LIVIVO - The Search Portal for Life Sciences

zur deutschen Oberfläche wechseln
Advanced search

Search results

Result 1 - 2 of total 2

Search options

  1. Article ; Online: Torque control strategy of electric racing car based on acceleration intention recognition.

    Yuan, Anlu / Zhang, Tieyi / Xiong, Lingcong / Zhang, Zhipeng

    Mathematical biosciences and engineering : MBE

    2024  Volume 21, Issue 2, Page(s) 2879–2900

    Abstract: A torque control strategy based on acceleration intention recognition is proposed to address the issue of insufficient power performance in linear torque control strategies for electric racing cars, aiming to better reflect the acceleration intention of ... ...

    Abstract A torque control strategy based on acceleration intention recognition is proposed to address the issue of insufficient power performance in linear torque control strategies for electric racing cars, aiming to better reflect the acceleration intention of racing drivers. First, the support vector machine optimized by the sparrow search algorithm is used to recognize the acceleration intention, and the running mode of the racing car is divided into two types: Starting mode and driving mode. In driving mode, based on the recognition results of acceleration intention, fuzzy control is used for torque compensation. Based on the results of simulation and hardware in the loop testing, we can conclude that the support vector machine model optimized using the sparrow search algorithm can efficiently identify the acceleration intention of racing drivers. Furthermore, the torque control strategy can compensate for positive and negative torque based on the results of intention recognition, significantly improving the power performance of the racing car.
    Language English
    Publishing date 2024-03-07
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2265126-3
    ISSN 1551-0018 ; 1551-0018
    ISSN (online) 1551-0018
    ISSN 1551-0018
    DOI 10.3934/mbe.2024128
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

  2. Article ; Online: Constant Force-Tracking Control Based on Deep Reinforcement Learning in Dynamic Auscultation Environment.

    Zhang, Tieyi / Chen, Chao / Shu, Minglei / Wang, Ruotong / Di, Chong / Li, Gang

    Sensors (Basel, Switzerland)

    2023  Volume 23, Issue 4

    Abstract: Intelligent medical robots can effectively help doctors carry out a series of medical diagnoses and auxiliary treatments and alleviate the current shortage of social personnel. Therefore, this paper investigates how to use deep reinforcement learning to ... ...

    Abstract Intelligent medical robots can effectively help doctors carry out a series of medical diagnoses and auxiliary treatments and alleviate the current shortage of social personnel. Therefore, this paper investigates how to use deep reinforcement learning to solve dynamic medical auscultation tasks. We propose a constant force-tracking control method for dynamic environments and a modeling method that satisfies physical characteristics to simulate the dynamic breathing process and design an optimal reward function for the task of achieving efficient learning of the control strategy. We have carried out a large number of simulation experiments, and the error between the tracking of normal force and expected force is basically within ±0.5 N. The control strategy is tested in a real environment. The preliminary results show that the control strategy performs well in the constant force-tracking of medical auscultation tasks. The contact force is always within a safe and stable range, and the average contact force is about 5.2 N.
    MeSH term(s) Auscultation ; Reward ; Learning ; Computer Simulation
    Language English
    Publishing date 2023-02-15
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2052857-7
    ISSN 1424-8220 ; 1424-8220
    ISSN (online) 1424-8220
    ISSN 1424-8220
    DOI 10.3390/s23042186
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

To top