Faster, cheaper, and more power efficient optimization solvers than those currently offered by general-purpose solutions are required for extending the use of model predictive control (MPC) to resource-constrained embedded platforms. We propose several custom computational architectures for different first-order optimization methods that can handle linear-quadratic MPC problems with input, input-rate, and soft state constraints. We provide analysis ensuring the reliable operation of the resulting controller under reduced precision fixed-point arithmetic. Implementation of the proposed architectures in FPGAs shows that satisfactory control performance at a sample rate beyond 1 MHz is achievable even on low-end devices, opening up new possibilities for the application of MPC on embedded systems.
@article{arxiv.1303.1090,
title = {Embedded Online Optimization for Model Predictive Control at Megahertz Rates},
author = {Juan L. Jerez and Paul J. Goulart and Stefan Richter and George A. Constantinides and Eric C. Kerrigan and Manfred Morari},
journal= {arXiv preprint arXiv:1303.1090},
year = {2017}
}