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Your search keyword '"Yuan, Shusen"' showing total 20 results
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20 results on '"Yuan, Shusen"'

10. Three-Dimensional Event-Triggered Predefined-Time Cooperative Guidance Law.

Author

Zhang, Dingye, Yu, Hang, Dai, Keren, Yi, Wenjun, Zhang, He, Guan, Jun, and Yuan, Shusen

Subjects
PROJECTILES, ENERGY consumption, ANGLES
Abstract

To address the problem of multiple missiles attacking a maneuvering target simultaneously in three-dimensional space, we propose a new predefined-time cooperative guidance law based on an event-triggered mechanism. The settling time of the system states under this guidance law is independent of the initial states, and the upper bound of the settling time can be directly set by the explicit parameters in the guidance law. Firstly, the time-to-go estimate is taken as a consistency variable, and the communication failure and time-delay that are easily encountered during the communication process are taken into account; the event-triggered mechanism is introduced into the guidance law along the line of sight (LOS) direction, and the event-triggered threshold is given. Then, a predefined-time extended state observer is used to accurately estimate disturbances. In addition, the stability of the proposed guidance laws along and perpendicular to the LOS direction is proven by the Lyapunov theory. Finally, the superiority of the proposed guidance law introducing the event-triggered mechanism in reducing energy consumption and its effectiveness in encountering communication failure and time-delay are verified through simulations. [ABSTRACT FROM AUTHOR]

Published
2024
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11. Adaptive robust control of electromagnetic actuators with friction nonlinearity and uncertainty compensation

Author

Yuan, Shusen, Yi, Wenjun, Yang, Guolai, Yuan, Shusen, Yi, Wenjun, and Yang, Guolai

Abstract

Friction nonlinearity and uncertainty are the main factors affecting the highly performance control of electromagnetic actuators. In this paper, a nonlinear adaptive robust control strategy is proposed of electromagnetic actuators with friction nonlinearity and uncertainty compensation. First, the dynamical model of the electromagnetic actuator is established considering nonlinearity and uncertainty. Then, an adaptive robust controller is designed based on the continuously differentiable friction model to ensure that the control input is continuously and bounded. In the design of the controller, the unfavorable effects of unknown parameters in the electromagnetic actuator are eliminated by constructing a parameter adaptive law. Meanwhile, in order to improve the tracking accuracy of the electromagnetic actuator, a nonlinear robust control law is designed to ensure the robustness of the controller. The stability analysis by Lyapunov function shows that the asymptotic tracking effect can be obtained when only parameter uncertainty exists in the closed-loop system of the electromagnetic actuator, and the consistent bounded stability can be ensured when the system also exists uncertain nonlinearity. Extensive comparative results verify the effectiveness of the proposed control method.

Published
2024

12. Nonlinear Robust Control of Vehicle Stabilization System with Uncertainty Based on Neural Network.

Author

Wang, Yimin, Yuan, Shusen, Wang, Xiuye, and Yang, Guolai

Subjects
ROBUST control, SLIDING mode control, HYPERSONIC planes, FEEDFORWARD neural networks
Abstract

To effectively suppress the effects of uncertainties including unmodeled dynamics and external disturbances in the vehicle stabilization system, a nonlinear robust control strategy based on a multilayer neural network is proposed in this paper. First, the mechanical and electrical coupling dynamics model of the vehicle stabilization system, considering model uncertainty and actuator dynamics, is refined. Second, the lumped uncertainty of the vehicle stabilization system is estimated by a multi-layer neural network and compensated by feedforward control. The high robustness of the system is ensured by constructing the sliding mode feedback control law. The proposed control method overcomes the limitations of sliding mode technology and the neural network and is naturally applied to the vehicle stabilization system, avoiding the adverse effects of high-gain feedback. Based on Lyapunov theory, it is demonstrated that the proposed controller is able to achieve the desired stability tracking performance. Finally, the effectiveness of the proposed control strategy is verified by co-simulation and comparative experiments. [ABSTRACT FROM AUTHOR]

Published
2024
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13. Speech Deception Detection Based on EMD and Temporal Neural Network

Author

Jiang, Youjun, Chen, Haibo, Yuan, Shusen, Xing, Hongbo, Cao, Yewen, Wang, Deqiang, and Xiong, Hailiang

Abstract

Deceptive behaviour is a common phenomenon in human society. Research has shown that humans are not good at distinguishing deception, so studying automated deception detection techniques is a critical task. Most of the relevant technologies are susceptible to personal and environmental influences: EEG-based technologies need large and expensive equipment, facial-based technologies are sensitive with the camera’s perspective, and these reasons have somewhat limited the development of applications for deception detection technologies. In contrast, the equipment required for speech deception detection is cheap and easy to use, and the capture of speech is highly covert. Based on the application of signal decomposition algorithms in other fields such as EEG signals and speech emotion recognition, this paper proposed a signal decomposition and reconstruction method based on EMD to process the speech signal and a better deception detection performance was obtained by improving the speech quality. The comparison results with other decomposition algorithms showed that the EMD decomposition algorithm is the most suitable for our method. Across many different classification algorithms, accuracy improved by an average of 2.05% and the F1 score improved by an average of 1.7%. In addition, a new deception detector, called the TCN-LSTM network, was proposed in this paper. Experiments showed that this network organically combines the processing capability of TCN and LSTM for time series data; the recognition rate of deception detection was greatly improved, with the highest accuracy and F1 score reaching 86.2% and 86.0% under the EMD-based signal decomposition reconstruction method. Based on the research in this paper, the signal decomposition algorithms need to be further optimised for speech signals and more classification algorithms not used for this task should be tried.

Published
2023
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20. Nonlinear robust adaptive control for bidirectional stabilization system of all-electric tank with unknown actuator backlash compensation and disturbance estimation

Author

Yuan, Shusen, Deng, Wenxiang, Yao, Jianyong, and Yang, Guolai

Abstract

Since backlash nonlinearity is inevitably existing in actuators for bidirectional stabilization system of all-electric tank, it behaves more drastically in high maneuvering environments. In this work, the accurate tracking control for bidirectional stabilization system of moving all-electric tank with actuator backlash and unmodeled disturbance is solved. By utilizing the smooth adaptive backlash inverse model, a nonlinear robust adaptive feedback control scheme is presented. The unknown parameters and unmodelled disturbance are addressed separately through the derived parametric adaptive function and the continuous nonlinear robust term. Because the unknown backlash parameters are updated via adaptive function and the backlash effect can be suppressed successfully by inverse operation, which ensures the system stability. Meanwhile, the system disturbance in the high maneuverable environment can be estimated with the constructed adaptive law online improving the engineering practicality. Finally, Lyapunov-based analysis proves that the developed controller can ensure the tracking error asymptotically converges to zero even with unmodeled disturbance and unknown actuator backlash. Contrast co-simulations and experiments illustrate the advantages of the proposed approach.

Published
2023
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