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Synchronization of Circadian Neurons and Protein Synthesis Control

  • Gerasimos G. Rigatos
Chapter

Abstract

The chapter proposes a new method for synchronization of coupled circadian cells and for nonlinear control of the associated protein synthesis process using differential flatness theory and the derivative-free nonlinear Kalman Filter. By proving that the dynamic model of the FRQ protein synthesis is a differentially flat one its transformation to the linear canonical (Brunovsky) form becomes possible. For the transformed model one can find a state feedback control input that makes the oscillatory characteristics in the concentration of the FRQ protein vary according to desirable setpoints. To estimate nonmeasurable elements of the state vector the Derivative-free nonlinear Kalman Filter is used. The Derivative-free nonlinear Kalman Filter consists of the standard Kalman Filter recursion on the linearized equivalent model of the coupled circadian cells and on computation of state and disturbance estimates using the diffeomorphism (relations about state variables transformation) provided by differential flatness theory. Moreover, to cope with parametric uncertainties in the model of the FRQ protein synthesis and with stochastic disturbances in measurements, the Derivative-free nonlinear Kalman Filter is redesigned in the form of a disturbance observer. The efficiency of the proposed Kalman Filter-based control scheme is tested through simulation experiments.

Keywords

Control Input Disturbance Observer Circadian Oscillator Flat Output Robust Synchronization 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Gerasimos G. Rigatos
    • 1
  1. 1.Unit of Industrial AutomationIndustrial Systems InstituteRion PatrasGreece

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