A general safety framework for learning-based control in uncertain robotic systems
JF Fisac, AK Akametalu, MN Zeilinger… - … on Automatic Control, 2018 - ieeexplore.ieee.org
The proven efficacy of learning-based control schemes strongly motivates their application
to robotic systems operating in the physical world. However, guaranteeing correct operation …
to robotic systems operating in the physical world. However, guaranteeing correct operation …
Robust control barrier–value functions for safety-critical control
This paper works towards unifying two popular approaches in the safety control community:
Hamilton-Jacobi (HJ) reachability and Control Barrier Functions (CBFs). HJ Reachability has …
Hamilton-Jacobi (HJ) reachability and Control Barrier Functions (CBFs). HJ Reachability has …
User's guide to viscosity solutions of second order partial differential equations
MG Crandall, H Ishii, PL Lions - Bulletin of the American mathematical …, 1992 - ams.org
The notion of viscosity solutions of scalar fully nonlinear partial differential equations of
second order provides a framework in which startling comparison and uniqueness …
second order provides a framework in which startling comparison and uniqueness …
Reach-avoid problems with time-varying dynamics, targets and constraints
We consider a reach-avoid differential game, in which one of the players aims to steer the
system into a target set without violating a set of state constraints, while the other player tries …
system into a target set without violating a set of state constraints, while the other player tries …
Hamilton–Jacobi formulation for reach–avoid differential games
K Margellos, J Lygeros - IEEE Transactions on automatic …, 2011 - ieeexplore.ieee.org
A new framework for formulating reachability problems with competing inputs, nonlinear
dynamics, and state constraints as optimal control problems is developed. Such reach-avoid …
dynamics, and state constraints as optimal control problems is developed. Such reach-avoid …
On reachability and minimum cost optimal control
J Lygeros - Automatica, 2004 - Elsevier
Questions of reachability for continuous and hybrid systems can be formulated as optimal
control or game theory problems, whose solution can be characterized using variants of the …
control or game theory problems, whose solution can be characterized using variants of the …
Dynamic power allocation for cell-free massive MIMO: Deep reinforcement learning methods
Y Zhao, IG Niemegeers, SMH De Groot - IEEE Access, 2021 - ieeexplore.ieee.org
Power allocation plays a central role in cell-free (CF) massive multiple-input multiple-output
(MIMO) systems. Many effective methods, eg, the weighted minimum mean square error …
(MIMO) systems. Many effective methods, eg, the weighted minimum mean square error …
Application of deep reinforcement learning for extremely rare failure prediction in aircraft maintenance
The use of aircraft operational logs to predict potential failure that may lead to disruption
poses many challenges and has yet to be fully explored. Given that aircraft are high-integrity …
poses many challenges and has yet to be fully explored. Given that aircraft are high-integrity …
Reachability and minimal times for state constrained nonlinear problems without any controllability assumption
We consider a target problem for a nonlinear system under state constraints. We give a new
continuous level-set approach for characterizing the optimal times and the backward …
continuous level-set approach for characterizing the optimal times and the backward …
Twin actor twin delayed deep deterministic policy gradient (TATD3) learning for batch process control
T Joshi, S Makker, H Kodamana, H Kandath - Computers & Chemical …, 2021 - Elsevier
Control of batch processes is a difficult task due to their complex nonlinear dynamics and
unsteady-state operating conditions within batch and batch-to-batch. It is expected that some …
unsteady-state operating conditions within batch and batch-to-batch. It is expected that some …