Features (expected)
Efficient
TinyMPC uses a specialized ADMM-based method that exploits structure of the MPC problem via Riccati recursion.
Robust
The algorithm is absolutely division free it requires no assumptions on problem data (the problem only needs to be convex). It just works!
Free
TinyMPC is free and will always be free for everyone. It is under the Apache 2.0 License.
Embeddable
TinyMPC has an easy interface to generate customized embeddable C code with no memory manager required.
Multi-interface
TinyMPC supports many interfaces including C/C++, Matlab, Python, Julia.
Dependency-minimal
TinyMPC only needs Eigen to run.
TinyMPC is small, but fast
We benchmarked TinyMPC against OSQP, the state-of-the-art QP solver, on microcontrollers.
Credits
Anoushka Alavilli
Main developer
Carnegie Mellon University
Khai Nguyen
Main developer
Carnegie Mellon University
Sam Schoedel
Main developer
Carnegie Mellon University
Brian Plancher
Algorithm Development
Barnard College
Zac Manchester
Algorithm Development
Carnegie Mellon University