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

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-17 ◽  
Author(s):  
Zhengsong Wang ◽  
Dakuo He ◽  
Xu Zhu ◽  
Jiahuan Luo ◽  
Yu Liang ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Adaptive Control ◽  
Drug Development ◽  
Fluidized Bed ◽  
Tracking Error ◽  
Data Driven ◽  
Stability And Convergence ◽  
Development Phase ◽  
Granulation Process ◽  
Model Free

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.

scholarly journals AMT Starting Control as a Soft Starter for Belt Conveyors Using a Data-Driven Method

Symmetry ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 1808
Author(s):  
Yunxia Li ◽  
Lei Li ◽  
Chengliang Zhang
Keyword(s):  
Adaptive Control ◽  
Tracking Error ◽  
Angular Acceleration ◽  
Data Driven ◽  
Prototype Model ◽  
Mechanical Transmission ◽  
Model Free ◽  
Soft Starter ◽  
Belt Conveyors ◽  
Starting Control

Automated mechanical transmission (AMT) is used as a soft starter in this paper. To improve the soft starting quality, a novel data-driven method is studied. By analyzing and comparing five common soft-starting acceleration curves, a segmented acceleration curve is put forward to be used as the soft-starting acceleration curve for the AMT. Based on the prototype model free adaptive control (MFAC) method, a modified MFAC method with jerk compensation is given to control the AMT output shaft’s angular acceleration and reduce driveline shock. Compared with the methods of prototype MFAC and traditional proportion integration differentiation (PID), the modified MFAC method with jerk compensation can better control the AMT output shaft’s angular acceleration and has excellent characteristics in terms of small tracking error and smaller shock. The research results provide a novel data-driven method for AMT as a soft starter.

The model-free adaptive cross-coupled control for two-dimensional linear motor

2019 ◽  
Vol 42 (5) ◽  
pp. 1059-1069
Author(s):  
Baolin Zhang ◽  
Rongmin Cao ◽  
Zhongsheng Hou
Keyword(s):  
Adaptive Control ◽  
Real Time ◽  
Error Estimation ◽  
Tracking Error ◽  
Estimation Method ◽  
Linear Motor ◽  
Experimental Comparison ◽  
Contour Error ◽  
Two Dimensional ◽  
Model Free

In order to improve the contour error accuracy of two-dimensional linear motor, an improved cross-coupled control (CCC) scheme combining real-time contour error estimation and model-free adaptive control (MFAC) is proposed. The real-time contour error estimation method is based on CCC theory and coordinate transformation idea. It can accurately determine the contour error point of regular contour and avoid the influence of tracking error on the contour error. At the same time, for the design of two-axis error controller, only the input and output data generated by two-dimensional linear motor in reciprocating motion are used to design a multiple input multiple output-model-free adaptive control (MIMO-MFAC) algorithm, this algorithm avoids the dependence on accurate mathematical model and reduces the control difficulty. The experimental comparison showed that the proposed method improves the system tracking accuracy and contour accuracy, and verifies the proposed method correctness and effectiveness.

scholarly journals Fuzzy Logic-Based Adaptive Control of Specific Growth Rate in Fed-Batch Biotechnological Processes. A Simulation Study

2020 ◽  
Vol 10 (19) ◽  
pp. 6818
Author(s):  
Mantas Butkus ◽  
Jolanta Repšytė ◽  
Vytautas Galvanauskas
Keyword(s):  
Growth Rate ◽  
Fuzzy Logic ◽  
Adaptive Control ◽  
Specific Growth Rate ◽  
Control Algorithm ◽  
Specific Growth ◽  
Expert Knowledge ◽  
Fed Batch ◽  
Biotechnological Process ◽  
Model Free

This article presents the development and application of a distinct adaptive control algorithm that is based on fuzzy logic and was used to control the specific growth rate (SGR) in a fed-batch biotechnological process. The developed control algorithm was compared with two adaptive control systems that were based on a model-free adaptive technique and gain scheduling technique. A typical mathematical model of recombinant Escherichia coli fed-batch cultivation process was selected to evaluate the performance of the fuzzy-based control algorithm. The investigated control techniques performed similarly when considering the whole process duration. The adaptive PI controller with fuzzy-based parameter adaptation demonstrated advantages over the previously mentioned algorithms—especially when compensating the deviations of the SGR. These deviations usually occur when the equipment malfunctions or process disturbances take place. The fuzzy-based control system was stable within the investigated ranges. It was determined that, regarding control quality, the investigated control algorithms are suited to control the SGR in a fed-batch biotechnological process. However, substrate feeding rate manipulation and limitation needs to be used. Taking into account the time needed to design and tune the controller, the developed controller is suitable for practical applications when expert knowledge is available. The proposed algorithm can be further adapted and developed to control the SGR in other cell cultivations while running the process under substrate limitation conditions.

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