While Transformer-based approaches have recently achieved notable success in super-
resolution, their extensive computational requirements impede widespread practical …

Fault detection and diagnosis (FDD) is a crucial task for ensuring the safety and efficiency of
industrial processes. We propose a novel FDD methodology for the Tennessee Eastman …

This article introduces a method for the adaptive control of a six-dimensional (6D)
hyperchaotic system using a multi-input multi-output (MIMO) approach, leveraging the deep …

Algorithms involving kernel functions, such as support vector machine (SVM), have attracted
huge attention within the artificial learning communities. The performance of these …

This study demonstrates that the Grünwald–Letnikov fractional proportional–integral–
derivative (GPID) controller outperforms traditional PID controllers in adaptive cruise control …

Effective frequency control stands as a pivotal concern in the operation of power systems,
given its direct impact on system productivity and reliability. This research unveils a novel …

Designing a robust controller for Modular Multilevel Converters (MMCs) is crucial to ensure
stability and optimal dynamic performance under various operating conditions, including …

This paper introduces a novel zero-force control method for upper-limb exoskeleton robots,
which are used in a variety of applications including rehabilitation, assistance, and human …

Aim Satellite images are significantly more accessible to collect and include a huge amount
of informative data in selected geographical areas. However, because of their vast …

This study presents a novel approach for the adaptive control of chaotic spur gear systems
using Proximal Policy Optimization (PPO) and attention-based learning. The spur gear …