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Volume 2017 (2017), Article ID 4960106, 17 pages
Research Article

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

1College of Information Science and Engineering, Northeastern University, Shenyang, Liaoning 110004, China
2State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110004, China
3State Key Laboratory of Process Automation in Mining & Metallurgy, Beijing 100160, China

Correspondence should be addressed to Dakuo He; nc.ude.uen.esi@oukadeh

Received 28 September 2017; Accepted 19 November 2017; Published 12 December 2017

Academic Editor: Julio Blanco-Fernández

Copyright © 2017 Zhengsong Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.