10 citations · 15 across the 19 of their papers we have counts for
19 papers
Communication-efficient ADMM over Hierarchical Networks
Binh Nguyen, Shuangqing Wei, Truong X. Nghiem
This paper develops a novel distributed optimization algorithm based on the Alternating Direction Method of Multipliers (ADMM) to solve hierarchical optimization problems over tree…
Learning-enabled Acceleration of Scenario-based Model Predictive Control
Trinh Tran, Binh Nguyen, Truong X. Nghiem
Scenario-based model predictive control (SBMPC) is a variant of model predictive control (MPC) that explicitly accounts for uncertainty by optimizing control actions over multiple…
AD-MPCC: Adaptive Differentiable Model Predictive Contouring Control for Autonomous Racing
Nam T. Nguyen, Binh Nguyen, Ahmad Amine +3
This paper presents Adaptive Differentiable Model Predictive Contouring Control (AD-MPCC), a framework for autonomous racing that integrates differentiable MPCC with online paramet…
Active Learning for Optimal Experimental Design in Machine Learning-Based Building Energy System Identification
Nam T. Nguyen, Truong X. Nghiem
Machine learning (ML) techniques have been commonly adopted to identify the dynamics of building energy systems (BESs), owing to their flexibility relative to first-principles, phy…
LEAF: A Learning-Enabled ADMM Framework for Accelerated Convex Optimization
Binh Nguyen, Trinh Tran, Truong X. Nghiem
We propose LEAF, a learning-enabled ADMM framework for accelerated convex optimization. The key idea is to approximate the Moreau envelope of the objective function using an Input…
Structure- and Stability-Preserving Learning of Port-Hamiltonian Systems
Binh Nguyen, Nam T. Nguyen, Truong X. Nghiem
This paper investigates the problem of data-driven modeling of port-Hamiltonian systems while preserving their intrinsic Hamiltonian structure and stability properties. We propose…
HUANet: Hard-Constrained Unrolled ADMM for Constrained Convex Optimization
Trinh Tran, Binh Nguyen, Truong X. Nghiem
This paper presents HUANet, a constrained deep neural network architecture that unrolls the Alternating Direction Method of Multipliers (ADMM) into a trainable neural network for a…
Failure-Aware Iterative Learning of State-Control Invariant Sets
Ahmad Amine, Nick-Marios T. Kokolakis, Ugo Rosolia +2
In this paper, we address the problem of computing maximal state-control invariant sets for deterministic linear systems using failing trajectories. We introduce the concept of sta…
SIT-LMPC: Safe Information-Theoretic Learning Model Predictive Control for Iterative Tasks
Zirui Zang, Ahmad Amine, Nick-Marios T. Kokolakis +3
Robots executing iterative tasks in complex, uncertain environments require control strategies that balance robustness, safety, and high performance. This paper introduces a safe i…
CBDs: Differentiable Causal Block Diagrams
Thomas Beckers, Ján Drgoňa, Truong X. Nghiem
Modern cyber-physical systems (CPS) integrate physics, computation, and learning, demanding modeling frameworks that are simultaneously composable, learnable, and verifiable. Yet e…
Safe Physics-Informed Machine Learning for Dynamics and Control
Jan Drgona, Truong X. Nghiem, Thomas Beckers +6
This tutorial paper focuses on safe physics-informed machine learning in the context of dynamics and control, providing a comprehensive overview of how to integrate physical models…
Spatially temporally distributed informative path planning for multi-robot systems
Binh Nguyen, Linh Nguyen, Truong X. Nghiem +5
This paper investigates the problem of informative path planning for a mobile robotic sensor network in spatially temporally distributed mapping. The robots are able to gather nois…
Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems
Truong X. Nghiem, Ján Drgoňa, Colin Jones +10
Physics-informed machine learning (PIML) is a set of methods and tools that systematically integrate machine learning (ML) algorithms with physical constraints and abstract mathema…
Ensemble Gaussian Processes for Adaptive Autonomous Driving on Multi-friction Surfaces
Tomáš Nagy, Ahmad Amine, Truong X. Nghiem +3
Driving under varying road conditions is challenging, especially for autonomous vehicles that must adapt in real-time to changes in the environment, e.g., rain, snow, etc. It is di…
Physics Constrained Motion Prediction with Uncertainty Quantification
Renukanandan Tumu, Lars Lindemann, Truong Nghiem +1
Predicting the motion of dynamic agents is a critical task for guaranteeing the safety of autonomous systems. A particular challenge is that motion prediction algorithms should obe…
Distributed Experiment Design and Control for Multi-agent Systems with Gaussian Processes
Viet-Anh Le, Truong X. Nghiem
This paper focuses on distributed learning-based control of decentralized multi-agent systems where the agents' dynamics are modeled by Gaussian Processes (GPs). Two fundamental pr…
ADMM-based Adaptive Sampling Strategy for Nonholonomic Mobile Robotic Sensor Networks
Viet-Anh Le, Linh Nguyen, Truong X. Nghiem
This paper discusses the adaptive sampling problem in a nonholonomic mobile robotic sensor network for efficiently monitoring a spatial field. It is proposed to employ Gaussian pro…
A Receding Horizon Approach for Simultaneous Active Learning and Control using Gaussian Processes
Viet-Anh Le, Truong X. Nghiem
This paper proposes a receding horizon active learning and control problem for dynamical systems in which Gaussian Processes (GPs) are utilized to model the system dynamics. The ac…
Linearized Gaussian Processes for Fast Data-driven Model Predictive Control
Truong X. Nghiem
Data-driven Model Predictive Control (MPC), where the system model is learned from data with machine learning, has recently gained increasing interests in the control community. Ga…