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【当期目录】IEEE/CAA JAS 第9卷 第9期
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主题 水下机器人，非线性系统，强化学习，多智能体，自适应控制，博弈混合策略，无人驾驶飞行器，模糊控制，迁移学习... 全球科研机构 加拿大Concordia University；澳大利亚Swinburne University of Technology；日本Shibaura Institute of Technology；韩国Yonsei University；清华大学、北京大学、中科院自动化所、北京航空航天大学、北京理工大学、东北大学、武汉大学... J. Wang, Z. X. Wu, H. J. Dong, M. Tan, and J. Z. Yu, “Development and control of underwater gliding robots: A review,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 9, pp. 1543–1560, Sept. 2022. doi: 10.1109/JAS.2022.105671 > Comprehensively summarizes the current platform research status of UGRs, including traditional UGs, hybrid-driven UGs, bio-inspired UGs, thermal UGs, and other types of underwater gliders. > Outline the research status of buoyancy driven system and control methods of UGRs, including system. modeling, motion control, and gliding optimization. > Analyze the current research bottlenecks and future development of UGRs, providing certain guidance and deployment suggestions for researchers in the future research. Z. Y. Zhang, Z. B. Mo, Y. T. Chen, and J. Huang, “Reinforcement learning behavioral control for nonlinear autonomous system,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 9, pp. 1561–1573, Sept. 2022. doi: 10.1109/JAS.2022.105797 > A novel two-layer RLBC method is proposed to reduce such dependence by trial-and-error learning. > In the upper layer, a novel RLMS is proposed to learn the optimal mission priority. > In the lower layer, a RLC with identifier-actor-critic structure is designed to track the reference trajectory optimally. G. Q. Zhu, H. Q. Li, X. Y. Zhang, C. L. Wang, C.-Y. Su, and J. P. Hu, “Adaptive consensus quantized control for a class of high-order nonlinear multi-agent systems with input hysteresis and full state constraints,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 9, pp. 1574–1589, Sept. 2022. doi: 10.1109/JAS.2022.105800 > Quantized control method of the hysteretic multi-agent systems is considered and a hysteresis quantization inverse compensator is established. > First study to apply the barrier Lyapunov function to the hysteretic multi-agent systems. > Quantized control strategy constructed only requires quantizer to have sector bounded property. M. J. Hu, L. K. Bu, Y. G. Bian, H. M. Qin, N. Sun, D. P. Cao, and Z. H. Zhong, “Hierarchical cooperative control of connected vehicles: From heterogeneous parameters to heterogeneous structures,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 9, pp. 1590–1602, Sept. 2022. doi: 10.1109/JAS.2022.105536 > A hierarchical framework is proposed for cooperative control of connected vehicles. > Both model parametric and structural heterogeneity are considered in the framework. > An asymptotic stability criterion is derived in the presence of time delay. B. Ning and Q.-L. Han, “Order-preserved preset-time cooperative control: A monotone system-based approach,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 9, pp. 1603–1611, Sept. 2022. doi: 10.1109/JAS.2022.105440 > Preset-time consensus of multi-agent systems with a guarantee of collision-free. > A monotone system-based approach. > Order-preserved preset-time cooperative control with directed graphs. Y. X. Wang, S. Qiu, D. Li, C. D. Du, B.-L. Lu, and H. G. He, “Multi-modal domain adaptation variational autoencoder for EEG-based emotion recognition,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 9, pp. 1612–1626, Sept. 2022. doi: 10.1109/JAS.2022.105515 > Using multi-modal data to realize emotion recognition under small samples condition. > MMDA-VAE learns cross-domain latent representations and solves missing modality problem. > Adversarial loss and cycle-consistency loss reduce cross-domain distribution difference. Y. Liu, H. G. Zhang, Y. C. Wang, and H. J. Liang, “Adaptive containment control for fractional-order nonlinear multi-agent systems with time-varying parameters,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 9, pp. 1627–1638, Sept. 2022. doi: 10.1109/JAS.2022.105545 > Explore adaptive containment control for the nonlinear FOMASs with unknown time-varying parameters and disturbances. > A novel distributed error compensating scheme is constructed to counteract the effect of the filter errors. > Bounded estimation approach is designed to overcome the difficulty generated by time-varying parameters and disturbances in the fractional-order nonlinear system. W. Y. Ruan, H. B. Duan, and Y. M. Deng, “Autonomous maneuver decisions via transfer learning pigeon-inspired optimization for UCAVs in dogfight engagements,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 9, pp. 1639–1657, Sept. 2022. doi: 10.1109/JAS.2022.105803 > A nonlinear F-16 aircraft model with aerodynamic data is used to the control object, and automatic control system are constructed by a MATLAB/Simulink platform. > Expanded elemental maneuver library is designed utilizing a maneuvering command generator, and the control commands converter from 3-DOF aircraft model to 6-DOF aircraft model is presented. > Maneuver decision objective function is designed using the game mixed strategy, and the optimal mixed strategy is obtained by TLPIO. Besides, the convergence and time complexity of TLPIO are discussed. H. Mo, Y. H. Meng, F.-Y. Wang, and D. R. Wu, “Interval type-2 fuzzy hierarchical adaptive cruise following-control for intelligent vehicles,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 9, pp. 1658–1672, Sept. 2022. doi: 10.1109/JAS.2022.105806 > Traditional variable time headway model is improved by considering the influence of acceleration of lead car on car-following safety distance. > An IT2 FLC is designed for the upper control structure to simulate the driver's operating habits. > "Feedforward + fuzzy PI feedback" control is utilized for the lower structure to improve the tracking speed and accuracy of the desired acceleration. W. Z. Liu, L. Dong, D. Niu, and C. Y. Sun, “Efficient exploration for multi-agent reinforcement learning via transferable successor features,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 9, pp. 1673–1686, Sept. 2022. doi: 10.1109/JAS.2022.105809 > Describe the tasks in multi-agent systems with successor features. > Propose multi-agent successor features which is approximated by a global successor feature network. > Propose a new algorithms called MADDPG-SFs. Y. Ma, X. Y. Wang, W. J. Gao, Y. Du, J. Huang, and F. Fan, “Progressive fusion network based on infrared light field equipment for infrared image enhancement,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 9, pp. 1687–1690, Sept. 2022. doi: 10.1109/JAS.2022.105812 Z. G. Liu, G. X. Yuan, and X. Luo, “Symmetry and non-negativity-constrained matrix factorization for community detection,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 9, pp. 1691–1693, Sept. 2022. doi: 10.1109/JAS.2022.105794 G. C. Zhang, R. C. Nie, and J. D. Cao, “SSL-WAEIE: Self-supervised learning with weighted auto-encoding and information exchange for infrared and visible image fusion,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 9, pp. 1694–1697, Sept. 2022. doi: 10.1109/JAS.2022.105815 J. Chen, K. L. Wu, Y. Yu, and L. B. Luo, “CDP-GAN: Near-infrared and visible image fusion via color distribution preserved GAN,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 9, pp. 1698–1701, Sept. 2022. doi: 10.1109/JAS.2022.105818 F. Li, T. Zheng, N. B. He, and Q. F. Cao, “Data-driven hybrid neural fuzzy network and ARX modeling approach to practical industrial process identification,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 9, pp. 1702–1705, Sept. 2022. doi: 10.1109/JAS.2022.105821 Y. F. Chang, G. Zhai, L. L. Xiong, and B. Fu, “An extended convex combination approach for quadratic L2 performance analysis of switched uncertain linear systems,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 9, pp. 1706–1709, Sept. 2022. doi: 10.1109/JAS.2022.105824 L. L. Chen, Z. Y. Lv, X. Y. Shen, Y. H. Wu, and X.-M. Sun, “Adaptive attitude control for a CTRUAV via immersion and invariance methodology,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 9, pp. 1710–1713, Sept. 2022. doi: 10.1109/JAS.2022.105827 Z.-H. Pang, X.-Y. Zhao, J. Sun, Y. T. Shi, and G.-P. Liu, “Comparison of three data-driven NPC methods for a class of nonlinear systems,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 9, pp. 1714–1716, Sept. 2022. doi: 10.1109/JAS.2022.105830
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