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Synthetic biology-inspired robust-perfect-adaptation-achieving control systems: model reduction and stability analysis

Mohammadie Zand, A ; Sharif University of Technology | 2020

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  1. Type of Document: Article
  2. DOI: 10.1109/TCNS.2020.3038835
  3. Publisher: Institute of Electrical and Electronics Engineers Inc , 2020
  4. Abstract:
  5. In addition to perfectly steering the output concentration of a process network to an exogenous set-point, a desired synthetically implemented biological controller should be able to robustly maintain this regulated output in the face of the extrinsic disturbances and inherent uncertainties due to an evervarying environment besides the imprecise modeling. Such an ability, which is called robust perfect adaptation (RPA), can be achieved by integral feedback control (IFC). Answering how IFC is (biochemically) constructible in generally unknown synthetic networks has been a research focus in the community. One of these answers, which has been well investigated previously, is to utilize a simple (Hill-type) integral negative feedback controller. Another effective solution, which has made significant progress, is the increasingly being used antithetic integral feedback controller. In this paper, by applying these two RPA-achieving controllers in control of an uncertain process network with arbitrary number of species, the behavior of the resulted closed-loop systems, in which the effect of molecular dilution is also considered, is analyzed. Through this analysis, by assuming that the stability is preserved, it is shown that the latter controller can be approximately reduced to the former (simpler) one by individually increasing one of its parameters (the annihilation rate). Furthermore, to address the stability assumption, exact parametric conditions are derived to guarantee the stability of the control systems. These findings can lead us to gain a deeper insight into and to simplify the robust design, performance analysis, and implementation of such living circuits. Simulation results accompany the paper's analytical elaborations. IEEE
  6. Keywords:
  7. Adaptation ; Biological networks ; Biology ; Biomolecular feedback ; Circuit stability ; Control systems ; Controller reduction ; Kinetic theory ; Substrates ; Uncertainty ; Closed loop control systems ; Closed loop systems ; Control system analysis ; Controllers ; Feedback control ; Process control ; Robust control ; Stability ; Synthetic biology ; Uncertainty analysis ; Annihilation rates ; Effective solution ; Feedback controller ; Output concentrations ; Parametric conditions ; Performance analysis ; Stability analysis ; Synthetic networks ; Control system stability
  8. Source: IEEE Transactions on Control of Network Systems ; 2020
  9. URL: https://ieeexplore.ieee.org/document/9261968