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A new adaptive controller for bio-reactors with unknown kinetics and biomass concentration: Guarantees for the boundedness and convergence properties

Author

Listed:
  • Rincón, A.
  • Piarpuzán, D.
  • Angulo, F.

Abstract

In this work, an adaptive controller for bioreactors, using the measurement of output gas flow rate to handle the uncertainty on biomass concentration and kinetic rate is developed. A mass–balance model based on a unique reaction pathway to represent the input–output behavior is considered. Time-varying and unknown but bounded behavior of plant parameters, including the substrate concentration at the inflow and the yield coefficients is taken into account in the controller. The Lyapunov-like function method to develop the controller is used. A new method to handle the unknown time-varying behavior of the control gain is developed; with this method it is assured that the output tracks the desired reference with a user-defined tolerance and parameter drifting is avoided. The main contributions of the scheme with respect to closely related works are: (i) the exact values of the plant parameters are not required to be known; (ii) upper or lower bound related to the plant parameters is not required to be known; (iii) the time-varying behavior of plant parameters in the control design and in the convergence analysis is considered.

Suggested Citation

  • Rincón, A. & Piarpuzán, D. & Angulo, F., 2015. "A new adaptive controller for bio-reactors with unknown kinetics and biomass concentration: Guarantees for the boundedness and convergence properties," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 112(C), pages 1-13.
    • Handle: RePEc:eee:matcom:v:112:y:2015:i:c:p:1-13
    DOI: 10.1016/j.matcom.2015.01.005
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    Cited by:

    1. Santiago Rómoli & Mario Serrano & Francisco Rossomando & Jorge Vega & Oscar Ortiz & Gustavo Scaglia, 2017. "Neural Network-Based State Estimation for a Closed-Loop Control Strategy Applied to a Fed-Batch Bioreactor," Complexity, Hindawi, vol. 2017, pages 1-16, September.

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