Model Free Predictive Control for a Nonlinear Biological System with Intelligent Optimization Approach
作者单位：Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering Faculty of Engineering University of Tehran Research Center for New Technologies in Life Science Engineering University of Tehran Department of Life Science Engineering Faculty of New Technologies University of Tehran Department of Chemical Engineering Faculty of Engineering University of Tehran
会议名称：《第25届中国控制与决策会议》
会议届次：25th
会议日期：2013年
学科分类：0710[理学-生物学] 0711[理学-系统科学] 07[理学] 08[工学] 09[农学] 071007[理学-遗传学] 0901[农学-作物学] 081101[工学-控制理论与控制工程] 090102[农学-作物遗传育种] 0811[工学-控制科学与工程] 071102[理学-系统分析与集成] 081103[工学-系统工程]
关 键 词：NNMPC GA Biological System Aerated Flask
摘 要：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.