Author

George J. Pappas

3 papers indexed here

We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.

Not the right person? Other researchers publish under this name.

Preprint Sep 2026

Physics-Informed Learning of Feedback-Linearizing Representations

This work proposes a cascaded physics-informed neural network (PINN) framework to approximately solve partial differential equations (PDEs) and demonstrates that this approach can computationally discover effective feedback-linearizing representations of nonlinear systems for control tasks.

Pavlos Kallinikidis, Feng-Jun Yang, David Snyder et al. · 0 citations

Preprint Sep 2026

Policy Gradient over History-Dependent Policy Classes for LQR with Domain Randomization

Domain Randomization (DR) has been widely used to overcome the sim-to-real gap by training a controller on a distribution of simulated environments via reinforcement learning. While DR can achieve robust performance simply using controllers synthesized via policy gradient (PG) methods, the optimization landscape is not...

Tesshu Fujinami, Bruce D. Lee, Anastasios Tsiamis et al. · 0 citations

Preprint Sep 2026

Verifying performance, stability, and feasibility of inexact non-linear model predictive controllers

A verification framework to numerically analyze inexact model predictive controllers (MPCs) in the constrained non-linear discrete-time setting to formulate an optimization problem that searches over the worst-case initial state within a given set and control inputs consistent with the inexact controller to maximize a...

Rajiv Sambharya, S. C. Anand, George J. Pappas · 0 citations