全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

H2 and H-Feedback Control Design for Nonlinear Gene Networks via Successive Galerkin’s Approximation

DOI: 10.4236/cmb.2022.122006, PP. 95-108

Keywords: Gene Regulatory Network, GMA System, Galerkin’s Approximation, Feedback Design of Biomolecular Systems, Hamilton-Jacobi Equation, Nonlinear Control

Full-Text   Cite this paper   Add to My Lib

Abstract:

This paper presents a design method of H2 and H-feedback control loop for nonlinear smooth gene networks that are in control affine form. Formulaic solution methodology for solving the nonlinear partial differential equations, namely the Hamilton-Jacobi-Bellman and Hamilton-Jacobi-Isaacs equations through successive Galerkin’s approximation is implemented and the results are compared. Throughout the implementation, there were several caveats that need to be further resolved for practical applications in general cases. Such issues and the clarification of causes are mathematically established and reviewed.

References

[1]  Chen, B. and Chang, Y.-T. and Wang, Y.-C. (2008) Robust H-Stabilization Design in Gene Networks under Stochastic Molecular Noises: Fuzzy-Interpolation Approach. Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, 38, 25-42.
[2]  Del Vecchio, D., Dy, A.J. and Qian, Y.L. (2016) Control Theory Meets Synthetic Biology. Journal of the Royal Society Interface, 13, Issue 120.
https://doi.org/10.1098/rsif.2016.0380
[3]  Oudjama, F., Boumédiène, A., Messirdi, M. and Boubekeur, D. (2021) Comparison of Linear and Nonlinear H-Infinity Controllers for an Electric Vehicle Driven by the Permanent Magnet Synchronous Motor. International Journal on Emerging Technologies, 12, 247-257.
[4]  Fiore, G., Perrino, G., di Bernardo, M. and di Bernardo, D. (2016) In Vivo Real-Time Control of Gene Expression: A Comparative Analysis of Feedback Control Strategies in Yeast. ACS Synthetic Biology, 5, 154-162.
https://doi.org/10.1021/acssynbio.5b00135
[5]  Fletcher, C.A.J. (1984) Computational Galerkin Methods. Springer, Berlin.
[6]  Modi, S., Dey, S. and Singh, A. (2021) Noise Suppression in Stochastic Genetic Circuits Using PID Controllers. PLOS Computational Biology, 17, e1009249.
https://doi.org/10.1371/journal.pcbi.1009249
[7]  Volt, E.O. (2000) Computational Analysis of Biochemical Systems: A Practical Guide for Biochemists and Molecular Biologists. Cambridge Univ. Press, Cambridge.
[8]  Bea, R.W. (1998) Successive Galerkin Approximation Algorithms for Nonlinear Optimal and Robust Control. International Journal of Control, 71, 717-743.
https://doi.org/10.1080/002071798221542
[9]  Saridis, G.N. and Lee, C.-S.G. (1979) An Approximation Theory of Optimal Control for Trainable Manipulators. IEEE Transactions on Systems, Man, and Cybernetics, 9, 1522-159.
https://doi.org/10.1109/TSMC.1979.4310171
[10]  van der Schaft, A.J. (1992) L2-gain Analysis of Nonlinear Systems and Nonlinear State-Feedback H Infinity Control. IEEE Transactions on Automatic Control, 37, 770-784.
https://doi.org/110.1109/9.256331
[11]  Shannon, B., Zamora-Chimal, C.G., Postiglione, L., Salzano, D., Grierson, C.S., Marucci, L., Savery, N.J. and di Bernardo, M. (2020) In Vivo Feedback Control of an Antithetic Molecular-Titration Motif in Escherichia coli Using Microfluidics. ACS Synthetic Biology, 9, 2617-2624.
https://doi.org/10.1021/acssynbio.0c00105
[12]  Zhang, W.H. and Chen, B. (2006) State Feedback H Control for a Class of Nonlinear Stochastic Systems. SIAM Journal on Control and Optimization, 44, 1973-1991.

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133