Alternating direction method of multipliers (ADMM), as a powerful distributed optimization algorithm, provides a framework for distributed model predictive control (MPC) for nonlinear process systems based on local subsystem model information. However, the practical application of classical ADMM is largely limited by the high computational cost caused by its slow (linear) rate of convergence and non-parallelizability. In this work, we combine a recently developed multi-block parallel ADMM algorithm with a Nesterov acceleration technique into a fast ADMM scheme, and apply it to the solution of optimal control problems associated with distributed nonlinear MPC. A benchmark chemical process is considered for a case study, which demonstrates a significant reduction of computational time and communication effort compared to non-parallel and non-accelerated ADMM counterparts.
|Original language||English (US)|
|Title of host publication||2019 American Control Conference, ACC 2019|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||6|
|State||Published - Jul 2019|
|Event||2019 American Control Conference, ACC 2019 - Philadelphia, United States|
Duration: Jul 10 2019 → Jul 12 2019
|Name||Proceedings of the American Control Conference|
|Conference||2019 American Control Conference, ACC 2019|
|Period||7/10/19 → 7/12/19|
Bibliographical noteFunding Information:
*Financial support from the National Science Foundation (NSF-CBET) is gratefully acknowledged.
© 2019 American Automatic Control Council.