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 …

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 …

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 …

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 …

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 …

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 …

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 …

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 …

The path planning of unmanned aerial vehicle is a complex practical optimization problem,
which is an important part of unmanned aerial vehicle technology. For constrained path …

In order to solve the multiple unmanned aerial vehicles (UAVs) collaborative path planning
problem under complex environments with multiple constraints, the multi-objective particle …