作者: Yuzhu Huang , Derong Liu , Qinglai Wei
DOI: 10.1007/978-3-642-31362-2_53
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摘要: In this paper, a neural network (NN)-based adaptive dynamic programming (ADP) algorithm is employed to solve the optimal temperature control problem in water-gas shift (WGS) process. Since WGS process has characteristics of nonlinearity, multi-input, time-delay and strong coupling, it very difficult establish precise model achieve using traditional methods. We develop an NN conversion furnace data gathered from process, then controller based on dual heuristic (DHP) optimize WGS. Simulation results demonstrate effectiveness neuro-controller.