导读

主题

线性系统，网络化控制，机器人控制，多智能体，进化计算，多目标优化，非线性系统，鲁棒视觉跟踪...

全球科研机构

德国Bielefeld University；法国Universite Claude Bernard Lyon 1、Universite de Poitiers、Nantes Universite；加拿大University of Waterloo；清华大学、中科院沈阳自动化所、哈尔滨工业大学、同济大学、西安电子科技大学、中山大学、西安交通大学、华中科技大学、湖南大学...

X. B. Ping, J. C. Hu, T. Y. Lin, B. C. Ding, P. Wang, and  Z. W. Li,  “A survey of output feedback robust MPC for linear parameter varying systems,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 10, pp. 1717–1751, Oct. 2022. 

doi: 10.1109/JAS.2022.105605

> Different classifications of output feedback robust model predictive control approaches for linear parameter varying systems and the related uncertain systems are summarized and compared.

> Methods of dealing with system uncertainties and physical constraints in different classifications of output feedback robust model predictive control optimizations for linear parameter varying systems are given.

> Key issues on output feedback robust model predictive control optimizations for linear parameter varying systems are discussed.

 

X. L. Zhu, W. Hu, Z. J. Deng, J. W. Zhang, F. Q. Hu, R. Zhou, K. Q. Li, and  F.-Y. Wang,“Interaction-aware cut-in trajectory prediction and risk assessment in mixed traffic,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 10, pp. 1752–1762, Oct. 2022.

doi: 10.1109/JAS.2022.105866 

> An integrated trajectory prediction and risk assessment framework is proposed.

> A hierarchical interaction-aware prediction method SVM-GMM is proposed.

> A new risk assessment method is proposed to assess the threat of a cut-in maneuver by combining different risk measurements to compensate the deficiency of the existing risk measures and improve the adaptability for various driving environments.

Y. Deng, V. Léchappé, C. D. Zhang, E. Moulay, D. J. Du, F. Plestan, and Q.-L. Han, “Designing discrete predictor-based controllers for networked control systems with time-varying delays: Application to a visual servo inverted pendulum system,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 10, pp. 1763–1777, Oct. 2022. 

doi: 10.1109/JAS.2021.1004249 

> A discrete-time control solution (state prediction and discrete predictor-based controller) is designed to stabilize a class of LTI networked control systems, and this method is applied to a networked visual servo inverted pendulum system.

> State prediction technique is used to deal with the sensor-to-controller time-varying delay, and the uncertain constant controller-to-actuator delay is compensated for using the predictor-based controller.

N. Tan, P. Yu, Z. Y. Zhong, and  F. L. Ni,  “A new noise-tolerant dual-neural-network scheme for robust kinematic control of robotic arms with unknown models,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 10, pp. 1778–1791, Oct. 2022. 

doi: 10.1109/JAS.2022.105869 

> A new IENT-ZNN model with better convergence and robustness is designed.

> A model-free control system is devised based on the IENT-ZNN for rigid-link and continuum robots.

> The finite-time convergence and noise-tolerant capability of the proposed method are proven.

X. Jiang, X. L. Zeng, J. Sun, J. Chen, and  Y. Wei,  “A fully distributed hybrid control framework for non-differentiable multi-agent optimization,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 10, pp. 1792–1800, Oct. 2022. 

doi: 10.1109/JAS.2022.105872 

> Provides a fully distributed hybrid framework for solving the general large-scale constrained optimization problem, whose objective functions may be non-differentiable and non-strongly-convex.

> Provide complete and rigorous convergence proofs for the proposed distributed hybrid method with the invariance principle for hybrid dynamical systems.

> Extended the existing hybrid works on consensus problems to distributed optimization problems.

Y. Tian, H. W. Chen, H. P. Ma, X. Y. Zhang, K. C. Tan, and  Y. C. Jin,  “Integrating conjugate gradients into evolutionary algorithms for large-scale continuous multi-objective optimization,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 10, pp. 1801–1817, Oct. 2022. 

doi: 10.1109/JAS.2022.105875 

> Deeply integrates evolutionary computation (EC) with mathematical programming (MP).

> Customizes a hybrid algorithm for large-scale multi-objective optimization.

> Proposed algorithm inherits the good convergence of MP and good diversity of EC.

Z. N. Li, Y. J. Li, B. Y. Tan, S. X. Ding, and  S. L. Xie,  “Structured sparse coding with the group log-regularizer for key frame extraction,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 10, pp. 1818–1830, Oct. 2022. 

doi: 10.1109/JAS.2022.105602 

> Propose a novel model of structured sparse-codingbased key frame extraction, wherein a nonconvex group log-regularizer is used with strong sparsity and a low reconstruction error.

> To automatically extract key frames, a decomposition scheme is designed to separate the sparse coefficient matrix by rows.

> DCA is employed to decompose the log-regularizer into the difference of two convex functions related to the l1 norm. 

M. Wang, H. T. Shi, and  C. Wang,  “Distributed cooperative learning for discrete-time strict-feedback multi agent systems over directed graphs,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 10, pp. 1831–1844, Oct. 2022. 

doi: 10.1109/JAS.2022.105542 

> Two novel lemmas are developed to show the exponential convergence for two kinds of linear time-varying systems with different phenomena including the nonsymmetric system matrix and time delays.

> A novel distributed cooperative learning scheme is proposed for discrete-time strict-feedback multi-agent systems under strongly connected and directed balanced graphs.

> Proposed distributed cooperative learning scheme not only ensures that the designed adaptive neural controllers can complete tracking control tasks of all the agents, but also ensures that the neural weights converge to their common ideal values.

Z. H. Feng, L. P. Yan, Y. Q. Xia, and  B. Xiao,  “An adaptive padding correlation filter with  group feature fusion for robust visual tracking,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 10, pp. 1845–1860, Oct. 2022. 

doi: 10.1109/JAS.2022.105878 

> Discriminative ability of trackers is improved by the adaptive padding.

> Tracking precision can be predicted by the proposed features in first frames.

> Feature groups are dynamically fused to avoid distraction of single feature group.

J. Q. Yang, Z. Q. Huang, S. W. Quan, Z. G. Cao, and  Y. N. Zhang,  “RANSACs for 3D rigid registration: A comparative evaluation,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 10, pp. 1861–1878, Oct. 2022. 

doi: 10.1109/JAS.2022.105500 

> A survey of six existing 6-DoF pose estimators in the RANSAC family.

> Eight RANSAC variants for a comprehensive 3D registration evaluation.

> Summary of the merits and demerits of RANSACs for 3D rigid registration.

Y. Wang, X. M. Chen, L. Shi, Y. H. Cheng, and H. J. Wang, “A trust assessment-based distributed localization algorithm for sensor networks under deception attacks,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 10, pp. 1879–1882, Oct. 2022. 

doi: 10.1109/JAS.2022.105881

S. S. Mei, Y. Ma, X. G. Mei, J. Huang, and F. Fan, “S2-Net: Self-supervision guided feature representation learning for cross-modality images,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 10, pp. 1883–1885, Oct. 2022. 

doi: 10.1109/JAS.2022.105884 

N. Yang, B. J. Xia, Z. Han, and T. R. Wang, “A domain-guided model for facial cartoonlization,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 10, pp. 1886–1888, Oct. 2022. 

doi: 10.1109/JAS.2022.105887 

Q. Xu, Z. Fu, B. Zou, H. Z. Liu, and L. Wang, “Push-sum based algorithm for constrained convex optimization problem and its potential application in smart grid,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 10, pp. 1889–1891, Oct. 2022. 

doi: 10.1109/JAS.2022.105890 

L. Y. Fang, D. S. Zhu, J. Yue, B. Zhang, and M. He, “Geometric-spectral reconstruction learning for multi-source open-set classification with hyperspectral and LiDAR data,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 10, pp. 1892–1895, Oct. 2022. 

doi: 10.1109/JAS.2022.105893