Deep reinforcement learning (DRL) has achieved tremendous success in many complex
decision-making tasks of autonomous systems with high-dimensional state and/or action …

This paper proposes the SeC-Learning Machine: Simplex-enabled safe continual learning
for safety-critical autonomous systems. The SeC-learning machine is built on Simplex logic …

Advances in deep learning have revolutionized cyber‐physical applications, including the
development of autonomous vehicles. However, real‐world collisions involving autonomous …

Autonomous vehicles are cyber-physical systems that combine embedded computing and
deep learning with physical systems to perceive the world, predict future states, and safely …

As Autonomous Vehicle (AV) development has progressed, concerns regarding the safety of
passengers and agents in their environment have risen. Each real world traffic collision …

Perception, Planning, and Control form the essential components of autonomy in advanced
air mobility. This work advances the holistic integration of these components to enhance the …

Recently, the containment control for multi-agent systems (MASs) with communication
delays has been studied. However, in these existing results, the assumptions for delays are …

Deep reinforcement learning (DRL) has demonstrated impressive success in solving
complex control tasks by synthesizing control policies from data. However, the safety and …

This paper proposes a novel model inference procedure to identify system matrix from a
single noisy trajectory over a finite-time interval. The proposed inference procedure …

In this article, a discrete fractional order adaptive law (DFOAL) is designed based on the
Caputo fractional difference to perform parameter estimation of structured uncertainties. The …