Physics-Informed Neural Networks for Learning and Control

This page describes the implementation of a learning-based nonlinear model predictive control (NMPC) using physics-informed neural networks (PINNs), which is experimentally validated with SPONGE. PINNs are trained in PyTorch, and their hyperparameters are optimized via ASHA. One PINN is integrated into a ROS package (sponge_mpc) and used as model within NMPC. For this purpose, CasADi is used in a ROS service (C++ node) which communicates with Simulink, enabling its use on the real-time hardware. The test-bench software is based on the originally test-bench software. It is therefore recommended that you familiarize yourself with this.

The code for PINN training, hyperparameter optimization and learning-based NMPC with PINNs can be found in the git repository. Also, 13 open-source real-world datasets of SPONGE with five actuators can be found there.

Additional Requirements

Usage

  1. Set up the test bench following these instructions. The ROS-interface is used, which is explained here
  2. Dev-PC: Initialize parameters and open Simulink model via init.m
  3. Dev-PC: If necessary, modify Simulink model
  4. Dev-PC: Compile the model by pressing Ctrl+b
  5. Dev-PC: Compile ROS-Workspace and copy to RT-PC via $ ./build.sh && ./sync.sh
  6. Connect to RT-PC via SSH and run the following commands on RT-PC: $ sudo /etc/init.d/ethercat start (start EtherCAT master) and $ ~/app_interface/ros_install/scripts/autostart.sh && tmux attach-session -t app (start compiled model)
  7. Dev-PC: Launch ROS service for PINN-based MPC via roslaunch sponge_mpc sponge_mpc.launch
  8. Dev-PC: Start external mode in Simulink model via Connect To Target to visualize/record data or alter settings (such as starting the MPC experiment)
  9. After the experiment on RT-PC: Ctrl+c in tmux window, $ tmux kill-session -t app and $ sudo /etc/init.d/ethercat stop to stop the EtherCAT master

Citing

The paper is freely available via arXiv. If you use parts of this project for your research, please cite the following publication:

Generalizable and Fast Surrogates: Model Predictive Control of Articulated Soft Robots using Physics-Informed Neural Networks
T.-L. Habich, A. Mohammad, S. F. G. Ehlers, M. Bensch, T. Seel, and M. Schappler
Currently under review