A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation

Vol. 11,  No. 9, 2024

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EDITORIAL
Editorial A Scholar of Dignity: Remembering Peter Luh
Fei-Yue Wang
2024, 11(9): 1895-1897. doi: 10.1109/JAS.2024.124833
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REVIEWS
Data Driven Vibration Control: A Review
Weiyi Yang, Shuai Li, Xin Luo
2024, 11(9): 1898-1917. doi: 10.1109/JAS.2024.124431
Abstract(297) HTML (44) PDF(83)
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With the ongoing advancements in sensor networks and data acquisition technologies across various systems like manufacturing, aviation, and healthcare, the data driven vibration control (DDVC) has attracted broad interests from both the industrial and academic communities. Input shaping (IS), as a simple and effective feedforward method, is greatly demanded in DDVC methods. It convolves the desired input command with impulse sequence without requiring parametric dynamics and the closed-loop system structure, thereby suppressing the residual vibration separately. Based on a thorough investigation into the state-of-the-art DDVC methods, this survey has made the following efforts: 1) Introducing the IS theory and typical input shapers; 2) Categorizing recent progress of DDVC methods; 3) Summarizing commonly adopted metrics for DDVC; and 4) Discussing the engineering applications and future trends of DDVC. By doing so, this study provides a systematic and comprehensive overview of existing DDVC methods from designing to optimizing perspectives, aiming at promoting future research regarding this emerging and vital issue.

PAPERS
Risk-Informed Model-Free Safe Control of Linear Parameter-Varying Systems
Babak Esmaeili, Hamidreza Modares
2024, 11(9): 1918-1932. doi: 10.1109/JAS.2024.124479
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This paper presents a risk-informed data-driven safe control design approach for a class of stochastic uncertain nonlinear discrete-time systems. The nonlinear system is modeled using linear parameter-varying (LPV) systems. A model-based probabilistic safe controller is first designed to guarantee probabilistic $\lambda $-contractivity (i.e., stability and invariance) of the LPV system with respect to a given polyhedral safe set. To obviate the requirement of knowing the LPV system model and to bypass identifying its open-loop model, its closed-loop data-based representation is provided in terms of state and scheduling data as well as a decision variable. It is shown that the variance of the closed-loop system, as well as the probability of safety satisfaction, depends on the decision variable and the noise covariance. A minimum-variance direct data-driven gain-scheduling safe control design approach is presented next by designing the decision variable such that all possible closed-loop system realizations satisfy safety with the highest confidence level. This minimum-variance approach is a control-oriented learning method since it minimizes the variance of the state of the closed-loop system with respect to the safe set, and thus minimizes the risk of safety violation. Unlike the certainty-equivalent approach that results in a risk-neutral control design, the minimum-variance method leads to a risk-averse control design. It is shown that the presented direct risk-averse learning approach requires weaker data richness conditions than existing indirect learning methods based on system identification and can lead to a lower risk of safety violation. Two simulation examples along with an experimental validation on an autonomous vehicle are provided to show the effectiveness of the presented approach.
Cognitive Navigation for Intelligent Mobile Robots: A Learning-Based Approach With Topological Memory Configuration
Qiming Liu, Xinru Cui, Zhe Liu, Hesheng Wang
2024, 11(9): 1933-1943. doi: 10.1109/JAS.2024.124332
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Autonomous navigation for intelligent mobile robots has gained significant attention, with a focus on enabling robots to generate reliable policies based on maintenance of spatial memory. In this paper, we propose a learning-based visual navigation pipeline that uses topological maps as memory configurations. We introduce a unique online topology construction approach that fuses odometry pose estimation and perceptual similarity estimation. This tackles the issues of topological node redundancy and incorrect edge connections, which stem from the distribution gap between the spatial and perceptual domains. Furthermore, we propose a differentiable graph extraction structure, the topology multi-factor transformer (TMFT). This structure utilizes graph neural networks to integrate global memory and incorporates a multi-factor attention mechanism to underscore elements closely related to relevant target cues for policy generation. Results from photorealistic simulations on image-goal navigation tasks highlight the superior navigation performance of our proposed pipeline compared to existing memory structures. Comprehensive validation through behavior visualization, interpretability tests, and real-world deployment further underscore the adaptability and efficacy of our method.

Explainable Neural Network for Sensitivity Analysis of Lithium-ion Battery Smart Production
Kailong Liu, Qiao Peng, Yuhang Liu, Naxin Cui, Chenghui Zhang
2024, 11(9): 1944-1953. doi: 10.1109/JAS.2024.124539
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Battery production is crucial for determining the quality of electrode, which in turn affects the manufactured battery performance. As battery production is complicated with strongly coupled intermediate and control parameters, an efficient solution that can perform a reliable sensitivity analysis of the production terms of interest and forecast key battery properties in the early production phase is urgently required. This paper performs detailed sensitivity analysis of key production terms on determining the properties of manufactured battery electrode via advanced data-driven modelling. To be specific, an explainable neural network named generalized additive model with structured interaction (GAM-SI) is designed to predict two key battery properties, including electrode mass loading and porosity, while the effects of four early production terms on manufactured batteries are explained and analysed. The experimental results reveal that the proposed method is able to accurately predict battery electrode properties in the mixing and coating stages. In addition, the importance ratio ranking, global interpretation and local interpretation of both the main effects and pairwise interactions can be effectively visualized by the designed neural network. Due to the merits of interpretability, the proposed GAM-SI can help engineers gain important insights for understanding complicated production behavior, further benefitting smart battery production.

Distributed Platooning Control of Automated Vehicles Subject to Replay Attacks Based on Proportional Integral Observers
Meiling Xie, Derui Ding, Xiaohua Ge, Qing-Long Han, Hongli Dong, Yan Song
2024, 11(9): 1954-1966. doi: 10.1109/JAS.2022.105941
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Secure platooning control plays an important role in enhancing the cooperative driving safety of automated vehicles subject to various security vulnerabilities. This paper focuses on the distributed secure control issue of automated vehicles affected by replay attacks. A proportional-integral-observer (PIO) with predetermined forgetting parameters is first constructed to acquire the dynamical information of vehicles. Then, a time-varying parameter and two positive scalars are employed to describe the temporal behavior of replay attacks. In light of such a scheme and the common properties of Laplace matrices, the closed-loop system with PIO-based controllers is transformed into a switched and time-delayed one. Furthermore, some sufficient conditions are derived to achieve the desired platooning performance by the view of the Lyapunov stability theory. The controller gains are analytically determined by resorting to the solution of certain matrix inequalities only dependent on maximum and minimum eigenvalues of communication topologies. Finally, a simulation example is provided to illustrate the effectiveness of the proposed control strategy.
A Generalized Array Factor for Time-Modulated Hexagonal Based Antenna Array Geometry With Novel Trapezoidal Switching
Gopi Ram
2024, 11(9): 1967-1972. doi: 10.1109/JAS.2024.124458
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The concept of the time-modulated array has been emerging as an alternative to the complex phase shifters, which lowers the cost of the array feeding network due to the utilization of radio frequency (RF) switches. The various forms of hexagonal antenna array geometries can be used for applications like surveillance tracking in phased array radar and wireless communication systems. This work proposes the generalized array factor (AF) for the hexagonal antenna array geometry based on time modulation. The time modulation in generalized hexagonal geometry can maintain the fixed static amplitude excitation, giving more flexibility over time. Furthermore, a novel trapezoidal switching function is also proposed and applied to the generalized array factor to enable future researchers to use this array factor in the field of advancement to observe how switching schemes like trapezoidal and rectangular affect the array pattern’s side lobe level (SLL). The generalized equation can be utilized for the analysis and synthesis of radiation characteristics of the time-modulated hexagonal array (TMHA), time-modulated concentric hexagonal array (TMCHA), time-modulated hexagonal cylindrical array (TMHCA), and time-modulated hexagonal concentric cylindrical array (TMHCCA). The numerical result illustrates the generation of AF of time-modulated hexagonal structures and also shows that the trapezoidal switching sequence outperforms the rectangular switch using the cat swarm optimization (CSO) approach.
Evolutionary Multi/Many-Objective Optimisation via Bilevel Decomposition
Shouyong Jiang, Jinglei Guo, Yong Wang, Shengxiang Yang
2024, 11(9): 1973-1986. doi: 10.1109/JAS.2024.124515
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Decomposition of a complex multi-objective optimisation problem (MOP) to multiple simple subMOPs, known as M2M for short, is an effective approach to multi-objective optimisation. However, M2M facilitates little communication/collaboration between subMOPs, which limits its use in complex optimisation scenarios. This paper extends the M2M framework to develop a unified algorithm for both multi-objective and many-objective optimisation. Through bilevel decomposition, an MOP is divided into multiple subMOPs at upper level, each of which is further divided into a number of single-objective subproblems at lower level. Neighbouring subMOPs are allowed to share some subproblems so that the knowledge gained from solving one subMOP can be transferred to another, and eventually to all the subMOPs. The bilevel decomposition is readily combined with some new mating selection and population update strategies, leading to a high-performance algorithm that competes effectively against a number of state-of-the-arts studied in this paper for both multi- and many-objective optimisation. Parameter analysis and component analysis have been also carried out to further justify the proposed algorithm.

Regional Multi-Agent Cooperative Reinforcement Learning for City-Level Traffic Grid Signal Control
Yisha Li, Ya Zhang, Xinde Li, Changyin Sun
2024, 11(9): 1987-1998. doi: 10.1109/JAS.2024.124365
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This article studies the effective traffic signal control problem of multiple intersections in a city-level traffic system. A novel regional multi-agent cooperative reinforcement learning algorithm called RegionSTLight is proposed to improve the traffic efficiency. Firstly a regional multi-agent Q-learning framework is proposed, which can equivalently decompose the global Q value of the traffic system into the local values of several regions. Based on the framework and the idea of human-machine cooperation, a dynamic zoning method is designed to divide the traffic network into several strong-coupled regions according to real-time traffic flow densities. In order to achieve better cooperation inside each region, a lightweight spatio-temporal fusion feature extraction network is designed. The experiments in synthetic, real-world and city-level scenarios show that the proposed RegionSTLight converges more quickly, is more stable, and obtains better asymptotic performance compared to state-of-the-art models.

Target Controllability of Multi-Layer Networks With High-Dimensional Nodes
Lifu Wang, Zhaofei Li, Ge Guo, Zhi Kong
2024, 11(9): 1999-2010. doi: 10.1109/JAS.2023.124152
Abstract(112) HTML (26) PDF(18)
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This paper studies the target controllability of multi-layer complex networked systems, in which the nodes are high-dimensional linear time invariant (LTI) dynamical systems, and the network topology is directed and weighted. The influence of inter-layer couplings on the target controllability of multi-layer networks is discussed. It is found that even if there exists a layer which is not target controllable, the entire multi-layer network can still be target controllable due to the inter-layer couplings. For the multi-layer networks with general structure, a necessary and sufficient condition for target controllability is given by establishing the relationship between uncontrollable subspace and output matrix. By the derived condition, it can be found that the system may be target controllable even if it is not state controllable. On this basis, two corollaries are derived, which clarify the relationship between target controllability, state controllability and output controllability. For the multi-layer networks where the inter-layer couplings are directed chains and directed stars, sufficient conditions for target controllability of networked systems are given, respectively. These conditions are easier to verify than the classic criterion.

Safe Efficient Policy Optimization Algorithm for Unsignalized Intersection Navigation
Xiaolong Chen, Biao Xu, Manjiang Hu, Yougang Bian, Yang Li, Xin Xu
2024, 11(9): 2011-2026. doi: 10.1109/JAS.2024.124287
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Unsignalized intersections pose a challenge for autonomous vehicles that must decide how to navigate them safely and efficiently. This paper proposes a reinforcement learning (RL) method for autonomous vehicles to navigate unsignalized intersections safely and efficiently. The method uses a semantic scene representation to handle variable numbers of vehicles and a universal reward function to facilitate stable learning. A collision risk function is designed to penalize unsafe actions and guide the agent to avoid them. A scalable policy optimization algorithm is introduced to improve data efficiency and safety for vehicle learning at intersections. The algorithm employs experience replay to overcome the on-policy limitation of proximal policy optimization and incorporates the collision risk constraint into the policy optimization problem. The proposed safe RL algorithm can balance the trade-off between vehicle traffic safety and policy learning efficiency. Simulated intersection scenarios with different traffic situations are used to test the algorithm and demonstrate its high success rates and low collision rates under different traffic conditions. The algorithm shows the potential of RL for enhancing the safety and reliability of autonomous driving systems at unsignalized intersections.
LETTERS
RMPC-Based Visual Servoing for Trajectory Tracking of Quadrotor UAVs With Visibility Constraints
Qifan Yang, Huiping Li
2024, 11(9): 2027-2029. doi: 10.1109/JAS.2024.124533
Abstract(105) HTML (18) PDF(38)
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A Novel Approach for Trajectory Tracking Control of an Under-Actuated Quad-Rotor UAV
Ke Shao, Kang Huang, Shengchao Zhen, Hao Sun, Rongrong Yu
2024, 11(9): 2030-2032. doi: 10.1109/JAS.2016.7510238
Abstract(103) HTML (19) PDF(43)
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Safety-Critical Trajectory Tracking for Mobile Robots With Guaranteed Performance
Wentao Wu, Di Wu, Yibo Zhang, Shukang Chen, Weidong Zhang
2024, 11(9): 2033-2035. doi: 10.1109/JAS.2023.123864
Abstract(113) HTML (29) PDF(60)
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A Distortion Self-Calibration Method for Binocular High Dynamic Light Adjusting and Imaging System Based on Digital Micromirror Device
Pei Wu, Yanjie Wang, Honghai Sun, Zhuoman Wen
2024, 11(9): 2036-2038. doi: 10.1109/JAS.2017.7510433
Abstract(89) HTML (20) PDF(20)
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