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Ruikun Zhou

2 accepted papers

2024

Physics-Informed Neural Network Policy Iteration: Algorithms, Convergence, and Verification

ICML 2024poster

Solving nonlinear optimal control problems is a challenging task, particularly for high-dimensional problems. We propose algorithms for model-based policy iterations to solve nonlinear optimal control problems with convergence guarantees. The main component of our approach is an iterative procedure…

Cited by 14SourcePDFScholar
2022

Neural Lyapunov Control of Unknown Nonlinear Systems with Stability Guarantees

NeurIPS 2022accept

Learning for control of dynamical systems with formal guarantees remains a challenging task. This paper proposes a learning framework to simultaneously stabilize an unknown nonlinear system with a neural controller and learn a neural Lyapunov function to certify a region of attraction (ROA) for the…