6 papers
Physics-informed Learning for Orbital Uncertainty Propagation with Error Bounds
Chun-Wei Kong, Morteza Lahijanian, Jay McMahon
The Fokker-Planck partial differential equation (FP-PDE) governs uncertainty evolution in stochastic dynamical systems. In orbital dynamics, solving the FP-PDE is challenging becau…
Certified Stochastic Control via Covariance Steering with Pick-to-Learn
Chun-Wei Kong, Zachary Donovan, Morteza Lahijanian +1
We present CS-P2L, a framework coupling covariance steering (CS) with the Pick-to-Learn (P2L) meta-algorithm for certified controller synthesis over high-fidelity stochastic simula…
Training with Hard Constraints: Learning Neural Certificates and Controllers for SDEs
Chun-Wei Kong, Sebastian Escobar, Ibon Gracia +2
Due to their expressive power, neural networks (NNs) are promising templates for functional optimization problems, particularly for reach-avoid certificate generation for systems g…
Bayesian Diagnosability and Active Fault Identification
Chun-Wei Kong, Jay McMahon, Morteza Lahijanian
We study fault identification in discrete-time nonlinear systems subject to additive Gaussian white noise. We introduce a Bayesian framework that explicitly accounts for unmodeled…
A Control Framework for CUBESAT Rendezvous and Proximity Operations using Electric Propulsion
Bo-Chuan Lin, Chun-Wei Kong, Simone Semeraro +1
A control framework is presented to solve the rendezvous and proximity operations (RPO) problem of the EP-Gemini mission. In this mission, a CubeSat chaser is controlled to approac…
Error Bounds for Physics-Informed Neural Networks in Fokker-Planck PDEs
Chun-Wei Kong, Luca Laurenti, Jay McMahon +1
Stochastic differential equations are commonly used to describe the evolution of stochastic processes. The state uncertainty of such processes is best represented by the probabilit…