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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.

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Credits

Anoushka Alavilli
Anoushka Alavilli

Main developer

Carnegie Mellon University

Khai Nguyen
Khai Nguyen

Main developer

Carnegie Mellon University

Sam Schoedel
Sam Schoedel

Main developer

Carnegie Mellon University

Brian Plancher
Brian Plancher

Algorithm Development

Barnard College

Zac Manchester
Zac Manchester

Algorithm Development

Carnegie Mellon University