Generally, dynamic model selection is implemented using algorithms that need a feedback
from the system's output; but, in many real-world applications this feedback is not available …

Sliding mode control (SMC) has been widely used to control linear and nonlinear dynamics
systems because of its robustness against parametric uncertainties and matched …

The ever-increasing growth of the number of Internet of Things (IoT) devices connected to
the network has led to the emergence of cloud computing shortcomings such as delay …

Automated pavement crack sealing plays a crucial role in road maintenance. However,
challenges remain in refining crack segmentation and sealing control accuracy. This article …

Construction robots are challenging the paradigm of labor‐intensive construction tasks.
Imitation learning (IL) offers a promising approach, enabling robots to mimic expert actions …

This study configures an architecture based on Deep Reinforcement Learning (DRL) with
the aim of providing online services to end users in Mobile Edge Computing (MEC) …

In this letter, a novel variable gain active disturbance rejection control (VGADRC) approach
is developed for solving the trajectory tracking problem of delta parallel manipulators with …

One of the common techniques to reduce the scalability problem in collaborative filtering
(CF)-based recommender systems is the clustering technique, which accelerates finding the …

The utilization of the Delta parallel robot in high-speed and high-precision applications has
been extensive, with motion stability being a critical performance measure. To address the …

To improve the control performance of servo systems under time-varying disturbances, a
nonsingular terminal super twisting sliding mode control (NTSTC) method based on a …