This article reviews the state-of-the-art modeling and control techniques for aerial robots
such as quadrotor systems and presents several future research directions in this area. The …

Contraction theory is an analytical tool to study differential dynamics of a non-autonomous
(ie, time-varying) nonlinear system under a contraction metric defined with a uniformly …

Graph theory analysis using electroencephalogram (EEG) signals is currently an advanced
technique for seizure prediction. Recent deep learning approaches, which fail to fully …

Physics-informed machine learning (PIML) is a set of methods and tools that systematically
integrate machine learning (ML) algorithms with physical constraints and abstract …

Model-based control requires an accurate model of the system dynamics for precisely and
safely controlling the robot in complex and dynamic environments. Moreover, in presence of …

The growing need for high-performance controllers in safety-critical applications like
autonomous driving motivated the development of formal safety verification techniques. In …

Due to the fact that it is expensive and time-consuming to annotate a large amount of data,
the available labeled data to train a deep neural network is usually scarce, resulting in the …

In this article, switched model predictive control (MPC) is proposed for nonholonomic mobile
robots with adaptive dwell time and a dual-terminal set. The dual-terminal set is used to …

Safety is always one of the most critical principles for a control system. This article
investigates a safety-critical control scheme for unknown structured systems by using the …

To meet the requirements of high accuracy and low cost of target classification in modern
warfare, and lay the foundation for target threat assessment, the article proposes a human …