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F.-Y. Wang,“A scholar of dignity: Remembering peter luh,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 9, pp. 1895–1897, Sept. 2024. 

W. Yang, S. Li, and  X. Luo,  “Data driven vibration control: A review,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 9, pp. 1898–1917, Sept. 2024. 

> Introducing the theory of input shaping and several standard shapers.

> Summarizing the progress of input shaping method from designing to optimizing perspectives, where the state-of-the-art is carefully reviewed and categorized.

> Summarizing the typical evaluation metrics of data driven vibration control models, as well as the control metrics for comparing different data driven vibration control methods.

B. Esmaeili and H. Modares, “Risk-informed model-free safe control of linear parameter-varying systems,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 9, pp. 1918–1932, Sept. 2024. 

> Data-Driven Safe Control: Introduces a method for designing safe controls in nonlinear systems using direct data, without requiring system models.

> Risk-Averse Design: Implements a minimum-variance approach to enhance safety and reduce risk, favoring robust over neutral strategies.

> Practical Validation: Demonstrates effectiveness through simulations and an experimental autonomous vehicle application.

Q. Liu, X. Cui, Z. Liu, and  H. Wang,  “Cognitive navigation for intelligent mobile robots: A learning-based approach with topological memory configuration,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 9, pp. 1933–1943, Sept. 2024. 

> Introducing the theory of input shaping and several standard shapers.

> Summarizing the progress of input shaping method from designing to optimizing perspectives, where the state-of-the-art is carefully reviewed and categorized.

> Summarizing the typical evaluation metrics of data driven vibration control models, as well as the control metrics for comparing different data driven vibration control methods.

W. Yang, S. Li, and  X. Luo,  “Data driven vibration control: A review,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 9, pp. 1898–1917, Sept. 2024. 

> Introducing the theory of input shaping and several standard shapers.

> Summarizing the progress of input shaping method from designing to optimizing perspectives, where the state-of-the-art is carefully reviewed and categorized.

> Summarizing the typical evaluation metrics of data driven vibration control models, as well as the control metrics for comparing different data driven vibration control methods.

Q. Liu, X. Cui, Z. Liu, and  H. Wang,  “Cognitive navigation for intelligent mobile robots: A learning-based approach with topological memory configuration,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 9, pp. 1933–1943, Sept. 2024. 

> Introduce a learning-based visual navigation pipeline that uses topological memory.

> Construct precise topological memory by integrating perceptual and spatial relations.

> Develop a neural-based pipeline TMFT to extract topological memory for navigation.

K. Liu, Q. Peng, Y. Liu, N. Cui, and  C. Zhang,  “Explainable neural network for sensitivity analysis of lithium-ion battery smart production,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 9, pp. 1944–1953, Sept. 2024. 

> An explainable neural network is proposed to benefit battery smart production.

> Sensitivity analysis of key parameters in battery manufacturing line is performed.

> Proposed method can accurately predict battery electrode properties.

M. Xie, D. Ding, X. Ge, Q.-L. Han, H. Dong, and Y. Song, “Distributed platooning control of automated vehicles subject to replay attacks based on proportional integral observers,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 9, pp. 1954–1966, Sept. 2024. 

> A time-varying parameter and two positive scalars are introduced to describe the temporal behavior of replay attacks.

> A sufficient condition dependent on the duration and the active ratio of replay attacks is received.

> Desired gains of PIO-based controllers are designed by resorting to the solution of certain matrix inequalities only dependent on maximum and minimum eigenvalues of communication topologies.

G. Ram, “A generalized array factor for time-modulated hexagonal based antenna array geometry with novel trapezoidal switching,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 9, pp. 1967–1972, Sept. 2024. 

> Proposes the generalized AF for the hexagonal antenna array based on time modulation.

> A novel trapezoidal switching function is proposed and applied to the generalized array factor.

> Generalized equation can be utilized for the analysis and synthesis of radiation Pattern.

S. Jiang, J. Guo, Y. Wang, and  S. Yang,  “Evolutionary multi/many-objective optimisation via bilevel decomposition,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 9, pp. 1973–1986, Sept. 2024. 

> Bilevel decomposition of problems into upper-level leaders and lower-level followers.

> Active interaction between leaders and followers for fast information propagation.

> New mating selection to balance local exploitation and global exploration.

Y. Li, Y. Zhang, X. Li, and  C. Sun,  “Regional multi-agent cooperative reinforcement learning for city-level traffic grid signal control,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 9, pp. 1987–1998, Sept. 2024. 

> As to the city-level traffic signal control problem, a regional multi-agent Q-learning framework is developed to simplify the overall complex traffic signal control problem to several regional control problems.

> Based on the idea of human-machine cooperation, a dynamic zoning approach is designed to divided the entire traffic network into several strong-coupled regions.

> A lightweight spatio-temporal fusion feature extraction network is designed to achieve better cooperation inside each region.

L. Wang, Z. Li, G. Guo, and  Z. Kong,  “Target controllability of multi-layer networks with high-dimensional nodes,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 9, pp. 1999–2010, Sept. 2024. 

> Investigates target controllability in multi-layer networks with high-dimensional nodes.

> Reveals inter-layer coupling influences overall network target controllability.Provides necessary and sufficient conditions for target controllability in general structures.

> Clarifies the relationship between target, state, and output controllability.

X. Chen, B. Xu, M. Hu, Y. Bian, Y. Li, and X. Xu, “Safe efficient policy optimization algorithm for unsignalized intersection navigation,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 9, pp. 2011–2026, Sept. 2024. 

> Proposed a scalable RL algorithm for safe, efficient navigation at unsignalized intersections.

> Introduced a semantic scene graph for variable vehicle scenarios and stable learning.

> Developed a collision risk function to penalize unsafe actions and ensure safety.

Q. Yang and H. Li, “RMPC-based visual servoing for trajectory tracking of quadrotor UAVs with visibility constraints,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 9, pp. 2027–2029, Sept. 2024. 

K. Shao, K. Huang, S. Zhen, H. Sun, and R. Yu, “A novel approach for trajectory tracking control of an under-actuated quad-rotor UAV,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 9, pp. 2030–2032, Sept. 2024. 

W. Wu, D. Wu, Y. Zhang, S. Chen, and W. Zhang, “Safety-critical trajectory tracking for mobile robots with guaranteed performance,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 9, pp. 2033–2035, Sept. 2024. 

P. Wu, Y. Wang, H. Sun, and Z. Wen, “A distortion self-calibration method for binocular high dynamic light adjusting and imaging system based on digital micromirror device,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 9, pp. 2036–2038, Sept. 2024.