Author

George J. Pappas

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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

This work first study PG of domain randomized LQR over history-dependent policy classes, such as finite impulse response controllers, as they can extend the possibilities of simultaneous stabilization, and proposes a curriculum learning based algorithm which gradually expands the memory of the controller.

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