Distributed model predictive control based on Nash optimality for large-scale processes
In this paper,a novel distributed model predictive control scheme based on Nash optimality is presented for large-scale processes,in which the on-line optimization of the whole system is decomposed into that of several smallscale subsystems in distributed structures.Under network environment,the connectivity of the communication network is assumed to be sufficient for each subsystem to obtain information from other subsystems,control performance can be efficiently improved and the Nash equilibrium for all subsystems can be guaranteed.The relevant computational convergence and the nominal stability for unconstrained distributed model predictive control scheme are analyzed.The Shell heavy oil fractionator benchmark control problem is illustrated to verify the effectiveness and practicality of the proposed distributed MPC algorithm.
model predictive control (mpc) distributed control system (dcs) Nash optimality large-scale processes shell benchmark control problem
Zhang Y Li S Y
Department of Automation,Shanghai Jiaotong University,800 Dongchuan Road,Shanghai 200240,China
国际会议
上海
英文
942-952
2008-09-26（万方平台首次上网日期，不代表论文的发表时间）