Neural-Lyapunov-Control
This repository contains the code for the paper: Neural Lyapunov Control
Requirements
How it works
The framework consists of a learner and a falsifier. The learner minimizes the Lyapunov risk to find parameters in both a control function and a neural Lyapunov function. The falsifier takes the learned control function and the neural Lyapunov function from the learner and checks whether there is a state vector violating the Lyapunov conditions.
A typical procedure is as follows:
- Define the neural network with random parameters for Lyapunov function and initialize controller’s parameters to the solution of linear quadratic regular
- Define a controlled dynamical system
- Set checking conditions for falsifier
- Start training and verifying
- Procedure stops when no counterexample is found
The training part updates the parameters by iteratively minimizing the Lyapunov risk, a cost function measures the degree of violation of the Lyapunov conditions and the verifying part periodically searches counterexample state vectors and adds them back to the training set for the next iteration. This procedure provides flexibility to adjust the cost function for learning additional properties of controllers and Lyapunov functions. In examples we add a tuning term to maximize the region of attractions.