Deep neural networks have recently become increasingly used for a wide range of
applications,(eg, image and video processing). The demand for edge inference is growing …

The Digital Twins offer promising solutions for smart grid challenges related to the optimal
operation, management, and control of energy assets, for safe and reliable distribution of …

The learning-based model predictive control (LB-MPC) is an effective and critical method to
solve the path tracking problem in mobile platforms under uncertain disturbances. It is well …

The Digital Twins (DTs) offer promising solutions for smart grid challenges related to the
optimal operation, management, and control of energy assets, for safe and reliable …

Training data for a deep learning (DL) neural network (NN) controller are obtained from the
input and output signals of a conventional digital controller that is designed to provide the …

Agent-based model predictive control (AMPC) has recently been proposed as a distributed
scheme that collaborates with all agents to achieve optimal holistic performance. However …

Model Predictive Control (MPC) is a method to control nonlinear systems with guaranteed
stability and constraint satisfaction but suffers from high computation times. Approximate …

Approximate model-predictive control (AMPC) aims to imitate an MPC's behavior with a
neural network, removing the need to solve an expensive optimization problem at runtime …

A variety of automatic control systems are increasingly being deployed to assist clinicians to
monitor patient functioning and enhance healthcare delivery during surgical procedures …

Traditional algorithmic methods are not appropriate to solve today's issues. Nowadays, deep-
learning-based fuzzy systems are becoming famous and effective among researchers in …