Enforcing hard constraints with soft barriers: Safe reinforcement learning in unknown stochastic environments
It is quite challenging to ensure the safety of reinforcement learning (RL) agents in an
unknown and stochastic environment under hard constraints that require the system state …
unknown and stochastic environment under hard constraints that require the system state …
Efficient global robustness certification of neural networks via interleaving twin-network encoding
The robustness of deep neural networks has received significant interest recently, especially
when being deployed in safety-critical systems, as it is important to analyze how sensitive …
when being deployed in safety-critical systems, as it is important to analyze how sensitive …
Physics-aware safety-assured design of hierarchical neural network based planner
Neural networks have shown great promises in planning, control, and general decision
making for learning-enabled cyber-physical systems (LE-CPSs), especially in improving …
making for learning-enabled cyber-physical systems (LE-CPSs), especially in improving …
Joint differentiable optimization and verification for certified reinforcement learning
Model-based reinforcement learning has been widely studied for controller synthesis in
cyber-physical systems (CPSs). In particular, for safety-critical CPSs, it is important to …
cyber-physical systems (CPSs). In particular, for safety-critical CPSs, it is important to …
Design-while-verify: correct-by-construction control learning with verification in the loop
In the current control design of safety-critical cyber-physical systems, formal verification
techniques are typically applied after the controller is designed to evaluate whether the …
techniques are typically applied after the controller is designed to evaluate whether the …
Cross-layer adaptation with safety-assured proactive task job skipping
During the operation of many real-time safety-critical systems, there are often strong needs
for adapting to a dynamic environment or evolving mission objectives, eg, increasing …
for adapting to a dynamic environment or evolving mission objectives, eg, increasing …
Cloud and Edge Computing for Connected and Automated Vehicles
The recent development of cloud computing and edge computing shows great promise for
the Connected and Automated Vehicle (CAV), by enabling CAVs to offload their massive on …
the Connected and Automated Vehicle (CAV), by enabling CAVs to offload their massive on …
Online Adaptive Neural Observer for Prescribed Performance Hyper-Chaotic Systems
HPH Anh, NT Dat - Knowledge-Based Systems, 2024 - Elsevier
This paper proposes a new neural-based algorithm applied to online identifying, observing,
and adaptively controlling of uncertain nonlinear systems via which the system transient and …
and adaptively controlling of uncertain nonlinear systems via which the system transient and …
Verification in the loop: Correct-by-construction control learning with reach-avoid guarantees
In the current control design of safety-critical autonomous systems, formal verification
techniques are typically applied after the controller is designed to evaluate whether the …
techniques are typically applied after the controller is designed to evaluate whether the …
Verification and Design of Robust and Safe Neural Network-enabled Autonomous Systems
Neural networks are being applied to a wide range of tasks in autonomous systems, such as
perception, prediction, planning, control, and general decision making. While they may …
perception, prediction, planning, control, and general decision making. While they may …