Yumei Ma

Qihe People's Hospital

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Jinpeng Yu

Qingdao University

Lin Zhao

Harbin Engineering University

Jiapeng Liu

Northeast Forestry University

Chong Lin

Qingdao University

Haisheng Yu

Ludong University

Zhenkuan Pan

Qingdao University

Cheng Fu

Qingdao University

Fatao Shi

Qingdao University

Chen Song

Guangdong Institute of New Materials

Xuqun Zhang

Qingdao University

Cooperative Institutions

Qingdao University

Lanzhou University

Qingdao University of Science and Technology

Chinese Academy of Sciences

First Hospital of Lanzhou University

Huazhong University of Science and Technology

State Grid Corporation of China (China)

Shandong University

Beijing University of Technology

University of Chinese Academy of Sciences

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Adaptive Fuzzy Observer-Based Command Filtered Discrete-Time Control for PMSMs With Input Constraint

IEEE Access (2021)

Yumeng Xu Jiapeng Liu Qixin Lei Yumei Ma Jinpeng Yu

This paper proposes an adaptive fuzzy observer-based command filtered discrete-time control method for permanent magnet synchronous motors (PMSMs) with input constraint. First, the rotor angular velocity is estimated by using an adaptive fuzzy observer, and the unknown nonlinear function is approximated by using fuzzy logic systems (FLSs) in the PMSMs drive systems. Then, compared with the traditional backstepping method, command filtered backstepping method is developed to solve the “complexity of computation” problem. It is shown that all signals of the closed-loop system are semi-globally uniformly ultimately bounded. Finally, the results of the simulation and experiment demonstrate that the new design method can fully consider the influence of input constraint and load disturbance, and improve the tracking performance for PMSMs drive systems.

Observer (physics)

Adaptive system

10.1109/access.2021.3114554

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Citations (3)

Full State Constraints-based Adaptive Fuzzy Finite-time Command Filtered Control for Permanent Magnet Synchronous Motor Stochastic Systems

International Journal of Control Automation and Systems (2022)

Qi Jiang Yumei Ma Jiapeng Liu Jinpeng Yu

Synchronous motor

10.1007/s12555-021-0558-2

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Citations (7)

Stochastic Resonance in an Array of Dynamical Saturating Nonlinearity with Second-Order

Proceedings of International Conference on Artificial Life and Robotics (2017)

Yumei Ma Lin Zhao Zhenkuan Pan Jinpeng Yu

Stochastic Resonance

Nonlinear resonance

10.5954/icarob.2017.os14-5

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Citations (0)

Command filter and observer-based adaptive neural networks control for PMSMs

2022 13th Asian Control Conference (ASCC) (2017)

Hao Niu Cheng Fu Yumei Ma Haisheng Yu Lin Zhao

In this paper, a command filter and observer-based adaptive neural networks control scheme is put forward for the permanent magnet synchronous motors (PMSMs). The radial basis function (RBF) neural networks (NNs) approximation method are exploited to approximate the nonlinear terms of the PMSMs drive systems and an observer combined with RBF NNs is designed to estimate the rotor angular velocity of the PMSMs. Then, the command filtered control (CFC) technology is utilized to overcome the "explosion of complexity" problem inherent in the traditional backstepping control and decrease the filtering errors. Furthermore, the adaptive NNs backstepping method is employed to construct the controllers to guarantee the tracking error converge to a small neighborhood of the origin. Finally, simulation results illustrate the effectiveness of the proposed approach.

Tracking error

Observer (physics)

10.1109/ascc.2017.8287490

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Citations (0)

Full-state Constraints-based Neuroadaptive Finite-time Control for Induction Motor Drive Systems with Iron Losses

International Journal of Control Automation and Systems (2022)

Chen Song Jinpeng Yu Lin Zhao Yumei Ma

10.1007/s12555-021-0043-y

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Citations (3)

Research on Image Signal Identification Based on Adaptive Array Stochastic Resonance

Journal of Systems Science and Complexity (2021)

Jingjing Zhao Yumei Ma Zhenkuan Pan Huage Zhang

SIGNAL (programming language)

Stochastic Resonance

10.1007/s11424-021-0133-1

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Citations (13)

Neuroadaptive observer-based discrete-time command filtered fault-tolerant control for induction motors with load disturbances

Neurocomputing (2020)

Qixin Lei Yumei Ma Jiapeng Liu Jinpeng Yu

Observer (physics)

Position (finance)

10.1016/j.neucom.2020.10.085

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Citations (11)

Image Restoration Based on Stochastic Resonance in a Parallel Array of Fitzhugh–Nagumo Neuron

Complexity (2020)

Huage Zhang Jinfei Yu Yumei Ma Zhenkuan Pan Jingjing Zhao

The poor denoising effect for noisy grayscale images with traditional processing methods would be obtained under strong noise condition, and some image details would be lost. In this paper, a parallel array model of Fitzhugh–Nagumo (FHN) neurons was proposed, which can restore noisy grayscale images well with low peak signal-to-noise ratio (PSNR) conditions and the image details are better preserved. Firstly, the row-column scanning method was used to convert the 2D grayscale image into a 1D signal, and then the 1D signal was converted into a binary pulse amplitude modulation (BPAM) signal by signal modulation. The modulated signal was input to an FHN parallel array for stochastic resonance (SR). Finally, the array output signal was restored to a 2D gray image, and the image restoration effect was analyzed based on the PSNR and Structural SIMilarity (SSIM) index. It is shown that the SR effect can be exhibited in an array of FHN neuron nonlinearities by increasing the array size, and the image restoration effect is significantly better than the traditional image restoration method, and larger PSNR and SSIM can be obtained. It provides a new idea for grayscale image restoration in a low PSNR environment.

SIGNAL (programming language)

Peak signal-to-noise ratio

Stochastic Resonance

10.1155/2020/8843950

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Citations (7)

Comparison of stochastic resonance in static and dynamical nonlinearities

Physics Letters A (2014)

Yumei Ma Fabing Duan

Stochastic Resonance

10.1016/j.physleta.2014.07.025

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