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

## Current Issue Vol. 9,  No. 5, 2022

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2022, 9(5): 749-762. doi: 10.1109/JAS.2022.105434
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Big data have the characteristics of enormous volume, high velocity, diversity, value-sparsity, and uncertainty, which lead the knowledge learning from them full of challenges. With the emergence of crowdsourcing, versatile information can be obtained on-demand so that the wisdom of crowds is easily involved to facilitate the knowledge learning process. During the past thirteen years, researchers in the AI community made great efforts to remove the obstacles in the field of learning from crowds. This concentrated survey paper comprehensively reviews the technical progress in crowdsourcing learning from a systematic perspective that includes three dimensions of data, models, and learning processes. In addition to reviewing existing important work, the paper places a particular emphasis on providing some promising blueprints on each dimension as well as discussing the lessons learned from our past research work, which will light up the way for new researchers and encourage them to pursue new contributions.

2022, 9(5): 763-783. doi: 10.1109/JAS.2022.105506
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Multi-agent systems can solve scientific issues related to complex systems that are difficult or impossible for a single agent to solve through mutual collaboration and cooperation optimization. In a multi-agent system, agents with a certain degree of autonomy generate complex interactions due to the correlation and coordination, which is manifested as cooperative/competitive behavior. This survey focuses on multi-agent cooperative optimization and cooperative/non-cooperative games. Starting from cooperative optimization, the studies on distributed optimization and federated optimization are summarized. The survey mainly focuses on distributed online optimization and its application in privacy protection, and overviews federated optimization from the perspective of privacy protection me- chanisms. Then, cooperative games and non-cooperative games are introduced to expand the cooperative optimization problems from two aspects of minimizing global costs and minimizing individual costs, respectively. Multi-agent cooperative and non-cooperative behaviors are modeled by games from both static and dynamic aspects, according to whether each player can make decisions based on the information of other players. Finally, future directions for cooperative optimization, cooperative/non-cooperative games, and their applications are discussed.

2022, 9(5): 784-800. doi: 10.1109/JAS.2022.105548
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A cyber physical system (CPS) is a complex system that integrates sensing, computation, control and networking into physical processes and objects over Internet. It plays a key role in modern industry since it connects physical and cyber worlds. In order to meet ever-changing industrial requirements, its structures and functions are constantly improved. Meanwhile, new security issues have arisen. A ubiquitous problem is the fact that cyber attacks can cause significant damage to industrial systems, and thus has gained increasing attention from researchers and practitioners. This paper presents a survey of state-of-the-art results of cyber attacks on cyber physical systems. First, as typical system models are employed to study these systems, time-driven and event-driven systems are reviewed. Then, recent advances on three types of attacks, i.e., those on availability, integrity, and confidentiality are discussed. In particular, the detailed studies on availability and integrity attacks are introduced from the perspective of attackers and defenders. Namely, both attack and defense strategies are discussed based on different system models. Some challenges and open issues are indicated to guide future research and inspire the further exploration of this increasingly important area.

2022, 9(5): 801-811. doi: 10.1109/JAS.2022.105551
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In this paper we focus on the target capturing problem for a swarm of agents modelled as double integrators in any finite space dimension. Each agent knows the relative position of the target and has only an estimation of its velocity and acceleration. Given that the estimation errors are bounded by some known values, it is possible to design a control law that ensures that agents enter a user-defined ellipsoidal ring around the moving target. Agents know the relative position of the other members whose distance is smaller than a common detection radius. Finally, in the case of no uncertainty about target data and homogeneous agents, we show how the swarm can reach a static configuration around the moving target. Some simulations are reported to show the effectiveness of the proposed strategy.

2022, 9(5): 812-833. doi: 10.1109/JAS.2022.105554
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The distributed nonconvex optimization problem of minimizing a global cost function formed by a sum of n local cost functions by using local information exchange is considered. This problem is an important component of many machine learning techniques with data parallelism, such as deep learning and federated learning. We propose a distributed primal-dual stochastic gradient descent (SGD) algorithm, suitable for arbitrarily connected communication networks and any smooth (possibly nonconvex) cost functions. We show that the proposed algorithm achieves the linear speedup convergence rate

\begin{document}${{{\cal{O}}(1/\sqrt{nT})}}$\end{document}

for general nonconvex cost functions and the linear speedup convergence rate

\begin{document}${\cal{O}}(1/(nT))$\end{document}

when the global cost function satisfies the Polyak-Łojasiewicz (P-Ł) condition, where T is the total number of iterations. We also show that the output of the proposed algorithm with constant parameters linearly converges to a neighborhood of a global optimum. We demonstrate through numerical experiments the efficiency of our algorithm in comparison with the baseline centralized SGD and recently proposed distributed SGD algorithms.

2022, 9(5): 834-846. doi: 10.1109/JAS.2022.105557
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The attitude regulation problem with bounded control for a class of satellites in the presence of large disturbances, with bounded moving average, is solved using a Lyapunov-like design. The analysis and design approaches are introduced in the case in which the underlying system is an integrator and are then applied to the satellite attitude regulation problem. The performance of the resulting closed-loop systems are studied in detail and it is shown that trajectories are ultimately bounded despite the effect of the persistent disturbance. Simulation results on a model of a small satellite subject to large, but bounded in moving average, disturbances are presented.

2022, 9(5): 847-853. doi: 10.1109/JAS.2021.1004377
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In this study, the bipartite time-varying output formation tracking problem for heterogeneous multi-agent systems (MASs) with multiple leaders and switching communication networks is considered. Note that the switching communication networks may be connected or disconnected. To address this problem, a novel reduced-dimensional observer-based fully distributed asynchronous dynamic edge-event-triggered output feedback control protocol is developed, and the Zeno behavior is ruled out. The theoretical analysis gives the admissible switching frequency and switching width under the proposed control protocol. Different from the existing works, the control protocol reduces the dimension of information to be transmitted between neighboring agents. Moreover, since an additional positive internal dynamic variable is introduced into the triggering functions, the control protocol can guarantee a larger inter-event time interval compared with previous results. Finally, a simulation example is given to verify the effectiveness and performance of the theoretical result.

2022, 9(5): 854-863. doi: 10.1109/JAS.2022.105446
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Time-delay phenomena extensively exist in practical systems, e.g., multi-agent systems, bringing negative impacts on their stabilities. This work analyzes a collaborative control problem of redundant manipulators with time delays and proposes a time-delayed and distributed neural dynamics scheme. Under assumptions that the network topology is fixed and connected and the existing maximal time delay is no more than a threshold value, it is proved that all manipulators in the distributed network are able to reach a desired motion. The proposed distributed scheme with time delays considered is converted into a time-variant optimization problem, and a neural dynamics solver is designed to solve it online. Then, the proposed neural dynamics solver is proved to be stable, convergent and robust. Additionally, the allowable threshold value of time delay that ensures the proposed scheme’s stability is calculated. Illustrative examples and comparisons are provided to intuitively prove the validity of the proposed neural dynamics scheme and solver.

2022, 9(5): 864-877. doi: 10.1109/JAS.2022.105560
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In this paper, we consider the robust output containment problem of linear heterogeneous multi-agent systems under fixed directed networks. A distributed dynamic observer based on the leaders’ measurable output was designed to estimate a convex combination of the leaders’ states. First, for the case of followers with identical state dimensions, distributed dynamic state and output feedback control laws were designed based on the state-coupled item and the internal model compensator to drive the uncertain followers into the leaders’ convex hull within the output regulation framework. Subsequently, we extended theoretical results to the case where followers have nonidentical state dimensions. By establishing virtual errors between the dynamic observer and followers, a new distributed dynamic output feedback control law was constructed using only the states of the compensator to solve the robust output containment problem. Finally, two numerical simulations verified the effectiveness of the designed schemes.

2022, 9(5): 878-892. doi: 10.1109/JAS.2022.105563
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Reducing the defocus blur that arises from the finite aperture size and short exposure time is an essential problem in computational photography. It is very challenging because the blur kernel is spatially varying and difficult to estimate by traditional methods. Due to its great breakthrough in low-level tasks, convolutional neural networks (CNNs) have been introduced to the defocus deblurring problem and achieved significant progress. However, previous methods apply the same learned kernel for different regions of the defocus blurred images, thus it is difficult to handle nonuniform blurred images. To this end, this study designs a novel blur-aware multi-branch network (BaMBNet), in which different regions are treated differentially. In particular, we estimate the blur amounts of different regions by the internal geometric constraint of the dual-pixel (DP) data, which measures the defocus disparity between the left and right views. Based on the assumption that different image regions with different blur amounts have different deblurring difficulties, we leverage different networks with different capacities to treat different image regions. Moreover, we introduce a meta-learning defocus mask generation algorithm to assign each pixel to a proper branch. In this way, we can expect to maintain the information of the clear regions well while recovering the missing details of the blurred regions. Both quantitative and qualitative experiments demonstrate that our BaMBNet outperforms the state-of-the-art (SOTA) methods. For the dual-pixel defocus deblurring (DPD)-blur dataset, the proposed BaMBNet achieves 1.20 dB gain over the previous SOTA method in term of peak signal-to-noise ratio (PSNR) and reduces learnable parameters by 85%. The details of the code and dataset are available at https://github.com/junjun-jiang/BaMBNet.

2022, 9(5): 893-906. doi: 10.1109/JAS.2022.105566
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This paper deals with the problem of active disturbance rejection control (ADRC) design for a class of uncertain nonlinear systems with sporadic measurements. A novel extended state observer (ESO) is designed in a cascade form consisting of a continuous time estimator, a continuous observation error predictor, and a reset compensator. The proposed ESO estimates not only the system state but also the total uncertainty, which may include the effects of the external perturbation, the parametric uncertainty, and the unknown nonlinear dynamics. Such a reset compensator, whose state is reset to zero whenever a new measurement arrives, is used to calibrate the predictor. Due to the cascade structure, the resulting error dynamics system is presented in a non-hybrid form, and accordingly, analyzed in a general sampled-data system framework. Based on the output of the ESO, a continuous ADRC law is then developed. The convergence of the resulting closed-loop system is proved under given conditions. Two numerical simulations demonstrate the effectiveness of the proposed control method.

2022, 9(5): 907-921. doi: 10.1109/JAS.2021.1003859
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2022, 9(5): 922-925. doi: 10.1109/JAS.2022.105569
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2022, 9(5): 926-929. doi: 10.1109/JAS.2022.105572
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2022, 9(5): 930-933. doi: 10.1109/JAS.2022.105503
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2022, 9(5): 934-936. doi: 10.1109/JAS.2022.105575
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2022, 9(5): 937-940. doi: 10.1109/JAS.2022.105578
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2022, 9(5): 941-944. doi: 10.1109/JAS.2022.105581
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2022, 9(5): 945-948. doi: 10.1109/JAS.2022.105584
Abstract(144) HTML (6) PDF(63)
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