本期导读

主题

非线性过滤、神经网络、H_∞ 控制、开关系统、多智能体强化学习、合作运动控制、自触发控制、多机器人、模型预测控制、三维点云数据集...

全球科研机构

英国Brunel University London；新加坡National University of Singapore；中国科学院大学、清华大学、北京大学、东北大学、哈尔滨工业大学、北京航空航天大学、北京理工大学、北京师范大学、西安电子科技大学、大连海事大学、华东理工大学、东南大学、西北工业大学、北京化工大学、北方工业大学、武汉理工大学、华北电力大学、重庆邮电大学...

W. Song, Z. Wang, Z. Li, J. Wang, and  Q.-L. Han,  “Nonlinear filtering with sample-based approximation under constrained communication: Progress, insights and trends,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 7, pp. 1539–1556, Jul. 2024.

> Offer a systematic, concise and accessible review on the latest developments of the sample-based networked nonlinear filtering schemes under communication constraints.

> Summarize the up-to-date results from three engineering-oriented aspects, namely, incomplete/imperfect information compensation, resource saving, and security preservation.

> Point out some challenging and promising directions to facilitate the future research.

J.-X. Zhang, K.-D. Xu, and  Q.-G. Wang,  “Prescribed performance tracking control of time-delay nonlinear systems with output constraints,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 7, pp. 1557–1565, Jul. 2024. 

> Admissible time delays by our approach need only to be bounded and locally Lipschitz continuous, without additional requirements.

> Achieves reference tracking with preassigned performance and under output constraints, in the case where the reference is not known a priori.

> Exhibits a significant simplicity without estimation, adaption, identification, approximation, filtering, etc, despite unknown system dynamics.

Y. Zhang, Z. Wang, L. Zou, Y. Chen, and  G. Lu,  “Ultimately bounded output feedback control for networked nonlinear systems with unreliable communication channel: A buffer-aided strategy,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 7, pp. 1566–1578, Jul. 2024. 

> An intricately devised ADP-based output-feedback control scheme is introduced to address system dynamics constrained by limited communication capacity and the buffer-aided strategy.

> An adaptive tuning law is designed for the controller.

> Ultimate boundedness affected by unreliable communication channels and the buffer-aided strategy are rigorously analyzed.

S. Lu, T. Wu, L. Zhang, J. Yang, and Y. Liang, “Interpolated bumpless transfer control for asynchronously switched linear systems,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 7, pp. 1579–1590, Jul. 2024. 

> A new BTC scheme called IBTC is proposed.

> IBTC does not necessarily run through the full interval of subsystems.

> Possesses time-varying controller gains with more flexibility and less conservatism.

K. Jiang, W. Liu, Y.  Wang, L. Dong, and  C. Sun,  “Discovering latent variables for the tasks with confounders in multi-agent reinforcement learning,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 7, pp. 1591–1604, Jul. 2024. 

> Provides a more clear representation of the latent variable space in multi-agent reinforcement learning tasks with confounding factors.

> A latent variable discovery method is proposed to infer the distribution of latent variables from a large amount of offline experience, enhancing the agent's capability to explore complex tasks.

> Infers specific latent variables for each agent based on the environment at different times and states, utilizing them to expand the observation space of each agent.

Y. Lian, X. Xiao, J. Zhang, L. Jin, J. Yu, and  Z. Sun,  “Neural dynamics for cooperative motion control of omnidirectional mobile manipulators in the presence of noises: A distributed approach,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 7, pp. 1605–1620, Jul. 2024. 

> Distributed communication for multiple omnidirectional mobile manipulators.

> Cooperative quadratic planning for repetitive motion and trajectory tracking.

> Neural dynamics-based cooperative control of multi-mobile manipulators.

W. He and  Y. Wang,  “Distributed optimal variational GNE seeking in merely monotone games,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 7, pp. 1621–1630, Jul. 2024. 

> An algorithm is proposed to seek the optimal GNE in a distributed manner.

> Each agent adjusts its update step size solely based on its local information.

> An algorithm, augmented with relaxation acceleration scheme, is also proposed to expedite the convergence speed.

R. Zhao, J.-e Feng, and  D. Zhang,  “Self-triggered set stabilization of Boolean control networks and its applications,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 7, pp. 1631–1642, Jul. 2024. 

> Graphical criteria with lower computational complexity for the set stabilization are provided.

> Minimum-time and minimum-triggering self-triggered set stabilizers are designed.

> As applications, self-triggered synchronization and output tracking/regulation are discussed.

M. Zhou, Z. Wang, J. Wang, and  Z. Cao,  “Multi-robot collaborative hunting in cluttered environments with obstacle-avoiding Voronoi cells,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 7, pp. 1643–1655, Jul. 2024. 

> Proposes a distributed cooperative hunting algorithm based on a BVC based on the real-time distance between the pursuers and other obstacles.

> Construction process of BVC based on a SVM method is introduced in detail.

> An optimal matching solution between pursuers and hunting points is designed based on the Hungarian algorithm.

B. Zhu, X. Yuan, L. Dai, and  Z. Qiang,  “Finite-time stabilization for constrained discrete-time systems by using model predictive control,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 7, pp. 1656–1666, Jul. 2024. 

> Proposed MPC guarantees finite-time convergence for systems subject to constraints.

> Proposed MPC is applicable to finite-time stabilization for constrained under-actuated vector systems.

> Proposed finite-time MPC is applicable for constrained nonlinear systems, and calculation for diffeomorphism can be avoided.

B. Xu, J. Yin, C. Lian, Y. Su, and  Z. Zeng,  “Low-rank optimal transport for robust domain adaptation,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 7, pp. 1667–1680, Jul. 2024. 

> A low-rank optimal transport algorithm is presented for the robust domain adaptation problem.

> Discrete formulation of optimal transport with low-rank constraints is solved by the Augmented Lagrange Multiplier method.

> Rank constraint on the transport matrix recovers the corrupted subspace structures and extracts the class structure information.

Q. Zhang, L. Wang, H. Meng, W. Zhang, and  G. Huang,  “A LiDAR point clouds dataset of ships in a maritime environment,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 7, pp. 1681–1694, Jul. 2024. 

> Released the first-ever LiDAR ship point cloud dataset used for ship perception.

> Dataset includes both real-world collected data and simulated data.

> Simulated data models rainy and foggy weather, compensating for the lack of collected data.

S. Gao, Z. Peng, H. Wang, L. Liu, and  D. Wang,  “Long duration coverage control of multiple robotic surface vehicles under battery energy constraints,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 7, pp. 1695–1698, Jul. 2024. 

Z. Gong and F. Yang, “Secure tracking control via fixed-time convergent reinforcement learning for a UAV CPS,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 7, pp. 1699–1701, Jul. 2024. 

J. Lu, L. Li, Q. Wei, and F.-Y. Wang, “Deep reinforcement learning or Lyapunov analysis? A preliminary comparative study on event-triggered optimal control,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 7, pp. 1702–1704, Jul. 2024. 

Y. Guo, W. Xu, H. Wang, J. Lu, and S. Du, “Privacy-preserving average consensus algorithm under round-robin scheduling protocol,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 7, pp. 1705–1707, Jul. 2024. 

S. Tong, D. Qian, K. Yuan, D. Liu, Y. Li, and J. Zhang, “Fuzzy-inverse-model-based networked tracking control frameworks of time-varying signals,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 7, pp. 1708–1710, Jul. 2024. 

D.-W. Zhang and G.-P. Liu, “Disturbance observer-based predictive tracking control of uncertain HOFA cyber-physical systems,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 7, pp. 1711–1713, Jul. 2024. 

J. Yu, Q. Li, and W. Zhou, “Nonlinear robust stabilization of ship roll by convex optimization,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 7, pp. 1714–1716, Jul. 2024. 

J. Chen, Y. Yuan, and X. Luo, “SDGNN: Symmetry-preserving dual-stream graph neural networks,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 7, pp. 1717–1719, Jul. 2024. 

Y. Lin, G. Hu, L. Wang, Q. Li, and J. Zhu, “A multi-AGV routing planning method based on deep reinforcement learning and recurrent neural network,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 7, pp. 1720–1722, Jul. 2024.