Preview Control for Discrete Time Systems

Ravat, Akhilesh Kumar; Dhawan, Amit; Tiwari, Manish

doi:10.1007/978-981-16-2761-3_100

Cited by 2 publications

(2 citation statements)

References 9 publications

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“…It's crucial to keep in mind that the preview controllers were created in a DT domain, where preview action is represented by time steps when working in the DT domain. Ravat et al [30] have outlined the implementation of a preview control system using preview horizon control. They provide a numerical simulation illustration related to the preview with the assistance of MATLAB.…”

Section: Literature Reviewmentioning

confidence: 99%

Feasibility and stability of preview control for 2-D discrete-time systems described by the Roesser model

2023

IJATEE

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The term "preview control" typically refers to a class of anticipatory control processes with a preview period which extends into the future [1-2].The authors in [3,4] have discussed the tracking of wheeled robots, humanoids, robotic cars, and bipedal walking robots using the preview control.For real-time applications, a preview control mechanism has been shown in [5]. Sheridan has discussed three models of preview control in [6]. An optimal finite-preview controller has been formulated by Tomizuka in [7]. By using a different technique, the above-mentioned control problems have been

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Section: Literature Reviewmentioning

confidence: 99%

Feasibility and stability of preview control for 2-D discrete-time systems described by the Roesser model

2023

IJATEE

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“…For developing intelligent environments and cutting-edge security systems, action recognition in a live video is essential. It has uses in a variety of industries, including human-machine interface [1], monitoring systems [2], and visual comprehension [3]. Voluntary and non-voluntary activities taken by people are distinguished in human behavior [4].…”

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confidence: 99%

A Real-Time 3-Dimensional Object Detection Based Human Action Recognition Model

Gupta,

Gill,

Gulia

et al. 2024

IEEE Open J. Comput. Soc.

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Computer vision technology has made significant strides recently thanks to deep learning and increased computational power. One of the most critical applications of this technology is Human Action Recognition (HAR), which finds use in developing intelligent video surveillance systems and security measures. This paper presents a deep learning-based HAR model that combines a 3D Convolutional neural network (3DCNN), an LSTM multiplicative recurrent network, and Yolov6 for real-time object detection. We have extensively trained our model using various datasets, such as NTU-RGB-D, KITTI, NTU-RGB-D 120, UCF 101, and Fused datasets. Our proposed model outperforms other state-of-the-art (SOTA) methods, including traditional CNN, Yolov6, and CNN with BiLSTM, with impressive accuracies of 98.23%, 97.65%, 98.76%, 95.45%, and 97.65% on different datasets. This paper offers a breakthrough in the field of HAR that has the potential to revolutionize video surveillance systems and security measures.

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Preview Control for Discrete Time Systems

Cited by 2 publications

References 9 publications

Feasibility and stability of preview control for 2-D discrete-time systems described by the Roesser model

Feasibility and stability of preview control for 2-D discrete-time systems described by the Roesser model

A Real-Time 3-Dimensional Object Detection Based Human Action Recognition Model

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