Networked model predictive control based on neighbourhood optimization for serially connected large-scale processes

Abstract In this paper, two novel networked model predictive control schemes based on neighbourhood optimization are presented for on-line optimization and control of a class of serially connected processes (known as the cascade processes in some references), in which the on-line optimization of the whole system is decomposed into that of several small-scale subsystems in distributed structures. Under network environment, the connectivity of the communication network is assumed to be sufficient for each subsystem to exchange information with its neighbour subsystems. An iterative algorithm for networked MPC and a networked MPC algorithm with one-step delay communication are developed according to different network capacities. The optimality of the iteration based networked MPC algorithm is analyzed and the nominal stability is derived for unconstrained distributed control systems. The nominal stability with one-step delay communication is employed for distributed control systems without the inequality constraints. Finally, an illustrative example and the simulation study of the fuel feed flow control for the walking beam reheating furnace are provided to test the effectiveness and practicality of the proposed networked MPC algorithms.

[1]  M. R. Katebi,et al.  Predictive control design for large-scale systems , 1997, Autom..

[2]  F. Borrelli,et al.  A study on decentralized receding horizon control for decoupled systems , 2004, Proceedings of the 2004 American Control Conference.

[3]  D. Jia,et al.  Min-max feedback model predictive control for distributed control with communication , 2002, Proceedings of the 2002 American Control Conference (IEEE Cat. No.CH37301).

[4]  N. Motee,et al.  Optimal partitioning in distributed model predictive control , 2003, Proceedings of the 2003 American Control Conference, 2003..

[5]  R.M. Murray,et al.  Receding horizon control of multi-vehicle formations: a distributed implementation , 2004, 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601).

[6]  William B. Dunbar,et al.  Model predictive control of coordinated multi-vehicle formations , 2002, Proceedings of the 41st IEEE Conference on Decision and Control, 2002..

[7]  Yugeng Xi.  New design method for discrete-time multi-variable predictive controllers , 1989 .

[8]  Eduardo Camponogara,et al.  Distributed model predictive control , 2002 .

[9]  Jan M. Maciejowski,et al.  Predictive control : with constraints , 2002 .

[10]  Salam Sawadogo,et al.  Decentralized predictive controller for delivery canals , 1998, SMC'98 Conference Proceedings. 1998 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.98CH36218).

[11]  Bruce H. Krogh,et al.  Distributed model predictive control , 2001, Proceedings of the 2001 American Control Conference. (Cat. No.01CH37148).

[12]  S. Joe Qin,et al.  A survey of industrial model predictive control technology , 2003 .

[13]  Yan Zhang,et al.  Nash-optimization enhanced distributed model predictive control applied to the Shell benchmark problem , 2005, Inf. Sci..

[14]  Liu Ding,et al.  Multiple-objective optimization decision-making method for large scale systems , 2002, Proceedings of the 4th World Congress on Intelligent Control and Automation (Cat. No.02EX527).

[15]  Tae Woong Yoon,et al.  Modeling and predictive control of a reheating furnace , 2000, Proceedings of the 2000 American Control Conference. ACC (IEEE Cat. No.00CH36334).

[16]  David Q. Mayne,et al.  Constrained model predictive control: Stability and optimality , 2000, Autom..

[17]  Qidi Wu,et al.  Optimal-setting control for complicated industrial processes and its application study , 2004 .

[18]  Zeng-Guang Hou,et al.  Simultaneous local optimization and coordination of dynamical large-scale systems , 1996, Proceedings of the IEEE International Conference on Industrial Technology (ICIT'96).

[19]  George B. Dantzig,et al.  Decomposition Principle for Linear Programs , 1960 .

[20]  G. Bornard,et al.  Optimal control of complex irrigation systems via decomposition-coordination and the use of augmented Lagrangian , 1998, SMC'98 Conference Proceedings. 1998 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.98CH36218).