TY - JOUR
T1 - Distributed Model Predictive Control and Optimization for Linear Systems with Global Constraints and Time-Varying Communication
AU - Jin, Bo
AU - Li, Huiping
AU - Yan, Weisheng
AU - Cao, Ming
N1 - Publisher Copyright:
© 1963-2012 IEEE.
PY - 2021/7
Y1 - 2021/7
N2 - In the article, we study the distributed model predictive control (DMPC) problem for a network of linear discrete-time systems, where the system dynamics are decoupled, the system constraints are coupled, and the communication networks are described by time-varying directed graphs. A novel distributed optimization algorithm called the push-sum dual gradient (PSDG) algorithm is proposed to solve the dual problem of the DMPC optimization problem in a fully distributed way. We prove that the sequences of the primal, and dual variables converge to their optimal values. Furthermore, to solve the implementation issues, stopping criteria are designed to allow early termination of the PSDG Algorithm, and the gossip-based push-sum algorithm is proposed to check the stopping criteria in a distributed manner. It is shown that the optimization problem is iteratively feasible, and the closed-loop system is exponentially stable. Finally, the effectiveness of the proposed DMPC approach is verified via an example.
AB - In the article, we study the distributed model predictive control (DMPC) problem for a network of linear discrete-time systems, where the system dynamics are decoupled, the system constraints are coupled, and the communication networks are described by time-varying directed graphs. A novel distributed optimization algorithm called the push-sum dual gradient (PSDG) algorithm is proposed to solve the dual problem of the DMPC optimization problem in a fully distributed way. We prove that the sequences of the primal, and dual variables converge to their optimal values. Furthermore, to solve the implementation issues, stopping criteria are designed to allow early termination of the PSDG Algorithm, and the gossip-based push-sum algorithm is proposed to check the stopping criteria in a distributed manner. It is shown that the optimization problem is iteratively feasible, and the closed-loop system is exponentially stable. Finally, the effectiveness of the proposed DMPC approach is verified via an example.
KW - Distributed model predictive control (DMPC)
KW - global constraints
KW - gossip-based push-sum algorithm
KW - push-sum dual gradient (PSDG) algorithm
KW - time-varying directed graphs
UR - http://www.scopus.com/inward/record.url?scp=85112580611&partnerID=8YFLogxK
U2 - 10.1109/TAC.2020.3021528
DO - 10.1109/TAC.2020.3021528
M3 - 文章
AN - SCOPUS:85112580611
SN - 0018-9286
VL - 66
SP - 3393
EP - 3400
JO - IEEE Transactions on Automatic Control
JF - IEEE Transactions on Automatic Control
IS - 7
M1 - 9186330
ER -