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How to train your neural control barrier function: Learning safety filters for complex input-constrained systems
O So, Z Serlin, M Mann, J Gonzales… - … on Robotics and …, 2024 - ieeexplore.ieee.org
Control barrier functions (CBFs) have become popular as a safety filter to guarantee the
safety of nonlinear dynamical systems for arbitrary inputs. However, it is difficult to construct …
safety of nonlinear dynamical systems for arbitrary inputs. However, it is difficult to construct …
Physics-informed machine learning for modeling and control of dynamical systems
Physics-informed machine learning (PIML) is a set of methods and tools that systematically
integrate machine learning (ML) algorithms with physical constraints and abstract …
integrate machine learning (ML) algorithms with physical constraints and abstract …
Differentiable safe controller design through control barrier functions
S Yang, S Chen, VM Preciado… - IEEE Control Systems …, 2022 - ieeexplore.ieee.org
Learning-based controllers, such as neural network (NN) controllers, can show high
empirical performance but lack formal safety guarantees. To address this issue, control …
empirical performance but lack formal safety guarantees. To address this issue, control …
Data-driven control: Theory and applications
D Soudbakhsh, AM Annaswamy… - 2023 American …, 2023 - ieeexplore.ieee.org
The ushering in of the big-data era, ably supported by exponential advances in computation,
has provided new impetus to data-driven control in several engineering sectors. The rapid …
has provided new impetus to data-driven control in several engineering sectors. The rapid …
Learning soft constrained MPC value functions: Efficient MPC design and implementation providing stability and safety guarantees
N Chatzikiriakos, KP Wabersich… - … Annual Learning for …, 2024 - proceedings.mlr.press
Abstract Model Predictive Control (MPC) can be applied to safety-critical control problems,
providing closed-loop safety and performance guarantees. Application of MPC requires …
providing closed-loop safety and performance guarantees. Application of MPC requires …
Deep Learning for Continuous-Time Leader Synchronization in Graphical Games Using Sampling and Deep Neural Networks
We propose a novel deep learning-based approach for the problem of continuous-time
leader synchronization in graphical games on large networks. The problem setup is to …
leader synchronization in graphical games on large networks. The problem setup is to …
Differentiable Predictive Control for Robotics: A Data-Driven Predictive Safety Filter Approach
Model Predictive Control (MPC) is effective at generating safe control strategies in
constrained scenarios, at the cost of computational complexity. This is especially the case in …
constrained scenarios, at the cost of computational complexity. This is especially the case in …
Robust Differentiable Predictive Control with Safety Guarantees: A Predictive Safety Filter Approach
In this paper, we propose a novel predictive safety filter that is robust to bounded
perturbations and is combined with a learning-based control called differentiable predictive …
perturbations and is combined with a learning-based control called differentiable predictive …
Real-Time Implementation of Differentiable Predictive Control on Embedded Microcontroller Hardware: A Case Study
This paper presents the embedded implementation of differentiable predictive control (DPC)
in a real-time control application with fast dynamics. DPC is a model-based policy …
in a real-time control application with fast dynamics. DPC is a model-based policy …
Safe Nonlinear Control Under Control Constraints via Reachability, Optimal Control and Reinforcement Learning
O So - 2024 - dspace.mit.edu
Autonomous robots in the real world have nonlinear dynamics with actuators that are subject
to constraints. The combination of the two poses complicates the task of designing …
to constraints. The combination of the two poses complicates the task of designing …