affine-mpc 0.1.1

Python bindings for the affine_mpc library

affine_mpc

affine_mpc is a library for model predictive control using discrete-time affine models, with C++ and Python interfaces. It is designed to support real-time control and rapid research prototyping with a focused set of common costs and constraints, along with live parameter updates.

Statement of Need

Researchers and engineers often need an MPC tool that sits between low-level QP assembly and large general-purpose control frameworks. affine_mpc aims to lower the barrier to entry for developing MPC controllers by reducing the amount of low-level problem assembly needed for common affine MPC workflows. affine_mpc focuses on discrete-time affine MPC problems with optional costs and constraints, efficient repeated solves, and workflows that support both experimentation in Python and integration in C++.

Highlights

• Condensed and sparse MPC formulations behind a common interface
• Move-blocking, linear interpolation, and B-spline input parameterizations
• Optional input cost, state bounds, input bounds, and slew-rate constraints
• Efficient OSQP-backed updates when the QP sparsity pattern is unchanged
• Binary .npz logging via MPCLogger for plotting and post-run analysis

Documentation

The primary documentation lives at https://byu-rad-lab.github.io/affine_mpc/.

• Getting Started
• Python guide
• C++ guide
• Concepts and formulation
• Example walkthrough
• Development guide

The Markdown source for the documentation lives in docs/.

Core C++ sources live under src/ and include/, Python bindings and package sources live under python/, and example workflows live under examples/.

Important Usage Notes

• Fully configure the model, limits, weights, and references before calling initializeSolver()
• OSQP sparsity is fixed at initialization; later updates must not introduce new nonzero structure
• If a model coefficient or cost weight may become nonzero later, initialize with that structure already present

Community Standards

• Contributing guide: CONTRIBUTING.md
• Code of conduct: CODE_OF_CONDUCT.md
• Citation metadata: CITATION.cff

Citation

If you use affine_mpc in research, please cite the project using the metadata in CITATION.cff.

License

This project is licensed under the BSD 3-Clause License. See LICENSE for details.