Deep Neuro-Fuzzy System application trends, challenges, and future perspectives: A systematic survey

N Talpur, SJ Abdulkadir, H Alhussian… - Artificial intelligence …, 2023 - Springer
Deep neural networks (DNN) have remarkably progressed in applications involving large
and complex datasets but have been criticized as a black-box. This downside has recently …

Review of advanced guidance and control algorithms for space/aerospace vehicles

R Chai, A Tsourdos, A Savvaris, S Chai, Y **a… - Progress in Aerospace …, 2021 - Elsevier
The design of advanced guidance and control (G&C) systems for space/aerospace vehicles
has received a large amount of attention worldwide during the last few decades and will …

Unified deep learning approach for efficient intrusion detection system using integrated spatial–temporal features

PR Kanna, P Santhi - Knowledge-Based Systems, 2021 - Elsevier
Intrusion detection systems (IDS) differentiate the malicious entries from the legitimate
entries in network traffic data and helps in securing the networks. Deep learning algorithms …

Attitude tracking control for reentry vehicles using centralised robust model predictive control

R Chai, A Tsourdos, H Gao, S Chai, Y **a - Automatica, 2022 - Elsevier
In this work, a centralised robust model predictive control (CRMPC) algorithm is proposed
for reentry vehicles to track reference attitude trajectories subject to state/input constraints …

Dual-loop tube-based robust model predictive attitude tracking control for spacecraft with system constraints and additive disturbances

R Chai, A Tsourdos, H Gao, Y **a… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
In this article, the problem of optimal time-varying attitude tracking control for rigid spacecraft
with system constraints and unknown additive disturbances is considered. Through the …

Design and implementation of deep neural network-based control for automatic parking maneuver process

R Chai, A Tsourdos, A Savvaris, S Chai… - … on Neural Networks …, 2020 - ieeexplore.ieee.org
This article focuses on the design, test, and validation of a deep neural network (DNN)-
based control scheme capable of predicting optimal motion commands for autonomous …

Ast-gnn: An attention-based spatio-temporal graph neural network for interaction-aware pedestrian trajectory prediction

H Zhou, D Ren, H **a, M Fan, X Yang, H Huang - Neurocomputing, 2021 - Elsevier
Predicting pedestrian trajectories in the future is a basic research topic in many real
applications, such as video surveillance, self-driving cars, and robotic systems. There are …

Multi-objective particle swarm optimization with multi-mode collaboration based on reinforcement learning for path planning of unmanned air vehicles

X Zhang, S **a, X Li, T Zhang - Knowledge-Based Systems, 2022 - Elsevier
In order to solve the multiple unmanned aerial vehicles (UAVs) collaborative path planning
problem under complex environments with multiple constraints, the multi-objective particle …

Six-DOF spacecraft optimal trajectory planning and real-time attitude control: a deep neural network-based approach

R Chai, A Tsourdos, A Savvaris, S Chai… - IEEE transactions on …, 2019 - ieeexplore.ieee.org
This brief presents an integrated trajectory planning and attitude control framework for six-
degree-of-freedom (6-DOF) hypersonic vehicle (HV) reentry flight. The proposed framework …

Deep reinforcement learning-based air combat maneuver decision-making: literature review, implementation tutorial and future direction

X Wang, Y Wang, X Su, L Wang, C Lu, H Peng… - Artificial Intelligence …, 2024 - Springer
Nowadays, various innovative air combat paradigms that rely on unmanned aerial vehicles
(UAVs), ie, UAV swarm and UAV-manned aircraft cooperation, have received great attention …