Numerical experiments and the open-source solver from the paper "MPCGPU: Real-Time Nonlinear Model Predictive Control through Preconditioned Conjugate Gradient on the GPU"

git clone https://github.com/A2R-Lab/MPCGPU
cd MPCGPU
git submodule update --init --recursive
make build_qdldl
make examples
mkdir -p tmp/results

Either install the qdldl shared library by running cd qdldl/build && make install or modify the LD_LIBRARY_PATH environment variable to include the path to MPCGPU/qdldl/build/out.

./examples/pcg.exe
./examples/qdldl.exe

You can set a bunch of parameters in include/setting.cuh file. You can also modify these by passing them as compiler flags. This will overwrite the default values set for these parameters. Please refer to Makefile for an example.

You should be able to replace the underlying linear system solver with your own solver. Please refer to include/linsys_solvers/qdldl/sqp.cuh for an example.

You should also be able to compile and run it for a different problem that "Kuka IIWA manipulator". Please refer to include/dynamics/ folder for an example. We use GRiD for computing rigid body dynamics with analytical gradients.

To cite this work in your research, please use the following bibtex:

@inproceedings{adabag2024mpcgpu,
  title={MPCGPU: Real-Time Nonlinear Model Predictive Control through Preconditioned Conjugate Gradient on the GPU}, 
  author={Emre Adabag and Miloni Atal and William Gerard and Brian Plancher},
  booktitle={IEEE International Conference on Robotics and Automation (ICRA)},
  address = {Yokohama, Japan},
  month={May.},
  year = {2024}
}