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
.npzlogging viaMPCLoggerfor 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 may become nonzero later, initialize with that structure already present (or, with the sparse formulation, pass
ModelSparsity::FulltoinitializeSolver()); with the condensed formulation the same holds for cost weights
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.
Metadata
Release files for affine-mpc 0.6.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
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Built distributions (wheels)
Total release size: 26.3 MB
Release files / affine_mpc-0.6.0.tar.gz
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