1 citations · 2 across the 6 of their papers we have counts for
6 papers
An Adjoint-based Neural Regulator for Real-Time Optimal Control with State Constraints
Isaiah A. Agboola, Yuxin Tong, Uduak Inyang-Udoh
This paper introduces a learning-based control framework for real-time constrained optimal control of nonlinear systems with safety guarantees based on the Pontryagin's Minimum Pri…
Neural Co-state Projection Regulator: A Model-free Paradigm for Real-time Optimal Control with Input Constraints
Lihan Lian, Uduak Inyang-Udoh
Learning-based approaches, notably Reinforcement Learning (RL), have shown promise for solving optimal control tasks without explicit system models. However, these approaches are o…
Neural Co-state Regulator: A Data-Driven Paradigm for Real-time Optimal Control with Input Constraints
Lihan Lian, Yuxin Tong, Uduak Inyang-Udoh
We propose a novel unsupervised learning framework for solving nonlinear optimal control problems (OCPs) with input constraints in real-time. In this framework, a neural network (N…
Co-state Neural Network for Real-time Nonlinear Optimal Control with Input Constraints
Lihan Lian, Uduak Inyang-Udoh
In this paper, we propose a method to solve nonlinear optimal control problems (OCPs) with constrained control input in real-time using neural networks (NNs). We introduce what we…
Nonlinear Model Predictive Control of a Hybrid Thermal Management System
Demetrius Gulewicz, Uduak Inyang-Udoh, Trevor Bird +1
Model predictive control has gained popularity for its ability to satisfy constraints and guarantee robustness for certain classes of systems. However, for systems whose dynamics a…
A (Strongly) Connected Weighted Graph is Uniformly Detectable based on any Output Node
Uduak Inyang-Udoh, Michael Shanks, Neera Jain
Many dynamical systems, including thermal, fluid, and multi-agent systems, can be represented as weighted graphs. In this paper we consider whether the unstable states of such syst…