A suitable control architecture for connected vehicle platoons may be seen as a promising solution for today's traffic problems, by improving road safety and traffic flow, reducing emissions and fuel consumption, and increasing driver comfort. This paper provides a comprehensive overview concerning the defining levels of a general control architecture for connected vehicle platoons, intending to illustrate the options available in terms of sensor technologies, in-vehicle networks, vehicular communication, and control solutions. Moreover, starting from the proposed control architecture, a solution that implements a Cooperative Adaptive Cruise Control (CACC) functionality for a vehicle platoon is designed. Also, two control algorithms based on the distributed model-based predictive control (DMPC) strategy and the feedback gain matrix method for the control level of the CACC functionality are proposed. The designed architecture was tested in a simulation scenario, and the obtained results show the control performances achieved using the proposed solutions suitable for the longitudinal dynamics of vehicle platoons.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10490767 | PMC |
http://dx.doi.org/10.3390/s23177576 | DOI Listing |
J Biomater Sci Polym Ed
December 2024
Sri Ramachandra Faculty of Pharmacy, Sri Ramachandra Institute of Higher Education and Research, Chennai, India.
Osteoporosis is well noted to be a universal ailment that realization impaired bone mass and micro architectural deterioration thus enhancing the probability of fracture. Despite its high incidence, its management remains highly demanding because of the multifactorial pathophysiology of the disease. This review highlights recent findings in the management of osteoporosis particularly, gene expression and hormonal control.
View Article and Find Full Text PDFJ Mol Neurosci
December 2024
Department of Neurosurgery, National Children's Medical Center (Shanghai), Children's Hospital of Fudan University, No.399 Wan Yuan Avenue, Minhang District, Shanghai, 201102, China.
Focal cortical dysplasia (FCD) II is a cortical malformation characterized by cortical architectural abnormalities, dysmorphic neurons, with or without balloon cells. Here, we systematically explored the pathophysiological role of the GATOR1 subunit NPRL3 variants including a novel mutation from iPSCs derived from one FCD II patient. Three FCD II children aged 0.
View Article and Find Full Text PDFJ Imaging
December 2024
Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), 76131 Karlsruhe, Germany.
In recent years, synthetic Computed Tomography (CT) images generated from Magnetic Resonance (MR) or Cone Beam Computed Tomography (CBCT) acquisitions have been shown to be comparable to real CT images in terms of dose computation for radiotherapy simulation. However, until now, there has been no independent strategy to assess the quality of each synthetic image in the absence of ground truth. In this work, we propose a Deep Learning (DL)-based framework to predict the accuracy of synthetic CT in terms of Mean Absolute Error (MAE) without the need for a ground truth (GT).
View Article and Find Full Text PDFJ Imaging
November 2024
Research Laboratory: Networked Objects, Control and Communication Systems, NOCCS-ENISo, National Engineering School of Sousse, University of Sousse, Soussse 4023, Tunisia.
We propose a novel architecture, Transformer Dil-DenseUNet, designed to address the challenges of accurately segmenting stroke lesions in MRI images. Precise segmentation is essential for diagnosing and treating stroke patients, as it provides critical spatial insights into the affected brain regions and the extent of damage. Traditional manual segmentation is labor-intensive and error-prone, highlighting the need for automated solutions.
View Article and Find Full Text PDFBiomimetics (Basel)
December 2024
School of Computer Science and Technology, Dongguan University of Technology, Dongguan 523808, China.
In this paper, a deep reinforcement learning (DRL) approach based on generative adversarial imitation learning (GAIL) and long short-term memory (LSTM) is proposed to resolve tracking control problems for robotic manipulators with saturation constraints and random disturbances, without learning the dynamic and kinematic model of the manipulator. Specifically, it limits the torque and joint angle to a certain range. Firstly, in order to cope with the instability problem during training and obtain a stability policy, soft actor-critic (SAC) and LSTM are combined.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!