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. 1, 2024

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PERSPECTIVE
Sustainable Mining in the Era of Artificial Intelligence
Long Chen, Yuting Xie, Yutong Wang, Shirong Ge, Fei-Yue Wang
2024, 11(1): 1-4. doi: 10.1109/JAS.2023.124182
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REVIEWS
A Tutorial on Quantized Feedback Control
Minyue Fu
2024, 11(1): 5-17. doi: 10.1109/JAS.2023.123972
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In this tutorial paper, we explore the field of quantized feedback control, which has gained significant attention due to the growing prevalence of networked control systems. These systems require the transmission of feedback information, such as measurements and control signals, over digital networks, presenting novel challenges in estimation and control design. Our examination encompasses various topics, including the minimal information needed for effective feedback control, the design of quantizers, strategies for quantized control design and estimation, achieving consensus control with quantized data, and the pursuit of high-precision tracking using quantized measurements.
Recent Progress in Reinforcement Learning and Adaptive Dynamic Programming for Advanced Control Applications
Ding Wang, Ning Gao, Derong Liu, Jinna Li, Frank L. Lewis
2024, 11(1): 18-36. doi: 10.1109/JAS.2023.123843
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Reinforcement learning (RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming (ADP) within the control community. This paper reviews recent developments in ADP along with RL and its applications to various advanced control fields. First, the background of the development of ADP is described, emphasizing the significance of regulation and tracking control problems. Some effective offline and online algorithms for ADP/adaptive critic control are displayed, where the main results towards discrete-time systems and continuous-time systems are surveyed, respectively. Then, the research progress on adaptive critic control based on the event-triggered framework and under uncertain environment is discussed, respectively, where event-based design, robust stabilization, and game design are reviewed. Moreover, the extensions of ADP for addressing control problems under complex environment attract enormous attention. The ADP architecture is revisited under the perspective of data-driven and RL frameworks, showing how they promote ADP formulation significantly. Finally, several typical control applications with respect to RL and ADP are summarized, particularly in the fields of wastewater treatment processes and power systems, followed by some general prospects for future research. Overall, the comprehensive survey on ADP and RL for advanced control applications has demonstrated its remarkable potential within the artificial intelligence era. In addition, it also plays a vital role in promoting environmental protection and industrial intelligence.
Orientation and Decision-Making for Soccer Based on Sports Analytics and AI: A Systematic Review
Zhiqiang Pu, Yi Pan, Shijie Wang, Boyin Liu, Min Chen, Hao Ma, Yixiong Cui
2024, 11(1): 37-57. doi: 10.1109/JAS.2023.123807
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Due to ever-growing soccer data collection approaches and progressing artificial intelligence (AI) methods, soccer analysis, evaluation, and decision-making have received increasing interest from not only the professional sports analytics realm but also the academic AI research community. AI brings game-changing approaches for soccer analytics where soccer has been a typical benchmark for AI research. The combination has been an emerging topic. In this paper, soccer match analytics are taken as a complete observation-orientation-decision-action (OODA) loop. In addition, as in AI frameworks such as that for reinforcement learning, interacting with a virtual environment enables an evolving model. Therefore, both soccer analytics in the real world and virtual domains are discussed. With the intersection of the OODA loop and the real-virtual domains, available soccer data, including event and tracking data, and diverse orientation and decision-making models for both real-world and virtual soccer matches are comprehensively reviewed. Finally, some promising directions in this interdisciplinary area are pointed out. It is claimed that paradigms for both professional sports analytics and AI research could be combined. Moreover, it is quite promising to bridge the gap between the real and virtual domains for soccer match analysis and decision-making.
Security and Privacy in Solar Insecticidal Lamps Internet of Things: Requirements and Challenges
Qingsong Zhao, Lei Shu, Kailiang Li, Mohamed Amine Ferrag, Ximeng Liu, Yanbin Li
2024, 11(1): 58-73. doi: 10.1109/JAS.2023.123870
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Solar insecticidal lamps (SIL) can effectively control pests and reduce the use of pesticides. Combining SIL and Internet of Things (IoT) has formed a new type of agricultural IoT, known as SIL-IoT, which can improve the effectiveness of migratory phototropic pest control. However, since the SIL is connected to the Internet, it is vulnerable to various security issues. These issues can lead to serious consequences, such as tampering with the parameters of SIL, illegally starting and stopping SIL, etc. In this paper, we describe the overall security requirements of SIL-IoT and present an extensive survey of security and privacy solutions for SIL-IoT. We investigate the background and logical architecture of SIL-IoT, discuss SIL-IoT security scenarios, and analyze potential attacks. Starting from the security requirements of SIL-IoT we divide them into six categories, namely privacy, authentication, confidentiality, access control, availability, and integrity. Next, we describe the SIL-IoT privacy and security solutions, as well as the blockchain-based solutions. Based on the current survey, we finally discuss the challenges and future research directions of SIL-IoT.
PAPERS
Anomaly-Resistant Decentralized State Estimation Under Minimum Error Entropy With Fiducial Points for Wide-Area Power Systems
Bogang Qu, Zidong Wang, Bo Shen, Hongli Dong, Hongjian Liu
2024, 11(1): 74-87. doi: 10.1109/JAS.2023.123795
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This paper investigates the anomaly-resistant decentralized state estimation (SE) problem for a class of wide-area power systems which are divided into several non-overlapping areas connected through transmission lines. Two classes of measurements (i.e., local measurements and edge measurements) are obtained, respectively, from the individual area and the transmission lines. A decentralized state estimator, whose performance is resistant against measurement with anomalies, is designed based on the minimum error entropy with fiducial points (MEEF) criterion. Specifically, 1) An augmented model, which incorporates the local prediction and local measurement, is developed by resorting to the unscented transformation approach and the statistical linearization approach; 2) Using the augmented model, an MEEF-based cost function is designed that reflects the local prediction errors of the state and the measurement; and 3) The local estimate is first obtained by minimizing the MEEF-based cost function through a fixed-point iteration and then updated by using the edge measuring information. Finally, simulation experiments with three scenarios are carried out on the IEEE 14-bus system to illustrate the validity of the proposed anomaly-resistant decentralized SE scheme.

An Incentive Mechanism for Federated Learning: A Continuous Zero-Determinant Strategy Approach
Changbing Tang, Baosen Yang, Xiaodong Xie, Guanrong Chen, Mohammed A. A. Al-qaness, Yang Liu
2024, 11(1): 88-102. doi: 10.1109/JAS.2023.123828
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As a representative emerging machine learning technique, federated learning (FL) has gained considerable popularity for its special feature of “making data available but not visible”. However, potential problems remain, including privacy breaches, imbalances in payment, and inequitable distribution. These shortcomings let devices reluctantly contribute relevant data to, or even refuse to participate in FL. Therefore, in the application of FL, an important but also challenging issue is to motivate as many participants as possible to provide high-quality data to FL. In this paper, we propose an incentive mechanism for FL based on the continuous zero-determinant (CZD) strategies from the perspective of game theory. We first model the interaction between the server and the devices during the FL process as a continuous iterative game. We then apply the CZD strategies for two players and then multiple players to optimize the social welfare of FL, for which we prove that the server can keep social welfare at a high and stable level. Subsequently, we design an incentive mechanism based on the CZD strategies to attract devices to contribute all of their high-accuracy data to FL. Finally, we perform simulations to demonstrate that our proposed CZD-based incentive mechanism can indeed generate high and stable social welfare in FL.
Distributed Nash Equilibrium Seeking Strategies Under Quantized Communication
Maojiao Ye, Qing-Long Han, Lei Ding, Shengyuan Xu, Guobiao Jia
2024, 11(1): 103-112. doi: 10.1109/JAS.2022.105857
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This paper is concerned with distributed Nash equilibrium seeking strategies under quantized communication. In the proposed seeking strategy, a projection operator is synthesized with a gradient search method to achieve the optimization of players’ objective functions while restricting their actions within required non-empty, convex and compact domains. In addition, a leader-following consensus protocol, in which quantized information flows are utilized, is employed for information sharing among players. More specifically, logarithmic quantizers and uniform quantizers are investigated under both undirected and connected communication graphs and strongly connected digraphs, respectively. Through Lyapunov stability analysis, it is shown that players’ actions can be steered to a neighborhood of the Nash equilibrium with logarithmic and uniform quantizers, and the quantified convergence error depends on the parameter of the quantizer for both undirected and directed cases. A numerical example is given to verify the theoretical results.
Feature Matching via Topology-Aware Graph Interaction Model
Yifan Lu, Jiayi Ma, Xiaoguang Mei, Jun Huang, Xiao-Ping Zhang
2024, 11(1): 113-130. doi: 10.1109/JAS.2023.123774
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Feature matching plays a key role in computer vision. However, due to the limitations of the descriptors, the putative matches are inevitably contaminated by massive outliers. This paper attempts to tackle the outlier filtering problem from two aspects. First, a robust and efficient graph interaction model, is proposed, with the assumption that matches are correlated with each other rather than independently distributed. To this end, we construct a graph based on the local relationships of matches and formulate the outlier filtering task as a binary labeling energy minimization problem, where the pairwise term encodes the interaction between matches. We further show that this formulation can be solved globally by graph cut algorithm. Our new formulation always improves the performance of previous locality-based method without noticeable deterioration in processing time, adding a few milliseconds. Second, to construct a better graph structure, a robust and geometrically meaningful topology-aware relationship is developed to capture the topology relationship between matches. The two components in sum lead to topology interaction matching (TIM), an effective and efficient method for outlier filtering. Extensive experiments on several large and diverse datasets for multiple vision tasks including general feature matching, as well as relative pose estimation, homography and fundamental matrix estimation, loop-closure detection, and multi-modal image matching, demonstrate that our TIM is more competitive than current state-of-the-art methods, in terms of generality, efficiency, and effectiveness. The source code is publicly available at http://github.com/YifanLu2000/TIM.
Adaptive Optimal Discrete-Time Output-Feedback Using an Internal Model Principle and Adaptive Dynamic Programming
Zhongyang Wang, Youqing Wang, Zdzisław Kowalczuk
2024, 11(1): 131-140. doi: 10.1109/JAS.2023.123759
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In order to address the output feedback issue for linear discrete-time systems, this work suggests a brand-new adaptive dynamic programming (ADP) technique based on the internal model principle (IMP). The proposed method, termed as IMP-ADP, does not require complete state feedback-merely the measurement of input and output data. More specifically, based on the IMP, the output control problem can first be converted into a stabilization problem. We then design an observer to reproduce the full state of the system by measuring the inputs and outputs. Moreover, this technique includes both a policy iteration algorithm and a value iteration algorithm to determine the optimal feedback gain without using a dynamic system model. It is important that with this concept one does not need to solve the regulator equation. Finally, this control method was tested on an inverter system of grid-connected LCLs to demonstrate that the proposed method provides the desired performance in terms of both tracking and disturbance rejection.
Autonomous Vehicle Platoons In Urban Road Networks: A Joint Distributed Reinforcement Learning and Model Predictive Control Approach
Luigi D’Alfonso, Francesco Giannini, Giuseppe Franzè, Giuseppe Fedele, Francesco Pupo, Giancarlo Fortino
2024, 11(1): 141-156. doi: 10.1109/JAS.2023.123705
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In this paper, platoons of autonomous vehicles operating in urban road networks are considered. From a methodological point of view, the problem of interest consists of formally characterizing vehicle state trajectory tubes by means of routing decisions complying with traffic congestion criteria. To this end, a novel distributed control architecture is conceived by taking advantage of two methodologies: deep reinforcement learning and model predictive control. On one hand, the routing decisions are obtained by using a distributed reinforcement learning algorithm that exploits available traffic data at each road junction. On the other hand, a bank of model predictive controllers is in charge of computing the more adequate control action for each involved vehicle. Such tasks are here combined into a single framework: the deep reinforcement learning output (action) is translated into a set-point to be tracked by the model predictive controller; conversely, the current vehicle position, resulting from the application of the control move, is exploited by the deep reinforcement learning unit for improving its reliability. The main novelty of the proposed solution lies in its hybrid nature: on one hand it fully exploits deep reinforcement learning capabilities for decision-making purposes; on the other hand, time-varying hard constraints are always satisfied during the dynamical platoon evolution imposed by the computed routing decisions. To efficiently evaluate the performance of the proposed control architecture, a co-design procedure, involving the SUMO and MATLAB platforms, is implemented so that complex operating environments can be used, and the information coming from road maps (links, junctions, obstacles, semaphores, etc.) and vehicle state trajectories can be shared and exchanged. Finally by considering as operating scenario a real entire city block and a platoon of eleven vehicles described by double-integrator models, several simulations have been performed with the aim to put in light the main features of the proposed approach. Moreover, it is important to underline that in different operating scenarios the proposed reinforcement learning scheme is capable of significantly reducing traffic congestion phenomena when compared with well-reputed competitors.

Learning to Branch in Combinatorial Optimization With Graph Pointer Networks
Rui Wang, Zhiming Zhou, Kaiwen Li, Tao Zhang, Ling Wang, Xin Xu, Xiangke Liao
2024, 11(1): 157-169. doi: 10.1109/JAS.2023.124113
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Traditional expert-designed branching rules in branch-and-bound (B&B) are static, often failing to adapt to diverse and evolving problem instances. Crafting these rules is labor-intensive, and may not scale well with complex problems. Given the frequent need to solve varied combinatorial optimization problems, leveraging statistical learning to auto-tune B&B algorithms for specific problem classes becomes attractive. This paper proposes a graph pointer network model to learn the branch rules. Graph features, global features and historical features are designated to represent the solver state. The graph neural network processes graph features, while the pointer mechanism assimilates the global and historical features to finally determine the variable on which to branch. The model is trained to imitate the expert strong branching rule by a tailored top-k Kullback-Leibler divergence loss function. Experiments on a series of benchmark problems demonstrate that the proposed approach significantly outperforms the widely used expert-designed branching rules. It also outperforms state-of-the-art machine-learning-based branch-and-bound methods in terms of solving speed and search tree size on all the test instances. In addition, the model can generalize to unseen instances and scale to larger instances.
Control Strategies for Digital Twin Systems
Guo-Ping Liu
2024, 11(1): 170-180. doi: 10.1109/JAS.2023.123834
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With the continuous breakthrough in information technology and its integration into practical applications, industrial digital twins are expected to accelerate their development in the near future. This paper studies various control strategies for digital twin systems from the viewpoint of practical applications. To make full use of advantages of digital twins for control systems, an architecture of digital twin control systems, adaptive model tracking scheme, performance prediction scheme, performance retention scheme, and fault tolerant control scheme are proposed. Those schemes are detailed to deal with different issues on model tracking, performance prediction, performance retention, and fault tolerant control of digital twin systems. Also, the stability of digital twin control systems is analysed. The proposed schemes for digital twin control systems are illustrated by examples.
Path Planning and Tracking Control for Parking via Soft Actor-Critic Under Non-Ideal Scenarios
Xiaolin Tang, Yuyou Yang, Teng Liu, Xianke Lin, Kai Yang, Shen Li
2024, 11(1): 181-195. doi: 10.1109/JAS.2023.123975
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Parking in a small parking lot within limited space poses a difficult task. It often leads to deviations between the final parking posture and the target posture. These deviations can lead to partial occupancy of adjacent parking lots, which poses a safety threat to vehicles parked in these parking lots. However, previous studies have not addressed this issue. In this paper, we aim to evaluate the impact of parking deviation of existing vehicles next to the target parking lot (PDEVNTPL) on the automatic ego vehicle (AEV) parking, in terms of safety, comfort, accuracy, and efficiency of parking. A segmented parking training framework (SPTF) based on soft actor-critic (SAC) is proposed to improve parking performance. In the proposed method, the SAC algorithm incorporates strategy entropy into the objective function, to enable the AEV to learn parking strategies based on a more comprehensive understanding of the environment. Additionally, the SPTF simplifies complex parking tasks to maintain the high performance of deep reinforcement learning (DRL). The experimental results reveal that the PDEVNTPL has a detrimental influence on the AEV parking in terms of safety, accuracy, and comfort, leading to reductions of more than 27%, 54%, and 26% respectively. However, the SAC-based SPTF effectively mitigates this impact, resulting in a considerable increase in the parking success rate from 71% to 93%. Furthermore, the heading angle deviation is significantly reduced from 2.25 degrees to 0.43 degrees.
Analysis and Design of Time-Delay Impulsive Systems Subject to Actuator Saturation
Chenhong Zhu, Xiuping Han, Xiaodi Li
2024, 11(1): 196-204. doi: 10.1109/JAS.2023.123720
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This paper investigates the exponential stability and performance analysis of nonlinear time-delay impulsive systems subject to actuator saturation. When continuous dynamics is unstable, under some conditions, it is shown that the system can be stabilized by a class of saturated delayed-impulses regardless of the length of input delays. Conversely, when the system is originally stable, it is shown that under some conditions, the system is robust with respect to sufficient small delayed-impulses. Moreover, the design problem of the controller with the goal of obtaining a maximized estimate of the domain of attraction is formulated via a convex optimization problem. Three examples are provided to demonstrate the validity of the main results.
Data-Driven Learning Control Algorithms for Unachievable Tracking Problems
Zeyi Zhang, Hao Jiang, Dong Shen, Samer S. Saab
2024, 11(1): 205-218. doi: 10.1109/JAS.2023.123756
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For unachievable tracking problems, where the system output cannot precisely track a given reference, achieving the best possible approximation for the reference trajectory becomes the objective. This study aims to investigate solutions using the P-type learning control scheme. Initially, we demonstrate the necessity of gradient information for achieving the best approximation. Subsequently, we propose an input-output-driven learning gain design to handle the imprecise gradients of a class of uncertain systems. However, it is discovered that the desired performance may not be attainable when faced with incomplete information. To address this issue, an extended iterative learning control scheme is introduced. In this scheme, the tracking errors are modified through output data sampling, which incorporates low-memory footprints and offers flexibility in learning gain design. The input sequence is shown to converge towards the desired input, resulting in an output that is closest to the given reference in the least square sense. Numerical simulations are provided to validate the theoretical findings.
Practical Prescribed Time Tracking Control With Bounded Time-Varying Gain Under Non-Vanishing Uncertainties
Dahui Luo, Yujuan Wang, Yongduan Song
2024, 11(1): 219-230. doi: 10.1109/JAS.2023.123738
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This paper investigates the prescribed-time control (PTC) problem for a class of strict-feedback systems subject to non-vanishing uncertainties. The coexistence of mismatched uncertainties and non-vanishing disturbances makes PTC synthesis nontrivial. In this work, a control method that does not involve infinite time-varying gain is proposed, leading to a practical and global prescribed time tracking control solution for the strict-feedback systems, in spite of both the mismatched and non-vanishing uncertainties. Different from methods based on control switching to avoid the issue of infinite control gain that involves control discontinuity at the switching point, in our method a softening unit is exclusively included to ensure the continuity of the control action. Furthermore, in contrast to most existing prescribed-time control works where the control scheme is only valid on a finite time interval, in this work, the proposed control scheme is valid on the entire time interval. In addition, the prior information on the upper or lower bound of ${\boldsymbol{g_{i}}}$ is not in need, enlarging the applicability of the proposed method. Both the theoretical analysis and numerical simulation confirm the effectiveness of the proposed control algorithm.
Point Cloud Classification Using Content-Based Transformer via Clustering in Feature Space
Yahui Liu, Bin Tian, Yisheng Lv, Lingxi Li, Fei-Yue Wang
2024, 11(1): 231-239. doi: 10.1109/JAS.2023.123432
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Recently, there have been some attempts of Transformer in 3D point cloud classification. In order to reduce computations, most existing methods focus on local spatial attention, but ignore their content and fail to establish relationships between distant but relevant points. To overcome the limitation of local spatial attention, we propose a point content-based Transformer architecture, called PointConT for short. It exploits the locality of points in the feature space (content-based), which clusters the sampled points with similar features into the same class and computes the self-attention within each class, thus enabling an effective trade-off between capturing long-range dependencies and computational complexity. We further introduce an inception feature aggregator for point cloud classification, which uses parallel structures to aggregate high-frequency and low-frequency information in each branch separately. Extensive experiments show that our PointConT model achieves a remarkable performance on point cloud shape classification. Especially, our method exhibits 90.3% Top-1 accuracy on the hardest setting of ScanObjectNN. Source code of this paper is available at https://github.com/yahuiliu99/PointConT.
Non-Deterministic Liveness-Enforcing Supervisor Tolerant to Sensor-Reading Modification Attacks
Dan You, Shouguang Wang
2024, 11(1): 240-248. doi: 10.1109/JAS.2023.123702
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In this paper, we study the supervisory control problem of discrete event systems assuming that cyber-attacks might occur. In particular, we focus on the problem of liveness enforcement and consider a sensor-reading modification attack (SM-attack) that may disguise the occurrence of an event as that of another event by intruding sensor communication channels. To solve the problem, we introduce non-deterministic supervisors in the paper, which associate to every observed sequence a set of possible control actions offline and choose a control action from the set randomly online to control the system. Specifically, given a bounded Petri net (PN) as the reference formalism and an SM-attack, an algorithm that synthesizes a liveness-enforcing non-deterministic supervisor tolerant to the SM-attack is proposed for the first time.
LETTERS
Protocol-Based Non-Fragile State Estimation for Delayed Recurrent Neural Networks Subject to Replay Attacks
Fan Yang, Hongli Dong, Yuxuan Shen, Xuerong Li, Dongyan Dai
2024, 11(1): 249-251. doi: 10.1109/JAS.2023.123936
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Multimodal Data-Driven Reinforcement Learning for Operational Decision-Making in Industrial Processes
Chenliang Liu, Yalin Wang, Chunhua Yang, Weihua Gui
2024, 11(1): 252-254. doi: 10.1109/JAS.2023.123741
Abstract(376) HTML (46) PDF(94)
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Heterogeneous Image Knowledge Driven Visual Perception
Lan Yan, Wenbo Zheng, Fei-Yue Wang
2024, 11(1): 255-257. doi: 10.1109/JAS.2023.123435
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Control of 2-D Semi-Markov Jump Systems: A View from Mode Generation Mechanism
Yunzhe Men, Jian Sun, Jie Chen
2024, 11(1): 258-260. doi: 10.1109/JAS.2023.123654
Abstract(148) HTML (33) PDF(23)
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Prescribed-Time Fully Distributed Nash Equilibrium Seeking Strategy in Networked Games
Cheng Qian, Lei Ding
2024, 11(1): 261-263. doi: 10.1109/JAS.2023.123933
Abstract(213) HTML (37) PDF(40)
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An Information-Based Elite-Guided Evolutionary Algorithm for Multi-Objective Feature Selection
Ziqian Wang, Shangce Gao, Zhenyu Lei, Masaaki Omura
2024, 11(1): 264-266. doi: 10.1109/JAS.2023.123810
Abstract(229) HTML (22) PDF(21)
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Fixed-Time Consensus-Based Nash Equilibrium Seeking
Mengwei Sun, Jian Liu, Lu Ren, Changyin Sun
2024, 11(1): 267-269. doi: 10.1109/JAS.2023.123900
Abstract(196) HTML (49) PDF(54)
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Reinforcement Learning-Based MAS Interception in Antagonistic Environments
Siqing Sun, Defu Cai, Hai-Tao Zhang, Ning Xing
2024, 11(1): 270-272. doi: 10.1109/JAS.2023.123798
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Autonomous Recommendation of Fault Detection Algorithms for Spacecraft
Wenbo Li, Baoling Ning
2024, 11(1): 273-275. doi: 10.1109/JAS.2023.123423
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Distributed Optimal Formation Control for Unmanned Surface Vessels by a Regularized Game-Based Approach
Jun Shi, Maojiao Ye
2024, 11(1): 276-278. doi: 10.1109/JAS.2023.123930
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