Search Results for author:
Found 13 papers, 1 papers with code
MPC of Uncertain Nonlinear Systems with Meta-Learning for Fast Adaptation of Neural Predictive Models
The framework consists of two phases: the (offine) meta-training phase learns a aggregated NSSM using data from source systems, and the (online) meta-inference phase quickly adapts this aggregated model to the target system using only a few data points and few online training iterations, based on local loss function gradients.
Safe multi-agent motion planning under uncertainty for drones using filtered reinforcement learning
For this problem, we present a tractable motion planner that builds upon the strengths of reinforcement learning and constrained-control-based trajectory planning.
Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems
Specifically, the paper covers an overview of the theory, fundamental concepts and methods, tools, and applications on topics of: 1) physics-informed learning for system identification; 2) physics-informed learning for control; 3) analysis and verification of PIML models; and 4) physics-informed digital twins.
Violation-Aware Contextual Bayesian Optimization for Controller Performance Optimization with Unmodeled Constraints
We study the problem of performance optimization of closed-loop control systems with unmodeled dynamics.
Meta-Learning of Neural State-Space Models Using Data From Similar Systems
Deep neural state-space models (SSMs) provide a powerful tool for modeling dynamical systems solely using operational data.
Optimizing Closed-Loop Performance with Data from Similar Systems: A Bayesian Meta-Learning Approach
In this paper, we propose the use of meta-learning to generate an initial surrogate model based on data collected from performance optimization tasks performed on a variety of systems that are different to the target system.
Data-Driven Identification of Dynamic Quality Models in Drinking Water Networks
Traditional control and monitoring of water quality in drinking water distribution networks (WDN) rely on mostly model- or toolbox-driven approaches, where the network topology and parameters are assumed to be known.
VABO: Violation-Aware Bayesian Optimization for Closed-Loop Control Performance Optimization with Unmodeled Constraints
We study the problem of performance optimization of closed-loop control systems with unmodeled dynamics.
Attentive Neural Processes and Batch Bayesian Optimization for Scalable Calibration of Physics-Informed Digital Twins
Physics-informed dynamical system models form critical components of digital twins of the built environment.
Model Order Reduction for Water Quality Dynamics
Such representation is a byproduct of space- and time-discretization of the PDE modeling transport dynamics.
Safe Learning-based Observers for Unknown Nonlinear Systems using Bayesian Optimization
Data generated from dynamical systems with unknown dynamics enable the learning of state observers that are: robust to modeling error, computationally tractable to design, and capable of operating with guaranteed performance.
Safe Approximate Dynamic Programming Via Kernelized Lipschitz Estimation
We develop a method for obtaining safe initial policies for reinforcement learning via approximate dynamic programming (ADP) techniques for uncertain systems evolving with discrete-time dynamics.
Approximate Dynamic Programming For Linear Systems with State and Input Constraints
Enforcing state and input constraints during reinforcement learning (RL) in continuous state spaces is an open but crucial problem which remains a roadblock to using RL in safety-critical applications.
