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

Zhengsong Wang,Dakuo He,Jiahuan Luo,Xu Zhu,Yu Liang,Xu Wang

doi:10.1155/2017/4960106

Zhengsong Wang, Dakuo He + Show 4 more

Open Access

https://doi.org/10.1155/2017/4960106

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Journal: Complexity	Publication Date: Jan 1, 2017
Citations: 8	License type: CC BY 4.0

Affiliation: Northeastern University

Abstract

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.

Highlights

Spray fluidized-bed granulation process (SFBGP) is a process that forms small particles into larger granules using the liquid binding solution sprayed onto fluidized particles by a spray nozzle above the powder bed [1]
Through theoretical analysis and simulation study, we have proved that modelfree adaptive control (MFAC) and data-driven optimal iterative learning control (DDOILC) both have strengths in particle quality control under different initial values (IVs)
fuzzy DDMFAC (FDDMFAC) approach has been proven to have a better control performance than MFAC and DDOILC, we still have a question of whether it is possible that the superiority of FDDMFAC is entirely attributed to fuzzy adjustment of parameters and has nothing to do with the structure of data-driven model-free adaptive control (DDMFAC) itself

Summary

Introduction

Spray fluidized-bed granulation process (SFBGP) is a process that forms small particles into larger granules using the liquid binding solution sprayed onto fluidized particles by a spray nozzle above the powder bed [1]. A study on particle quality control based on DDC approaches in the drug development phase of a SFBGP is conducted to resolve the practical difficulty encountered in redesigning operating condition when prescription and material attributes are all changed. We select two classical and representative DDC approaches, MFAC and DDOILC, to study data-driven model-free adaptive control (DDMFAC) of average particle size (APS) for SFBGP with simulation experiment research. Compared with other DDC methods, MFAC and DDOILC have several attractive advantages that make them more suitable for many practical control applications [16] They do not require any process model and structural information of the controlled plant and merely depend on the real time measurement I/O data of the controlled plant, which indicates that it is feasible to independently design a generic controller for a certain class of practical industrial processes. We named the final DDC approach presented in this paper as fuzzy DDMFAC (FDDMFAC), and a series of simulations validate its effectiveness

Preliminaries

Theoretical Analysis of MFAC and DDOILC and Simulations in SFBGP

The Proposed Hybrid DDC Approach and Simulation Study in SFBGP

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Conclusions