5 papers
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…
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…
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…