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15 results on '"Meng, Zihan"'

6. Intensity-Varied Closed-Loop Noise Stimulation for Oscillation Suppression in the Parkinsonian State.

Author

Yu, Haitao, Meng, Zihan, Li, Huiyan, Liu, Chen, and Wang, Jiang

Abstract

This work explores the effectiveness of the intensity-varied closed-loop noise stimulation on the oscillation suppression in the Parkinsonian state. Deep brain stimulation (DBS) is the standard therapy for Parkinson’s disease (PD), but its effects need to be improved. The noise stimulation has compelling results in alleviating the PD state. However, in the open-loop control scheme, the noise stimulation parameters cannot be self-adjusted to adapt to the amplitude of the synchronized neuronal activities in real time. Thus, based on the delayed-feedback control algorithm, an intensity-varied closed-loop noise stimulation strategy is proposed. Based on a computational model of the basal ganglia (BG) that can present the intrinsic properties of the BG neurons and their interactions with the thalamic neurons, the proposed stimulation strategy is tested. Simulation results show that the noise stimulation suppresses the pathological beta (12–35 Hz) oscillations without any new rhythms in other bands compared with traditional high-frequency DBS. The intensity-varied closed-loop noise stimulation has a more profound role in removing the pathological beta oscillations and improving the thalamic reliability than open-loop noise stimulation, especially for different PD states. And the closed-loop noise stimulation enlarges the parameter space of the delayed-feedback control algorithm due to the randomness of noise signals. We also provide a theoretical analysis of the effective parameter domain of the delayed-feedback control algorithm by simplifying the BG model to an oscillator model. This exploration may guide a new approach to treating PD by optimizing the noise-induced improvement of the BG dysfunction. [ABSTRACT FROM AUTHOR]

Published
2022
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7. The Controller Design of the Water-Aerial Vehicle Based on Variable Gain PID

Author

Yang Xiaofei, Ding Shihong, Liu huanxiao, and Meng Zihan

Subjects
Controller design, 020301 aerospace & aeronautics, Variable (computer science), 0203 mechanical engineering, Computer science, Control theory, 0103 physical sciences, PID controller, 02 engineering and technology, Stage (hydrology), 01 natural sciences, Stability (probability), 010305 fluids & plasmas
Abstract

Combining the advantages of unmanned aerial vehicle (UAV) and unmanned surface vehicle (USV), water-aerial vehicles satisfy the requirements of operation in water-air amphibious environment. Due to the medium characteristics of air and water are different, it is difficult to maintain stability of the amphibious vehicle in the operation stage of transition. In order to solve this problem, the mathematical models of the amphibious vehicle are established in different motion stages, and one stability controller is designed based on the variable gain PID. The simulation results show that the proposed control method is feasible and meets the requirements of the water-air amphibious vehicle.

Published
2020

9. Neural adaptive synchronization control of chaotic FitzHugh-Nagumo neurons in the external electrical stimulation

Author

Zijie Xia, Haitao Yu, Jiang Wang, Meng Zihan, and Chen Liu

Subjects
0209 industrial biotechnology, Adaptive control, Quantitative Biology::Neurons and Cognition, Artificial neural network, Computer science, Chaotic, 02 engineering and technology, Sliding mode control, Synchronization, Nonlinear system, 020901 industrial engineering & automation, Control theory, Linearization, Adaptive system, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing
Abstract

This paper presents a neural adaptive control strategy for the chaotic synchronization of two electrically coupled FitzHugh-Nagumo (FHN) neurons in the external electrical stimulation. The control scheme integrates the sliding mode control, input-output linearization technique, and neural network approximation. Through input-output linearization, a sliding mode controller is derived firstly to compensate the nonlinearity of the coupled neuronal system. Considering the nonlinearity of neural system is usually unknown in practical applications, an adaptive sliding mode control law is designed with a radial basis function (RBF) neural network to approximate the unknown system nonlinearity. The neural network parameters are updated according to the Lyapunov approach. It is shown that using the proposed control approach, chaos synchronization between two coupled neurons can be obtained. Simulation results demonstrate the effectiveness of the proposed control scheme.

Published
2019

10. Tracking of ground targets by quadrotor UAV based on UWB positioning technology

Author

Xiaofei Yang, Yan Jiaxin, Meng Zihan, and Wang Yulong

Subjects
Computer science, business.industry, 020209 energy, 020208 electrical & electronic engineering, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, ComputerApplications_COMPUTERSINOTHERSYSTEMS, 02 engineering and technology, Kalman filter, Tracking (particle physics), Positioning technology, Extended Kalman filter, Base station, Backstepping, 0202 electrical engineering, electronic engineering, information engineering, Trajectory, ComputerSystemsOrganization_SPECIAL-PURPOSEANDAPPLICATION-BASEDSYSTEMS, Computer vision, Motion planning, Artificial intelligence, business
Abstract

For UAV indoor ground target tracking problem, a new scheme based on Ultra-Wide Band (UWB) indoor positioning technology is presented. Firstly, the dynamic models of the quadrotor and maneuvering target are established. Secondly, target position measurements can be obtained by UWB indoor positioning technology. Thirdly, the Extended Kalman Filter (EKF) is adopted to estimate the target state information. Based on artificial potential field theory, an improved motion planning algorithm is also presented. At last the Backstepping control strategy is utilized to promote UAV tracking accuracy of trajectory. Simulation results show that the scheme can achieve good performance in UAV indoor ground target tracking.

Published
2019

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