Data-Driven Model-Free Adaptive Control of Particle Quality in Drug Development Phase of Spray Fluidized-Bed Granulation Process

被引：2
作者：
Wang, Zhengsong [1 ]
He, Dakuo [1 ,2 ]
Zhu, Xu [1 ]
Luo, Jiahuan [1 ]
Liang, Yu [1 ]
Wang, Xu [3 ]
机构：
[1] Northeastern Univ, Coll Informat Sci & Engn, Shenyang 110004, Liaoning, Peoples R China
[2] Northeastern Univ, State Key Lab Synthet Automat Proc Ind, Shenyang 110004, Liaoning, Peoples R China
[3] State Key Lab Proc Automat Min & Met, Beijing 100160, Peoples R China
来源：
COMPLEXITY | 2017年
基金：
中国国家自然科学基金;
关键词：
ITERATIVE LEARNING CONTROL; POPULATION BALANCE; PREDICTIVE CONTROL; NONLINEAR-SYSTEMS; WET GRANULATION; CONTROL DESIGN; AGGREGATION; SIZE; AGGLOMERATION; GROWTH;
D O I：
10.1155/2017/4960106
中图分类号：
O1 [数学];
学科分类号：
0701 ; 070101 ;
摘要：
A novel data-driven model-free adaptive control (DDMFAC) approach is first proposed by combining the advantages of model-free adaptive control (MFAC) and data-driven optimal iterative learning control (DDOILC), and then its stability and convergence analysis is given to prove algorithm stability and asymptotical convergence of tracking error. Besides, the parameters of presented approach are adaptively adjusted with fuzzy logic to determine the occupied proportions of MFAC and DDOILC according to their different control performances in different control stages. Lastly, the proposed fuzzy DDMFAC (FDDMFAC) approach is applied to the control of particle quality in drug development phase of spray fluidized-bed granulation process (SFBGP), and its control effect is compared with MFAC and DDOILC and their fuzzy forms, in which the parameters of MFAC and DDOILC are adaptively adjusted with fuzzy logic. The effectiveness of the presented FDDMFAC approach is verified by a series of simulations.
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页数：17
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