While ensuring stability for linear systems is well understood, it remains a major challenge
for nonlinear systems. A general approach in such cases is to compute a combination of a …

Several learning-based control with computational intelligence strategies handle challenges
related to the difficulty of modeling complex systems or the need for control strategies with …

Machine learning-based methodologies have recently been adapted to solve control
problems. The Neural Lyapunov Control (NLC) method is one such example. This approach …

In this work, a Lyapunov-based predictive control method utilizing deep learning techniques
is proposed for driving continuous-time nonlinear processes towards the desired equilibrium …

Control Lyapunov functions are a central tool in the design and analysis of stabilizing
controllers for nonlinear systems. Constructing such functions, however, remains a …

This study presents a data-driven dynamics and robust control framework, referred to as
UAMDynCon-DT, for the accurate cloning of ground-truth dynamics and the robust control of …

Trajectory planning with a learning-based approach has emerged as a crucial element in
autonomous unmanned systems and has attracted substantial interest from both academia …

This paper presents a novel dynamic deep learning architecture integrated with Lyapunov
control to address the timing latency and constraints of deep learning. The dynamic …

Attitude stabilization is of paramount importance in the flight control of personal aerial
vehicle (PAV) in the future urban air mobility (UAM). This study proposes to adopt a deep …

The authors in Reference 11, the authors proposed a method to compute a control
Lyapunov function for nonlinear dynamics based on a deep learning robust neuro-control …