{"title":"Hierarchical predictive control strategy of microalgae culture in a photobioreactor","authors":"S. E. Benattia, S. Tebbani, D. Dumur","doi":"10.1109/ICSTCC.2015.7321298","DOIUrl":null,"url":null,"abstract":"In this paper, the problem of trajectory tracking of a nonlinear system with unknown but bounded model parameters uncertainties is addressed. The proposed control strategy combines a robust model predictive control law with a proportional-integral (PI) regulator. The predictive controller guarantees the tracking of the reference trajectory, whereas the PI regulator ensures a good tracking accuracy. The proposed robust predictive controller considers only the most influential model parameters (chosen from a sensitivity analysis), and involves the minimization of a regularized optimization problem. This new formulation of the predictive controller ensures a good trade-off between tracking accuracy and computation time. The developed hierarchical strategy is applied to a macroscopic continuous photobioreactor system, for regulating the biomass concentration at a chosen setpoint. Finally, the proposed strategy is validated in simulation to assess its efficiency.","PeriodicalId":257135,"journal":{"name":"2015 19th International Conference on System Theory, Control and Computing (ICSTCC)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 19th International Conference on System Theory, Control and Computing (ICSTCC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSTCC.2015.7321298","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
In this paper, the problem of trajectory tracking of a nonlinear system with unknown but bounded model parameters uncertainties is addressed. The proposed control strategy combines a robust model predictive control law with a proportional-integral (PI) regulator. The predictive controller guarantees the tracking of the reference trajectory, whereas the PI regulator ensures a good tracking accuracy. The proposed robust predictive controller considers only the most influential model parameters (chosen from a sensitivity analysis), and involves the minimization of a regularized optimization problem. This new formulation of the predictive controller ensures a good trade-off between tracking accuracy and computation time. The developed hierarchical strategy is applied to a macroscopic continuous photobioreactor system, for regulating the biomass concentration at a chosen setpoint. Finally, the proposed strategy is validated in simulation to assess its efficiency.