LMI-based invariant like nonlinear state observer for anaerobic digestion model

This note deals with the design of an invariant like nonlinear state observer for a two step (acidogenesis-methanogenesis) mass balance nonlinear model. In order to ensure the stability of the estimation error, a new LMI condition is proposed. The feasibility of this LMI is enhanced due to the use of a suitable reformulation of the Youngs inequality. Actually, additional decision variables are included in the LMI to render its feasibility less conservative compared to those established in the literature for the same class of systems. The numerical simulations using the investigated anaerobic digestion model show the effectiveness of the proposed LMI methodology.

[1]  A Genovesi,et al.  Dynamical model development and parameter identification for an anaerobic wastewater treatment process. , 2001, Biotechnology and bioengineering.

[2]  Philippe Martin,et al.  Symmetry-Preserving Observers , 2006, IEEE Transactions on Automatic Control.

[3]  Laurent Dewasme,et al.  State and Input Estimation of an Anaerobic Digestion Reactor using a Continuous-discrete Unknown Input Observer , 2015 .

[4]  A. Astolfi,et al.  Nonlinear controllers design for robust stabilization of continuous biological reactors , 2000, Proceedings of the 2000. IEEE International Conference on Control Applications. Conference Proceedings (Cat. No.00CH37162).

[5]  Jean-Luc Gouzé,et al.  Closed loop observers bundle for uncertain biotechnological models , 2004 .

[6]  Denis Dochain,et al.  State and parameter estimation in chemical and biochemical processes: a tutorial , 2003 .

[7]  Benoît Chachuat,et al.  STATE ESTIMATION FOR WASTEWATER TREATMENT PROCESSES , 2007 .

[8]  Mohamed Darouach,et al.  A Formal Modeling Framework for Anaerobic Digestion Systems , 2015, 2015 17th UKSim-AMSS International Conference on Modelling and Simulation (UKSim).

[9]  J. P. Steyer,et al.  High gain observer for diagnosing an anaerobic fixed bed reactor , 2001, 2001 European Control Conference (ECC).

[10]  D. Dochain,et al.  On-Line Estimation and Adaptive Control of Bioreactors , 2013 .

[11]  Mohamed Darouach,et al.  Invariant observer applied to anaerobic digestion model , 2016, 2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA).

[12]  G. V. Straten,et al.  A recursively identified model for short-term predictions of NH4/NO3 – concentrations in alternating activated sludge processes , 1999 .

[13]  Jingwei Ma,et al.  Mathematical Modeling in Anaerobic Digestion (AD) , 2013 .

[14]  Sirish L. Shah,et al.  Adaptive multirate state and parameter estimation strategies with application to a bioreactor , 1995 .

[15]  P. Rouchon,et al.  On invariant asymptotic observers , 2002, Proceedings of the 41st IEEE Conference on Decision and Control, 2002..

[16]  Brahim Cherki,et al.  An invariant observer for a chemostat model , 2014, Autom..

[17]  K. Fiaty,et al.  Implementation of observer for on-line estimation of concentration in continuous-stirred membrane bioreactor: Application to the fermentation of lactose , 1999 .

[18]  Denis Dochain,et al.  Review and classification of recent observers applied in chemical process systems , 2015, Comput. Chem. Eng..

[19]  P. Weiland Biogas production: current state and perspectives , 2009, Applied Microbiology and Biotechnology.

[20]  Jean-Luc Gouzé,et al.  Estimation of uncertain models of activated sludge processes with interval observers , 2001 .

[21]  Jonathan Hess Modélisation de la qualité du biogaz produit par un fermenteur méthanogène et stratégie de régulation en vue de sa valorisation , 2007 .

[22]  Michel Zasadzinski,et al.  H∞ circle criterion observer design for Lipschitz nonlinear systems with enhanced LMI conditions , 2016, 2016 American Control Conference (ACC).

[23]  Finn Haugen,et al.  State estimation and model-based control of a pilot anaerobic digestion reactor , 2014 .