Sensitivity-based hierarchical distributed model predictive control of nonlinear processes

•A sensitivity-based hierarchical distributed model predictive control algorithm is proposed.•Background distributed optimization is performed in local MPC controllers based on advanced-step prediction.•System-wide KKT matrix is formulated in the coordinator by combining local KKT matrices.•The nomi...

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Bibliographic Details
Published in:	Journal of process control 2019-12, Vol.84, p.146-167
Main Authors:	Yu, Tianyu, Zhao, Jun, Xu, Zuhua, Chen, Xi, Biegler, Lorenz T.
Format:	Article
Language:	eng
Subjects:	Distributed control Hierarchical control Model predictive control Nonlinear control Sensitivity
Online Access:	Get full text
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Summary:	•A sensitivity-based hierarchical distributed model predictive control algorithm is proposed.•Background distributed optimization is performed in local MPC controllers based on advanced-step prediction.•System-wide KKT matrix is formulated in the coordinator by combining local KKT matrices.•The nominal optimal inputs are updated online by solving system-wide sensitivity equation.•Robust stability of the proposed method is proved through input-to-state stability theory. Hierarchical distributed model predictive control (HDMPC) is a promising control framework for industrial processes. With the development of efficient solvers for large-scale nonlinear programming (NLP), the implementation of hierarchical distributed control with nonlinear models becomes achievable. However, there is a lack of systematic method that handles the online computational delay in HDMPC which may lead to deterioration of performance or stability. To speed up online computation, an NLP sensitivity-based HDMPC algorithm is proposed in this paper. The implementation strategy is divided into background and online stages. All the local MPC controllers solve their sub-optimization problems iteratively in background based on the predicted future state, and a sensitivity update step is performed online to correct the predicted optimal inputs. The system-wide sensitivity equation is formulated in the upper control level by combining the optimality information of local controllers. The optimality and stability analysis for the proposed method is given. Three case studies are presented to demonstrate the controller performance.
ISSN:	0959-1524 1873-2771