Neural Lyapunov Control
By Ya-Chien Chang, Nima Roohi, Sicun Gao. Neural Information Processing Systems, 2020
413 citations recorded by the source corpus.
We propose new methods for learning control policies and neural network Lyapunov functions for nonlinear control problems, with provable guarantee of stability. The framework consists of a learner that attempts to find the control and Lyapunov functions, and a falsifier that finds counterexamples to quickly guide the learner towards solutions. The procedure terminates when no counterexample is found by the falsifier, in which case the controlled nonlinear system is provably stable. The approach significantly simplifies the process of Lyapunov control design, provides end-to-end correctness guarantee, and can obtain much larger regions of attraction than existing methods such as LQR and SOS/SDP. We show experiments on how the new methods obtain high-quality solutions for challenging robot control problems such as path tracking for wheeled vehicles and humanoid robot balancing.
Subfields: Artificial Intelligence, Control and Systems Engineering
View Paper: https://www.semanticscholar.org/paper/4350f9b0fb5c2fbe6791cab4990d42908358bed7
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
In Control and Systems Engineering
Citations in the corpus
- On the Sample Complexity of Stability Constrained Imitation Learning (2021, neutral): “[32] Ya-Chien Chang, Nima Roohi, and Sicun Gao.”
- Safe Control With Learned Certificates: A Survey of Neural Lyapunov, Barrier, and Contraction Methods for Robotics and Control (2022, methodology): “These approaches typically take the form of a learner–verifier architecture, sometimes also referred to as counterexample-guided inductive synthesis (CEGIS [3], [8], [49], [50], [64]).”