Zhihua Xiong, Yixin Xu, et al.
Neural Computing and Applications
Based on a linear time-varying perturbation (LTVP) model, an integrated control strategy is proposed to track product quality trajectories of batch processes. To address the problem of model uncertainties occurring from batch to batch, the LTVP model is updated by using recursive discounted measurements (RDM) algorithm from the process operational data. Then batch-to-batch iterative learning control (ILC) can be feasibly combined with on-line model predictive control (MPC) within a batch. The integrated strategy can complement both methods to obtain good performance of tracking control. The proposed strategy is illustrated on a simulated batch polymerization reactor, and the results demonstrate that the performance of tracking product qualities can be improved under the proposed strategy when model uncertainties exist. © 2011 IFAC.
Zhihua Xiong, Yixin Xu, et al.
Neural Computing and Applications
Wei Wang, Yueting Chai, et al.
SOLI 2011
Jin Dong, Changrui Ren, et al.
SOLI 2008
Qinhua Wang, Changrui Ren, et al.
SOLI 2009