Model free predictive control for a nonlinear biological system with intelligent optimization approach

Author

Mehrjerdi, Mojtaba Zaare ; Amoabediny, G. ; Moshiri, Behzad ; Ashktorab, Arman ; Araabi, B.N.

Author_Institution

Control & Intell. Process. Center of Excellence, Univ. of Tehran, Tehran, Iran

fYear

2013

fDate

25-27 May 2013

Firstpage

2288

Lastpage

2293

Abstract

Today the importance of life science and its related processes are undeniable. Modeling and control of these kind of processes are too complicate because of existence of delay in growth and also nonlinear behavior of micro-organisms. Model predictive control is one of the most popular advanced controlling strategies in this industry, however its dependence on accurate model for predicting future input and output values is limitating. If there is a way that could predict the future values of the process properly, it is possible to overcome to the existing challenges. In this paper we design a model free predictive controller by using a trained recurrent neural network as a predictor for prediction stage at MPC and using GA for solving the associated optimization problem that result the optimal control signal sequence.

Keywords

biology; control system synthesis; genetic algorithms; microorganisms; neurocontrollers; nonlinear control systems; optimal control; prediction theory; predictive control; recurrent neural nets; GA; MPC; control design; controlling strategy; genetic algorithm; intelligent optimization approach; life science; microorganism; model free predictive controller; nonlinear biological system; optimal control signal sequence; optimization problem; prediction stage; predictor; trained recurrent neural network; Artificial neural networks; Biological cells; Predictive control; Predictive models; Trajectory; Aerated Flask; Biological System; GA; NNMPC;

fLanguage

English

Publisher

ieee

Conference_Titel

Control and Decision Conference (CCDC), 2013 25th Chinese

Conference_Location

Guiyang

Print_ISBN

978-1-4673-5533-9

Type

conf

DOI

10.1109/CCDC.2013.6561318

Filename

6561318