Page 325 - Distributed model predictive control for plant-wide systems

Index

Application, 237–92 Cooperative distributed MPC (C-DMPC),
12, 103–24, 169, 228–9
Black-box model, 27 Cooperative distributed predictive control
with constraints, 189–208
C-DMPC with constraints, 189–208 Cooperative DMPC see Cooperative
analysis, 194–201 distributed MPC
feasibility, 194–9
stability, 199–201 Decomposition, related gain array, 58–62
conclusion, 208 definition, RGA, 59
formulation, 191–3 interpretation, RGA, 60
constraint C-DMPC algorithm, 193–4 Niederlinksi index, 61
dual mode C-DMPC algorithm, 194 pairing rules, RGA, 61
introduction, 189 Distillation column, 62
predictive feasible state sequence, 191 Distributed MPC strategy based on Nash
simulation, 201–8 optimality, 82–101
stabilizing Cooperative DPC with input algorithm, 86
constraints, 191–4 communication failure
system description, 190–1 simulation, 89–93
Centralized MPC, 40, 231 computational convergence, linear
China Railway High-(CRH) speed, 263 systems, 86–8
electric multiple units (EMUs), 263, 264 formulation, 83–5
Constraint distributed predictive control, Nash optimal solution, 85
167–236 nominal stability, 88–9
Control structure, distributed MPC, 39–46 performance analysis, single-step
Control systems structure, plant-wide horizon control under
system, 3–8 communication failure, 89–93
centralized control, 4–5 simulation, 94–9
decentralized control, 5–6 Distributed power network, 2
distributed control, 6–8 Distributed predictive control (DMPC),
hierarchical coordinated decentralized 9–13
control, 7 advantage, 10–11

Distributed Model Predictive Control for Plant-Wide Systems, First Edition. Shaoyuan Li and Yi Zheng.
© 2015 John Wiley & Sons (Asia) Pte Ltd. Published 2015 by John Wiley & Sons (Asia) Pte Ltd.