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      日期:2022-11-01 09:35

      近日,中國知網(CNKI)學術精要數據庫基于中國知網資源總庫遴選各學科代表性論文,發布了2011-2022年高影響力論文。JAS自2014年創刊以來發表的論文共有259篇入選,包括47篇Top1% 高被引/高下載/高PCSI論文。限于篇幅,以下為2021-2022年入選的15篇Top1%論文,歡迎閱覽。


      G. Franzè, F. Tedesco, and D. Famularo, "Resilience against replay attacks: A distributed model predictive control scheme for networked multi-agent systems," IEEE/CAA J. Autom. Sinica, vol. 8, no. 3, pp. 628-640, Mar. 2021. 

      doi: 10.1109/JAS.2020.1003542


      In this paper, a resilient distributed control scheme against replay attacks for multi-agent networked systems subject to input and state constraints is proposed. The methodological starting point relies on a smart use of predictive arguments with a twofold aim: 1) Promptly detect malicious agent behaviors affecting normal system operations; 2) Apply specific control actions, based on predictive ideas, for mitigating as much as possible undesirable domino effects resulting from adversary operations. Specifically, the multi-agent system is topologically described by a leader-follower digraph characterized by a unique leader and set-theoretic receding horizon control ideas are exploited to develop a distributed algorithm capable to instantaneously recognize the attacked agent. Finally, numerical simulations are carried out to show benefits and effectiveness of the proposed approach.


      Q. L. Wei, X. Wang, X. N. Zhong, and N. Q. Wu, "Consensus control of leader-following multi-agent systems in directed topology with heterogeneous disturbances," IEEE/CAA J. Autom. Sinica, vol. 8, no. 2, pp. 423-431, Feb. 2021. 

      doi: 10.1109/JAS.2021.1003838


      This paper investigates the consensus problem for linear multi-agent systems with the heterogeneous disturbances generated by the Brown motion. Its main contribution is that a control scheme is designed to achieve the dynamic consensus for the multi-agent systems in directed topology interfered by stochastic noise. In traditional ways, the coupling weights depending on the communication structure are static. A new distributed controller is designed based on Riccati inequalities, while updating the coupling weights associated with the gain matrix by state errors between adjacent agents. By introducing time-varying coupling weights into this novel control law, the state errors between leader and followers asymptotically converge to the minimum value utilizing the local interaction. Through the Lyapunov directed method and It? formula, the stability of the closed-loop system with the proposed control law is analyzed. Two simulation results conducted by the new and traditional schemes are presented to demonstrate the effectiveness and advantage of the developed control method.


      K. Majdoub, F. Giri, and F.-Z. Chaoui, "Adaptive backstepping control design for semi-active suspension of half-vehicle with magnetorheological damper," IEEE/CAA J. Autom. Sinica, vol. 8, no. 3, pp. 582-596, Mar. 2021. 

      doi: 10.1109/JAS.2020.1003521 


      This paper investigates the problem of controlling half-vehicle semi-active suspension system involving a magnetorheological (MR) damper. This features a hysteretic behavior that is presently captured through the nonlinear Bouc-Wen model. The control objective is to regulate well the heave and the pitch motions of the chassis despite the road irregularities. The difficulty of the control problem lies in the nonlinearity of the system model, the uncertainty of some of its parameters, and the inaccessibility to measurements of the hysteresis internal state variables. Using Lyapunov control design tools, we design two observers to get online estimates of the hysteresis internal states and a stabilizing adaptive state-feedback regulator. The whole adaptive controller is formally shown to meet the desired control objectives. This theoretical result is confirmed by several simulations demonstrating the supremacy of the latter compared to the skyhook control and passive suspension.


      W. He, X. X. Mu, L. Zhang, and Y. Zou, "Modeling and trajectory tracking control for flapping-wing micro aerial vehicles," IEEE/CAA J. Autom. Sinica, vol. 8, no. 1, pp. 148-156, Jan. 2021. 

      doi: 10.1109/JAS.2020.1003417 


      This paper studies the trajectory tracking problem of flapping-wing micro aerial vehicles (FWMAVs) in the longitudinal plane. First of all, the kinematics and dynamics of the FWMAV are established, wherein the aerodynamic force and torque generated by flapping wings and the tail wing are explicitly formulated with respect to the flapping frequency of the wings and the degree of tail wing inclination. To achieve autonomous tracking, an adaptive control scheme is proposed under the hierarchical framework. Specifically, a bounded position controller with hyperbolic tangent functions is designed to produce the desired aerodynamic force, and a pitch command is extracted from the designed position controller. Next, an adaptive attitude controller is designed to track the extracted pitch command, where a radial basis function neural network is introduced to approximate the unknown aerodynamic perturbation torque. Finally, the flapping frequency of the wings and the degree of tail wing inclination are calculated from the designed position and attitude controllers, respectively. In terms of Lyapunov’s direct method, it is shown that the tracking errors are bounded and ultimately converge to a small neighborhood around the origin. Simulations are carried out to verify the effectiveness of the proposed control scheme.


      X. Yang, L. Shu, J. N. Chen, M. A. Ferrag, J. Wu, E. Nurellari, and K. Huang, "A survey on smart agriculture: Development modes, technologies, and security and privacy challenges," IEEE/CAA J. Autom. Sinica, vol. 8, no. 2, pp. 273-302, Feb. 2021. 

      doi: 10.1109/JAS.2020.1003536 


      With the deep combination of both modern information technology and traditional agriculture, the era of agriculture 4.0, which takes the form of smart agriculture, has come. Smart agriculture provides solutions for agricultural intelligence and automation. However, information security issues cannot be ignored with the development of agriculture brought by modern information technology. In this paper, three typical development modes of smart agriculture (precision agriculture, facility agriculture, and order agriculture) are presented. Then, 7 key technologies and 11 key applications are derived from the above modes. Based on the above technologies and applications, 6 security and privacy countermeasures (authentication and access control, privacy-preserving, blockchain-based solutions for data integrity, cryptography and key management, physical countermeasures, and intrusion detection systems) are summarized and discussed. Moreover, the security challenges of smart agriculture are analyzed and organized into two aspects: 1) agricultural production, and 2) information technology. Most current research projects have not taken agricultural equipment as potential security threats. Therefore, we did some additional experiments based on solar insecticidal lamps Internet of Things, and the results indicate that agricultural equipment has an impact on agricultural security. Finally, more technologies (5G communication, fog computing, Internet of Everything, renewable energy management system, software defined network, virtual reality, augmented reality, and cyber security datasets for smart agriculture) are described as the future research directions of smart agriculture.


      D. Zhang, G. Feng, Y. Shi, and D. Srinivasan, "Physical safety and cyber security analysis of multi-agent systems: A survey of recent advances," IEEE/CAA J. Autom. Sinica, vol. 8, no. 2, pp. 319-333, Feb. 2021. 

      doi: 10.1109/JAS.2021.1003820 


      Multi-agent systems (MASs) are typically composed of multiple smart entities with independent sensing, communication, computing, and decision-making capabilities. Nowadays, MASs have a wide range of applications in smart grids, smart manufacturing, sensor networks, and intelligent transportation systems. Control of the MASs are often coordinated through information interaction among agents, which is one of the most important factors affecting coordination and cooperation performance. However, unexpected physical faults and cyber attacks on a single agent may spread to other agents via information interaction very quickly, and thus could lead to severe degradation of the whole system performance and even destruction of MASs. This paper is concerned with the safety/security analysis and synthesis of MASs arising from physical faults and cyber attacks, and our goal is to present a comprehensive survey on recent results on fault estimation, detection, diagnosis and fault-tolerant control of MASs, and cyber attack detection and secure control of MASs subject to two typical cyber attacks. Finally, the paper concludes with some potential future research topics on the security issues of MASs.


      W. J. Zhang, J. C. Wang, and F. P. Lan, "Dynamic hand gesture recognition based on short-term sampling neural networks," IEEE/CAA J. Autom. Sinica, vol. 8, no. 1, pp. 110-120, Jan. 2021. 

      doi: 10.1109/JAS.2020.1003465 


      Hand gestures are a natural way for human-robot interaction. Vision based dynamic hand gesture recognition has become a hot research topic due to its various applications. This paper presents a novel deep learning network for hand gesture recognition. The network integrates several well-proved modules together to learn both short-term and long-term features from video inputs and meanwhile avoid intensive computation. To learn short-term features, each video input is segmented into a fixed number of frame groups. A frame is randomly selected from each group and represented as an RGB image as well as an optical flow snapshot. These two entities are fused and fed into a convolutional neural network (ConvNet) for feature extraction. The ConvNets for all groups share parameters. To learn long-term features, outputs from all ConvNets are fed into a long short-term memory (LSTM) network, by which a final classification result is predicted. The new model has been tested with two popular hand gesture datasets, namely the Jester dataset and Nvidia dataset. Comparing with other models, our model produced very competitive results. The robustness of the new model has also been proved with an augmented dataset with enhanced diversity of hand gestures.


      W. M. Chang, Y. M. Li, and S. C. Tong, "Adaptive fuzzy backstepping tracking control for flexible robotic manipulator," IEEE/CAA J. Autom. Sinica, vol. 8, no. 12, pp. 1923-1930, Dec. 2021. 

      doi: 10.1109/JAS.2017.7510886 


      In this paper, an adaptive fuzzy state feedback control method is proposed for the single-link robotic manipulator system. The considered system contains unknown nonlinear function and actuator saturation. Fuzzy logic systems (FLSs) and a smooth function are used to approximate the unknown nonlinearities and the actuator saturation, respectively. By combining the command-filter technique with the backstepping design algorithm, a novel adaptive fuzzy tracking backstepping control method is developed. It is proved that the adaptive fuzzy control scheme can guarantee that all the variables in the closed-loop system are bounded, and the system output can track the given reference signal as close as possible. Simulation results are provided to illustrate the effectiveness of the proposed approach.


      G. H. Lin, H. Y. Li, H. Ma, D. Y. Yao, and R. Q. Lu, "Human-in-the-loop consensus control for nonlinear multi-agent systems with actuator faults," IEEE/CAA J. Autom. Sinica, vol. 9, no. 1, pp. 111-122, Jan. 2022. doi: 10.1109/JAS.2020.1003596 


      This paper considers the human-in-the-loop leader-following consensus control problem of multi-agent systems (MASs) with unknown matched nonlinear functions and actuator faults. It is assumed that a human operator controls the MASs via sending the command signal to a non-autonomous leader which generates the desired trajectory. Moreover, the leader’s input is nonzero and not available to all followers. By using neural networks and fault estimators to approximate unknown nonlinear dynamics and identify the actuator faults, respectively, the neighborhood observer-based neural fault-tolerant controller with dynamic coupling gains is designed. It is proved that the state of each follower can synchronize with the leader’s state under a directed graph and all signals in the closed-loop system are guaranteed to be cooperatively uniformly ultimately bounded. Finally, simulation results are presented for verifying the effectiveness of the proposed control method.


      X. Ge, S. Xiao, Q.-L. Han, X.-M. Zhang, and D. Ding, "Dynamic event-triggered scheduling and platooning control co-design for automated vehicles over vehicular ad-hoc networks," IEEE/CAA J. Autom. Sinica, vol. 9, no. 1, pp. 31-46, Jan. 2022. 

      doi: 10.1109/JAS.2021.1004060 


      This paper deals with the co-design problem of event-triggered communication scheduling and platooning control over vehicular ad-hoc networks (VANETs) subject to finite communication resource. First, a unified model is presented to describe the coordinated platoon behavior of leader-follower vehicles in the simultaneous presence of unknown external disturbances and an unknown leader control input. Under such a platoon model, the central aim is to achieve robust platoon formation tracking with desired inter-vehicle spacing and same velocities and accelerations guided by the leader, while attaining improved communication efficiency. Toward this aim, a novel bandwidth-aware dynamic event-triggered scheduling mechanism is developed. One salient feature of the scheduling mechanism is that the threshold parameter in the triggering law is dynamically adjusted over time based on both vehicular state variations and bandwidth status. Then, a sufficient condition for platoon control system stability and performance analysis as well as a co-design criterion of the admissible event-triggered platooning control law and the desired scheduling mechanism are derived. Finally, simulation results are provided to substantiate the effectiveness and merits of the proposed co-design approach for guaranteeing a trade-off between robust platooning control performance and communication efficiency.


      Y. Z. Liu, Z. Y. Meng, Y. Zou, and M. Cao, "Visual object tracking and servoing control of a nano-scale quadrotor: System, algorithms, and experiments," IEEE/CAA J. Autom. Sinica, vol. 8, no. 2, pp. 344-360, Feb. 2021. 

      doi: 10.1109/JAS.2020.1003530 


      There are two main trends in the development of unmanned aerial vehicle (UAV) technologies: miniaturization and intellectualization, in which realizing object tracking capabilities for a nano-scale UAV is one of the most challenging problems. In this paper, we present a visual object tracking and servoing control system utilizing a tailor-made 38 g nano-scale quadrotor. A lightweight visual module is integrated to enable object tracking capabilities, and a micro positioning deck is mounted to provide accurate pose estimation. In order to be robust against object appearance variations, a novel object tracking algorithm, denoted by RMCTer, is proposed, which integrates a powerful short-term tracking module and an efficient long-term processing module. In particular, the long-term processing module can provide additional object information and modify the short-term tracking model in a timely manner. Furthermore, a position-based visual servoing control method is proposed for the quadrotor, where an adaptive tracking controller is designed by leveraging backstepping and adaptive techniques. Stable and accurate object tracking is achieved even under disturbances. Experimental results are presented to demonstrate the high accuracy and stability of the whole tracking system.


      J. Zhang, L. Pan, Q.-L. Han, C. Chen, S. Wen, and Y. Xiang, "Deep learning based attack detection for cyber-physical system cybersecurity: A survey," IEEE/CAA J. Autom. Sinica, vol. 9, no. 3, pp. 377-391, Mar. 2022. 

      doi: 10.1109/JAS.2021.1004261 


      With the booming of cyber attacks and cyber criminals against cyber-physical systems (CPSs), detecting these attacks remains challenging. It might be the worst of times, but it might be the best of times because of opportunities brought by machine learning (ML), in particular deep learning (DL). In general, DL delivers superior performance to ML because of its layered setting and its effective algorithm for extract useful information from training data. DL models are adopted quickly to cyber attacks against CPS systems. In this survey, a holistic view of recently proposed DL solutions is provided to cyber attack detection in the CPS context. A six-step DL driven methodology is provided to summarize and analyze the surveyed literature for applying DL methods to detect cyber attacks against CPS systems. The methodology includes CPS scenario analysis, cyber attack identification, ML problem formulation, DL model customization, data acquisition for training, and performance evaluation. The reviewed works indicate great potential to detect cyber attacks against CPS through DL modules. Moreover, excellent performance is achieved partly because of several high-quality datasets that are readily available for public use. Furthermore, challenges, opportunities, and research trends are pointed out for future research.


      Z. L. Yuan, X. R. Li, D. Wu, X. J. Ban, N.-Q. Wu, H.-N. Dai, and H. Wang, “Continuous-time prediction of industrial paste thickener system with differential ODE-net,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 4, pp. 686–698, Apr. 2022. 

      doi: 10.1109/JAS.2022.105464 


      It is crucial to predict the outputs of a thickening system, including the underflow concentration (UC) and mud pressure, for optimal control of the process. The proliferation of industrial sensors and the availability of thickening-system data make this possible. However, the unique properties of thickening systems, such as the non-linearities, long-time delays, partially observed data, and continuous time evolution pose challenges on building data-driven predictive models. To address the above challenges, we establish an integrated, deep-learning, continuous time network structure that consists of a sequential encoder, a state decoder, and a derivative module to learn the deterministic state space model from thickening systems. Using a case study, we examine our methods with a tailing thickener manufactured by the FLSmidth installed with massive sensors and obtain extensive experimental results. The results demonstrate that the proposed continuous-time model with the sequential encoder achieves better prediction performances than the existing discrete-time models and reduces the negative effects from long time delays by extracting features from historical system trajectories. The proposed method also demonstrates outstanding performances for both short and long term prediction tasks with the two proposed derivative types.


      L. Jin, X. Zheng, and  X. Luo,  “Neural dynamics for distributed collaborative control of manipulators with time delays,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 5, pp. 854–863, May 2022. 

      doi: 10.1109/JAS.2022.105446 


      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.


      S. P. Madruga, A. H. B. M. Tavares, S. O. D. Luiz, T. P. do Nascimento, and A. M. N. Lima, "Aerodynamic effects compensation on multi-rotor UAVs based on a neural network control allocation approach," IEEE/CAA J. Autom. Sinica, vol. 9, no. 2, pp. 295-312, Feb. 2022. 

      doi: 10.1109/JAS.2021.1004266 


      This paper shows that the aerodynamic effects can be compensated in a quadrotor system by means of a control allocation approach using neural networks. Thus, the system performance can be improved by replacing the classic allocation matrix, without using the aerodynamic inflow equations directly. The network training is performed offline, which requires low computational power. The target system is a Parrot MAMBO drone whose flight control is composed of PD-PID controllers followed by the proposed neural network control allocation algorithm. Such a quadrotor is particularly susceptible to the aerodynamics effects of interest to this work, because of its small size. We compared the mechanical torques commanded by the flight controller, i.e., the control input, to those actually generated by the actuators and established at the aircraft. It was observed that the proposed neural network was able to closely match them, while the classic allocation matrix could not achieve that. The allocation error was also determined in both cases. Furthermore, the closed-loop performance also improved with the use of the proposed neural network control allocation, as well as the quality of the thrust and torque signals, in which we perceived a much less noisy behavior.










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