Cloud-based computational model predictive control using a parallel multi-block ADMM approach
Optimization and Control
2022-04-19 v2 Systems and Control
Systems and Control
Abstract
Heavy computational load for solving nonconvex problems for large-scale systems or systems with real-time demands at each sample step has been recognized as one of the reasons for preventing a wider application of nonlinear model predictive control (NMPC). To improve the real-time feasibility of NMPC with input nonlinearity, we devise an innovative scheme called cloud-based computational model predictive control (MPC) by using an elaborately designed parallel multi-block alternating direction method of multipliers (ADMM) algorithm. This novel parallel multi-block ADMM algorithm is tailored to tackle the computational issue of solving a nonconvex problem with nonlinear constraints.
Cite
@article{arxiv.2202.06012,
title = {Cloud-based computational model predictive control using a parallel multi-block ADMM approach},
author = {Yaling Ma and Runze Gao and Li Dai and Jinxian Wu and Yuanqing Xia},
journal= {arXiv preprint arXiv:2202.06012},
year = {2022}
}
Comments
Statements and experiments are flawed