Skip to main content
Log in

Decentralized PI Controller Design for Robust Perfect Adaptation in Noisy Time-Delayed Genetic Regulatory Networks

  • Published:
Neural Processing Letters Aims and scope Submit manuscript

Abstract

In cellular scales, precise regulation of protein expression is challenging due to time delays and stochastic noises impairing the practical implementation of the existing controllers. To cope with such regulation issues in gene regulatory networks (GRNs), this paper designs a novel decentralized proportional–integral controller with an implementable structure that achieves robust perfect adaptation (RPA) in the presence of stochastic noises and time delays. In this regard, we first define a new augmented state space that incorporates the integral dynamics of the controller. Next, we exploit an appropriate Lyapunov–Krasovskii functional that enables us to quantify sufficient conditions for noise-to-state stability of the time-delayed GRNs in the form of linear matrix inequalities (LMIs). Then, we provide the proportional and integral gains of the decentralized controller using a modification of the variables along with a special structure of the derived LMIs. Finally, the proposed decentralized controller’s applicability and effectiveness are investigated using a synthetic network (known as Repressilator), and a GRN derived from a practical-based simulator. Due to its simple configuration, this feedback control strategy does not only provide RPA in cellular homeostasis but it can be universally implemented in any cellular environment.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

References

  1. Khammash M, Di Bernardo M, Di Bernardo D (2019) Cybergenetics: theory and methods for genetic control system. In: 2019 IEEE 58th conference on decision and control (CDC). IEEE, pp 916–926

  2. Chen S, Harrigan P, Heineike B, Stewart-Ornstein J, El-Samad H (2013) Building robust functionality in synthetic circuits using engineered feedback regulation. Curr Opin Biotechnol 24(4):790–796

    Google Scholar 

  3. Del Vecchio D, Dy AJ, Qian Y (2016) Control theory meets synthetic biology. J R Soc Interface 13(120):20160380

    Google Scholar 

  4. Milias-Argeitis A, Summers S, Stewart-Ornstein J, Zuleta I, Pincus D, El-Samad H, Khammash M, Lygeros J (2011) In silico feedback for in vivo regulation of a gene expression circuit. Nat Biotechnol 29(12):1114–1116

    Google Scholar 

  5. Milias-Argeitis A, Rullan M, Aoki SK, Buchmann P, Khammash M (2016) Automated optogenetic feedback control for precise and robust regulation of gene expression and cell growth. Nat Commun 7(1):1–11

    Google Scholar 

  6. Uhlendorf J, Miermont A, Delaveau T, Charvin G, Fages F, Bottani S, Batt G, Hersen P (2012) Long-term model predictive control of gene expression at the population and single-cell levels. Proc Natl Acad Sci 109(35):14271–14276

    Google Scholar 

  7. Menolascina F, Fiore G, Orabona E, De Stefano L, Ferry M, Hasty J, di Bernardo M, di Bernardo D (2014) In-vivo real-time control of protein expression from endogenous and synthetic gene networks. PLoS Comput Biol 10(5):e1003625

    Google Scholar 

  8. Perrino G, Wilson C, Santorelli M, di Bernardo D (2019) Quantitative characterization of \(\alpha \)-synuclein aggregation in living cells through automated microfluidics feedback control. Cell Rep 27(3):916–927

    Google Scholar 

  9. Saravanan S, Syed Ali M, Rajchakit G, Hammachukiattikul B, Priya B, Thakur GK (2021) Finite-time stability analysis of switched genetic regulatory networks with time-varying delays via Wirtinger’s integral inequality. Complexity 2021:1–21

    Google Scholar 

  10. Li L, Yang Y (2015) On sampled-data control for stabilization of genetic regulatory networks with leakage delays. Neurocomputing 149:1225–1231

    Google Scholar 

  11. Lu L, Xing Z, He B (2016) Non-uniform sampled-data control for stochastic passivity and passification of Markov jump genetic regulatory networks with time-varying delays. Neurocomputing 171:434–443

    Google Scholar 

  12. Yu T, Zhao T, Liu J, Zeng Q (2020) Dynamic output feedback control of discrete-time switched GRNS with time-varying delays. J Frankl Inst 357(2):1043–1069

    MathSciNet  MATH  Google Scholar 

  13. Pandiselvi S, Raja R, Cao J, Rajchakit G (2019) Stabilization of switched stochastic genetic regulatory networks with leakage and impulsive effects. Neural Process Lett 49(2):593–610

    Google Scholar 

  14. Pandiselvi S, Raja R, Cao J, Li X, Rajchakit G (2019) Impulsive discrete-time GRNS with probabilistic time delays, distributed and leakage delays: an asymptotic stability issue. IMA J Math Control Inf 36(1):79–100

    MathSciNet  MATH  Google Scholar 

  15. Pandiselvi S, Raja R, Zhu Q, Rajchakit G (2018) A state estimation \({H_{\infty }}\) issue for discrete-time stochastic impulsive genetic regulatory networks in the presence of leakage, multiple delays and Markovian jumping parameters. J Frankl Inst 355(5):2735–2761

    MathSciNet  MATH  Google Scholar 

  16. Pandiselvi S, Ramachandran R, Cao J, Rajchakit G, Seadawy AR, Alsaedi A (2018) An advanced delay-dependent approach of impulsive genetic regulatory networks besides the distributed delays, parameter uncertainties and time-varying delays. Nonlinear Anal Modell Control 23(6):803–829

    MathSciNet  MATH  Google Scholar 

  17. Pandiselvi S, Raja R, Cao J, Rajchakit G, Ahmad B (2018) Approximation of state variables for discrete-time stochastic genetic regulatory networks with leakage, distributed, and probabilistic measurement delays: a robust stability problem. Adv Differ Equ 2018(1):1–27

    MathSciNet  MATH  Google Scholar 

  18. Pan W, Wang Z, Gao H, Li Y, Du M (2010) Robust \({H_{\infty }}\) feedback control for uncertain stochastic delayed genetic regulatory networks with additive and multiplicative noise. Int J Robust Nonlinear Control 20(18):2093–2107

    MathSciNet  MATH  Google Scholar 

  19. He Y, Zeng J, Wu M, Zhang C-K (2012) Robust stabilization and \({H_{\infty }}\) controllers design for stochastic genetic regulatory networks with time-varying delays and structured uncertainties. Math Biosci 236(1):53–63

    MathSciNet  MATH  Google Scholar 

  20. Mathiyalagan K, Sakthivel R (2012) Robust stabilization and \({H_{\infty }}\) control for discrete-time stochastic genetic regulatory networks with time delays. Can J Phys 90(10):939–953

    Google Scholar 

  21. Jiao H, Zhang L, Shen Q, Zhu J, Shi P (2018) Robust gene circuit control design for time-delayed genetic regulatory networks without sum regulatory logic. IEEE/ACM Trans Comput Biol Bioinf 15(6):2086–2093

    Google Scholar 

  22. Shafikhani I, Karimi HS, Mohammadian M, Ramezani A, Momeni HR (2021) A recursive delay estimation algorithm for linear multivariable systems with time-varying delays. arXiv preprint arXiv:2109.02767

  23. Foo M, Kim J, Bates DG (2018) Modelling and control of gene regulatory networks for perturbation mitigation. IEEE/ACM Trans Comput Biol Bioinf 16(2):583–595

    Google Scholar 

  24. Imani M, Braga-Neto UM (2018) Control of gene regulatory networks using Bayesian inverse reinforcement learning. IEEE/ACM Trans Comput Biol Bioinf 16(4):1250–1261

    Google Scholar 

  25. Wan X, Wang Z, Han Q-L, Wu M (2019) A recursive approach to quantized \({H_{\infty }}\) state estimation for genetic regulatory networks under stochastic communication protocols. IEEE Trans Neural Netw Learn Syst 30(9):2840–2852

    MathSciNet  Google Scholar 

  26. Song X, Wang M, Song S, Ahn CK (2019) Sampled-data state estimation of reaction diffusion genetic regulatory networks via space-dividing approaches. IEEE/ACM Trans Comput Biol Bioinform 18(2):718–730

    Google Scholar 

  27. Dunlop MJ, Keasling JD, Mukhopadhyay A (2010) A model for improving microbial biofuel production using a synthetic feedback loop. Syst Synth Biol 4(2):95–104

    Google Scholar 

  28. Stapleton JA, Endo K, Fujita Y, Hayashi K, Takinoue M, Saito H, Inoue T (2012) Feedback control of protein expression in mammalian cells by tunable synthetic translational inhibition. ACS Synth Biol 1(3):83–88

    Google Scholar 

  29. Åström KJ, Hägglund T (1995) PID controllers: theory, design, and tuning. In: Instrument society of America Research Triangle Park, NC, vol 2

  30. Briat C, Gupta A, Khammash M (2016) Antithetic integral feedback ensures robust perfect adaptation in noisy biomolecular networks. Cell Syst 2(1):15–26

    Google Scholar 

  31. Aoki SK, Lillacci G, Gupta A, Baumschlager A, Schweingruber D, Khammash M (2019) A universal biomolecular integral feedback controller for robust perfect adaptation. Nature 570(7762):533–537

    Google Scholar 

  32. Åström KJ, Murray RM (2021) Feedback systems: an introduction for scientists and engineers. Princeton University Press, Princeton

    MATH  Google Scholar 

  33. Briat C, Gupta A, Khammash M (2018) Antithetic proportional-integral feedback for reduced variance and improved control performance of stochastic reaction networks. J R Soc Interface 15(143):20180079

    Google Scholar 

  34. Filo M, Kumar S, Khammash M (2022) A hierarchy of biomolecular proportional–integral-derivative feedback controllers for robust perfect adaptation and dynamic performance. Nat Commun 13(1):1–19

    Google Scholar 

  35. Frei T, Chang C-H, Filo M, Arampatzis A, Khammash M (2022) A genetic mammalian proportional–integral feedback control circuit for robust and precise gene regulation. Proc Natl Acad Sci 119(00):e2122132119

    Google Scholar 

  36. Xiao M, Zheng WX, Jiang G (2018) Bifurcation and oscillatory dynamics of delayed cyclic gene networks including small RNAS. IEEE Trans Cybern 49(3):883–896

    Google Scholar 

  37. Bakule L (2008) Decentralized control: an overview. Annu Rev Control 32(1):87–98

    MathSciNet  Google Scholar 

  38. Siljak DD (2011) Decentralized control of complex systems. Courier Corporation

  39. Šiljak DD, Zečević A (2005) Control of large-scale systems: beyond decentralized feedback. Annu Rev Control 29(2):169–179

    MATH  Google Scholar 

  40. Mukaidani H (2004) An LMI approach to decentralized guaranteed cost control for a class of uncertain nonlinear large-scale delay systems. J Math Anal Appl 300(1):17–29

    MathSciNet  MATH  Google Scholar 

  41. Del Vecchio D, Abdallah H, Qian Y, Collins JJ (2017) A blueprint for a synthetic genetic feedback controller to reprogram cell fate. Cell Syst 4(1):109–120

    Google Scholar 

  42. Mohammadian M (2019) Decentralized controller design for stochastic gene regulatory networks. J Electr Comput Eng Innov (JECEI) 7(2):213–220

    MathSciNet  Google Scholar 

  43. Zhang X, Zhang Z, Wang Y, Liu C (2020) Guaranteed cost control of genetic regulatory networks with multiple time-varying discrete delays and multiple constant distributed delays. IEEE Access 8:80175–80182

    Google Scholar 

  44. Swain PS, Elowitz MB, Siggia ED (2002) Intrinsic and extrinsic contributions to stochasticity in gene expression. Proc Natl Acad Sci 99(20):12795–12800

    Google Scholar 

  45. To T-L, Maheshri N (2010) Noise can induce bimodality in positive transcriptional feedback loops without bistability. Science 327(5969):1142–1145

    Google Scholar 

  46. Perez-Carrasco R, Guerrero P, Briscoe J, Page KM (2016) Intrinsic noise profoundly alters the dynamics and steady state of morphogen-controlled bistable genetic switches. PLoS Comput Biol 12(10):e1005154

    Google Scholar 

  47. Briat C, Khammash M (2020) In-silico proportional–integral moment control of stochastic gene expression. IEEE Trans Autom Control 66(7):3007–3019

    MathSciNet  MATH  Google Scholar 

  48. Deng H, Krstic M, Williams RJ (2001) Stabilization of stochastic nonlinear systems driven by noise of unknown covariance. IEEE Trans Autom Control 46(8):1237–1253

    MathSciNet  MATH  Google Scholar 

  49. Xu G, Bao H, Cao J (2020) Mean-square exponential input-to-state stability of stochastic gene regulatory networks with multiple time delays. Neural Process Lett 51(1):271–286

    Google Scholar 

  50. Li C, Chen L, Aihara K (2006) Stability of genetic networks with sum regulatory logic: Lur’e system and LMI approach. IEEE Trans Circuits Syst I Regul Pap 53(11):2451–2458

    MathSciNet  MATH  Google Scholar 

  51. Xiao S, Wang X, Zhang X, Zhu J-W, Yang X (2021) State estimator design for genetic regulatory networks with leakage and discrete heterogeneous delays: a nonlinear model transformation approach. Neurocomputing 446:86–94

    Google Scholar 

  52. Mohammadian M, Momeni HR, Karimi HS, Shafikhani I, Tahmasebi M (2015) An LPV based robust peak-to-peak state estimation for genetic regulatory networks with time varying delay. Neurocomputing 160:261–273

    Google Scholar 

  53. Mohammadian M, Momeni HR, Zahiri J, Karimi HS (2020) Switched adaptive observer for structure identification in gene regulatory networks. In: 2020 28th Iranian conference on electrical engineering (ICEE). IEEE, pp 1–5

  54. Li J, Dong H, Liu H, Han F (2021) Sampled-data non-fragile state estimation for delayed genetic regulatory networks under stochastically switching sampling periods. Neurocomputing 463:168–176

    Google Scholar 

  55. Yao L, Zhang W, Xie X-J (2020) Stability analysis of random nonlinear systems with time-varying delay and its application. Automatica 117:108994

    MathSciNet  MATH  Google Scholar 

  56. Boyd SP, Vandenberghe L (2004) Convex optimization. Cambridge University Press, Cambridge

    MATH  Google Scholar 

  57. Zhang F, Zhang Q (2006) Eigenvalue inequalities for matrix product. IEEE Trans Autom Control 51(9):1506–1509

    MathSciNet  MATH  Google Scholar 

  58. Sadeghzadeh A (2018) Gain-scheduled continuous-time control using polytope-bounded inexact scheduling parameters. Int J Robust Nonlinear Control 28(17):5557–5574

    MathSciNet  MATH  Google Scholar 

  59. Gardner TS, Cantor CR, Collins JJ (2000) Construction of a genetic toggle switch in Escherichia coli. Nature 403(6767):339–342

    Google Scholar 

  60. Kaern M, Elston TC, Blake WJ, Collins JJ (2005) Stochasticity in gene expression: from theories to phenotypes. Nat Rev Genet 6(6):451–464

    Google Scholar 

  61. Becskei A, Serrano L (2000) Engineering stability in gene networks by autoregulation. Nature 405(6786):590–593

    Google Scholar 

  62. Schaffter T, Marbach D, Floreano D (2011) Genenetweaver: in silico benchmark generation and performance profiling of network inference methods. Bioinformatics 27(16):2263–2270

    Google Scholar 

  63. Marbach D, Schaffter T, Mattiussi C, Floreano D (2009) Generating realistic in silico gene networks for performance assessment of reverse engineering methods. J Comput Biol 16(2):229–239

    Google Scholar 

  64. Oksendal B (2013) Stochastic differential equations: an introduction with applications. Springer, New York

    MATH  Google Scholar 

  65. Fridman E (2014) Introduction to time-delay systems: analysis and control. Springer, New York

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hazhar Sufi Karimi.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendices

Appendix

Proof of Relationship (17)

Based on Ito’s formula [64], the infinitesimal generator of the proposed Lyapunov function equals to:

$$\begin{aligned} \mathcal {L}V(t)={} & {} \sum _{i=1}^{3} \mathcal {L}V_i(t)\nonumber \\ \mathcal {L}V_1(t)={} & {} {{{{\bar{x}}}}^{T}}\left( t \right) P {\bar{r}}_x \left( t \right) + {\bar{r}}^T_x\left( t \right) P {\bar{x}}\left( t \right) +{{{{\bar{y}}}}^{T}}\left( t \right) Q {\bar{r}}_y \left( t \right) \nonumber \\{} & {} {\bar{r}}^T_y\left( t \right) Q {\bar{y}}\left( t \right) +{{{{\bar{x}}}}^{T}}\left( t \right) {{R}_{1}}{\bar{x}}\left( t \right) +{{{{\bar{y}}}}^{T}}\left( t \right) {{R}_{2}}{\bar{y}}\left( t \right) \nonumber \\{} & {} + \frac{1}{2}Tr\left\{ \bar{\Sigma }_{x}^{T}\left( t \right) \left( P+{{P}^{T}} \right) {{{\bar{\Sigma }}}_{x}}\left( t \right) \right\} \nonumber \\{} & {} +\frac{1}{2}Tr\left\{ \bar{\Sigma }_{x}^{T}\left( t \right) \left( P+{{P}^{T}} \right) {{{\bar{\Sigma }}}_{x}}\left( t \right) \right\} \nonumber \\ \mathcal {L}V_2(t)={} & {} -{{e}^{-\alpha \sigma \left( t \right) }}\left( 1-{\dot{\sigma }}\left( t \right) \right) {{{{\bar{x}}}}^{T}}\left( t-\sigma \left( t \right) \right) {{R}_{1}}{\bar{x}}\left( t-\sigma \left( t \right) \right) \nonumber \\{} & {} -{{e}^{-\alpha \tau \left( t \right) }}\left( 1-{\dot{\tau }}\left( t \right) \right) {{{{\bar{y}}}}^{T}}\left( t-\tau \left( t \right) \right) {{R}_{2}}{\bar{y}}\left( t-\tau \left( t \right) \right) \nonumber \\{} & {} -\alpha {{e}^{-\alpha t}}\int _{t-\sigma \left( t \right) }^{t}{{{e}^{\alpha s}}{{{{\bar{x}}}}^{T}}\left( s \right) {{R}_{1}}{\bar{x}}\left( s \right) ds} \nonumber \\{} & {} -\alpha {{e}^{-\alpha t}}\int _{t-\tau \left( t \right) }^{t}{{{e}^{\alpha s}}{{{{\bar{y}}}}^{T}}\left( s \right) {{R}_{2}}{\bar{y}}\left( s \right) ds} \nonumber \\ \mathcal {L}V_3(t)={} & {} +\bar{\sigma }{{{{\bar{r}}}}_{x}}^{T}\left( t \right) {{Z}_{1}}{{{{\bar{r}}}}_{x}}\left( t \right) +\bar{\tau }{{{{\bar{r}}}}_{y}}^{T}\left( t \right) {{Z}_{1}}{{{{\bar{r}}}}_{y}}\left( t \right) \nonumber \\{} & {} -{{e}^{-\alpha t}}\int _{t-\sigma \left( t \right) }^{t}{{{e}^{\alpha \theta }}}{{{{\bar{r}}}}_{x}}^{T}\left( \theta \right) {{Z}_{1}}{{{{\bar{r}}}}_{x}}\left( \theta \right) d\theta \nonumber \\{} & {} -{{e}^{-\alpha t}}\int _{t-\tau \left( t \right) }^{t}{{{e}^{\alpha \theta }}}{{{{\bar{r}}}}_{y}}^{T}\left( \theta \right) {{Z}_{2}}{{{{\bar{r}}}}_{y}}\left( \theta \right) d\theta \nonumber \\{} & {} -\alpha {{e}^{-\alpha t}}\int _{-\bar{\sigma }}^{0}{\int _{t+\theta }^{t}{{{e}^{\alpha s}}{{r}_{{{\bar{x}}}}}^{T}\left( s \right) {{Z}_{1}}{{r}_{{{\bar{x}}}}}\left( s \right) ds}}d\theta \nonumber \\{} & {} -\alpha {{e}^{-\alpha t}}\int _{-\bar{\tau }}^{0}{\int _{t+\theta }^{t}{{{e}^{\alpha s}}{{r}_{{{\bar{y}}}}}^{T}\left( s \right) {{Z}_{2}}{{r}_{{{\bar{y}}}}}\left( s \right) ds}}d\theta \nonumber \\{} & {} -{{e}^{-\alpha t}}\int _{t-\sigma \left( t \right) }^{t}{{{e}^{\alpha \theta }}Tr\left\{ {{{\bar{\Sigma }}}^{T}}_{x}\left( \theta \right) {{Z}_{1}}{{{\bar{\Sigma }}}_{x}}\left( \theta \right) \right\} d\theta } \nonumber \\{} & {} -{{e}^{-\alpha t}}\int _{t-\tau \left( t \right) }^{t}{{{e}^{\alpha \theta }}Tr\left\{ {{{\bar{\Sigma }}}^{T}}_{y}\left( \theta \right) {{Z}_{2}}{{{\bar{\Sigma }}}_{y}}\left( \theta \right) \right\} d\theta } \nonumber \\{} & {} -\alpha {{e}^{-\alpha t}}\int _{-\bar{\sigma }}^{0}{\int _{t+\theta }^{t}{{{e}^{\alpha s}}Tr\left\{ \bar{\Sigma }_{x}^{T}\left( s \right) {{Z}_{1}}{{{\bar{\Sigma }}}_{x}}\left( s \right) \right\} ds}}d\theta \nonumber \\{} & {} -\alpha {{e}^{-\alpha t}}\int _{-\bar{\tau }}^{0}{\int _{t+\theta }^{t}{{{e}^{\alpha s}}Tr\left\{ \bar{\Sigma }_{y}^{T}\left( s \right) {{Z}_{2}}{{{\bar{\Sigma }}}_{y}}\left( s \right) \right\} ds}}d\theta \nonumber \\{} & {} +\bar{\sigma }Tr\left\{ \bar{\Sigma }_{x}^{T}\left( t \right) {{\varepsilon }_{{{Z}_{1}}}}P{{{\bar{\Sigma }}}_{x}}\left( t \right) \right\} \nonumber \\{} & {} +\bar{\tau }Tr\left\{ \bar{\Sigma }_{y}^{T}\left( t \right) {{\varepsilon }_{{{Z}_{2}}}}Q{{{\bar{\Sigma }}}_{x}}\left( t \right) \right\} \end{aligned}$$
(36)

By defining \( {{\xi }^{T}}\left( t \right) =\left[ \begin{matrix} {{{\bar{x}}}^{T}}\left( t \right) &{} \quad {{{\bar{x}}}^{T}}\left( t-\sigma \left( t \right) \right) \\ \end{matrix} \right] \) and \({{\eta }^{T}}\left( t \right) =\left[ \begin{matrix} {{{\bar{y}}}^{T}}\left( t \right) &{} \quad {{{\bar{y}}}^{T}}\left( t-\tau \left( t \right) \right) \\ \end{matrix} \right] \), and using Ito’s isometry [64] and the Jensen inequality [65], the following inequalities can be obtained:

$$\begin{aligned}{} & {} 2{{\xi }^{T}}\left( t \right) {\tilde{N}}\left( {\bar{x}}\left( t \right) -{\bar{x}}\left( t-\sigma \left( t \right) \right) \right) \le \nonumber \\{} & {} {{\xi }^{T}}\left( t \right) {\tilde{N}}{{\left( \bar{\sigma }{{\left( t \right) }^{-1}}{{Z}_{1}} \right) }^{-1}}{\tilde{N}}{}^{T}\xi \left( t \right) + {{\xi }^{T}}\left( t \right) {\tilde{N}}{{Z}_{1}}^{-1}{\tilde{N}}{}^{T}\xi \left( t \right) \nonumber \\{} & {} +{{\left( \int _{t-\sigma \left( t \right) }^{t}{{{{{\bar{r}}}}_{x}}\left( s \right) ds} \right) }^{T}}{{\left( \bar{\sigma }\left( t \right) \right) }^{-1}}{{Z}_{1}}\left( \int _{t-\sigma \left( t \right) }^{t}{{{{{\bar{r}}}}_{x}}\left( s \right) ds} \right) \nonumber \\{} & {} +{{\left( \int _{t-\sigma \left( t \right) }^{t}{{{{\bar{\Sigma }}}_{x}}\left( s \right) d{{\omega }_{x}}} \right) }^{T}}{{Z}_{1}}\left( \int _{t-\sigma \left( t \right) }^{t}{{{{\bar{\Sigma }}}_{x}}\left( s \right) d{{\omega }_{x}}} \right) \end{aligned}$$
(37)
$$\begin{aligned}{} & {} 2{{\xi }^{T}}\left( t \right) {\tilde{N}}\left( {\bar{x}}\left( t \right) -{\bar{x}}\left( t-\sigma \left( t \right) \right) \right) \le \nonumber \\{} & {} \bar{\sigma }\left( t \right) {{\xi }^{T}}\left( t \right) {\tilde{N}}{{Z}_{1}}^{-1}{\tilde{N}}{}^{T}\xi \left( t \right) + {{\xi }^{T}}\left( t \right) {\tilde{N}}{{Z}_{1}}^{-1}{\tilde{N}}{}^{T}\xi \left( t \right) \nonumber \\{} & {} \quad +\int _{t-\sigma \left( t \right) }^{t}{{{{{\bar{r}}}}_{x}}^{T}\left( s \right) {{Z}_{1}}{{{{\bar{r}}}}_{x}}\left( s \right) ds} \nonumber \\{} & {} \quad +\int _{t-\sigma \left( t \right) }^{t}{Tr\left\{ \bar{\Sigma }_{x}^{T}\left( s \right) {{Z}_{1}}{{{\bar{\Sigma }}}_{x}}\left( s \right) \right\} ds} \end{aligned}$$
(38)

and

$$\begin{aligned}{} & {} 2{{\eta }^{T}}\left( t \right) {\tilde{M}}\left( {\bar{y}}\left( t \right) -{\bar{y}}\left( t-\tau \left( t \right) \right) \right) \le \nonumber \\{} & {} {{\eta }^{T}}\left( t \right) {\tilde{M}}{{\left( \bar{\tau }{{\left( t \right) }^{-1}}{{Z}_{2}} \right) }^{-1}}{\tilde{M}}{}^{T}\eta \left( t \right) + {{\eta }^{T}}\left( t \right) {\tilde{M}}{{Z}_{2}}^{-1}{\tilde{M}}{}^{T}\eta \left( t \right) \nonumber \\{} & {} +{{\left( \int _{t-\tau \left( t \right) }^{t}{{{{{\bar{r}}}}_{y}}\left( s \right) ds} \right) }^{T}}{{\left( \bar{\tau }\left( t \right) \right) }^{-1}}{{Z}_{2}}\left( \int _{t-\tau \left( t \right) }^{t}{{{{{\bar{r}}}}_{y}}\left( s \right) ds} \right) \nonumber \\{} & {} +{{\left( \int _{t-\tau \left( t \right) }^{t}{{{{\bar{\Sigma }}}_{y}}\left( s \right) d{{\omega }_{y}}} \right) }^{T}}{{Z}_{2}}\left( \int _{t-\tau \left( t \right) }^{t}{{{{\bar{\Sigma }}}_{y}}\left( s \right) d{{\omega }_{y}}} \right) \end{aligned}$$
(39)
$$\begin{aligned} \begin{aligned}&2{{\eta }^{T}}\left( t \right) {\tilde{M}}\left( {\bar{y}}\left( t \right) -{\bar{y}}\left( t-\tau \left( t \right) \right) \right) \le \\&\bar{\sigma }\left( t \right) {{\eta }^{T}}\left( t \right) {\tilde{M}}{{Z}_{2}}^{-1}{\tilde{M}}{}^{T}\eta \left( t \right) + {{\eta }^{T}}\left( t \right) {\tilde{M}}{{Z}_{2}}^{-1}{\tilde{M}}{}^{T}\eta \left( t \right) \\&+\int _{t-\tau \left( t \right) }^{t}{{{{{\bar{r}}}}_{y}}^{T}\left( s \right) {{Z}_{2}}{{{{\bar{r}}}}_{y}}\left( s \right) ds} \\&+\int _{t-\tau \left( t \right) }^{t}{Tr\left\{ \bar{\Sigma }_{y}^{T}\left( s \right) {{Z}_{2}}{{{\bar{\Sigma }}}_{y}}\left( s \right) \right\} ds} \\ \end{aligned} \end{aligned}$$
(40)

Now, we can write:

$$\begin{aligned} \begin{aligned}&\mathcal {L}V\left( t \right) +\alpha V\left( t \right) \le \\&{{{{\bar{x}}}}^{T}}\left( t \right) P {\bar{r}}_x \left( t \right) + {\bar{r}}^T_x\left( t \right) P {\bar{x}}\left( t \right) +{{{{\bar{y}}}}^{T}}\left( t \right) Q {\bar{r}}_y \left( t \right) \\&+ {\bar{r}}^T_y\left( t \right) Q {\bar{y}}\left( t \right) +{{{{\bar{x}}}}^{T}}\left( t \right) {{R}_{1}}{\bar{x}}\left( t \right) +{{{{\bar{y}}}}^{T}}\left( t \right) {{R}_{2}}{\bar{y}}\left( t \right) \\&-2{{{{\bar{f}}}}^{T}}\left( {\bar{y}}\left( t-\tau \left( t \right) \right) \right) \bar{\Lambda }\\&\times \left( {\bar{f}}\left( {\bar{y}}\left( t-\tau \left( t \right) \right) \right) -{{{{\bar{K}}}}_{f}}{\bar{y}}\left( t-\tau \left( t \right) \right) \right) \\&+{{{{\bar{x}}}}^{T}}\left( t \right) {{R}_{1}}{\bar{x}}\left( t \right) +{{{{\bar{y}}}}^{T}}\left( t \right) {{R}_{2}}{\bar{y}}\left( t \right) \\&-{{e}^{-\alpha \bar{\sigma }}}\left( 1-{{{\bar{\sigma }}}_{d}} \right) {{{{\bar{x}}}}^{T}}\left( t-\sigma \left( t \right) \right) {{R}_{1}}{\bar{x}}\left( t-\sigma \left( t \right) \right) \\&-{{e}^{-\alpha \bar{\tau }}}\left( 1-{{{\bar{\tau }}}_{d}} \right) {{{{\bar{y}}}}^{T}}\left( t-\tau \left( t \right) \right) {{R}_{2}}{\bar{y}}\left( t-\tau \left( t \right) \right) \\&+\bar{\sigma }{{{{\bar{r}}}}_{x}}^{T}\left( t \right) {{Z}_{1}}{{{{\bar{r}}}}_{x}}\left( t \right) +\bar{\tau }{{{{\bar{r}}}}^{T}}_{y}\left( t \right) {{Z}_{2}}{{{{\bar{r}}}}_{y}}\left( t \right) \\ {}&+{{e}^{-\alpha \bar{\sigma }}}\left\{ \bar{\sigma }\left( t \right) {{\xi }^{T}}\left( t \right) {\tilde{N}}{{Z}_{1}}^{-1}{\tilde{N}}{}^{T}\xi \left( t \right) \right\} \\&+{{e}^{-\alpha \bar{\sigma }}}\left\{ +{{\xi }^{T}}\left( t \right) {\tilde{N}}{{Z}_{1}}^{-1}{\tilde{N}}{}^{T}\xi \left( t \right) \right\} \\&+{{e}^{-\alpha \bar{\sigma }}}\left\{ -2{{\xi }^{T}}\left( t \right) {\tilde{N}}\left( {\bar{x}}\left( t \right) -{\bar{x}}\left( t-\sigma \left( t \right) \right) \right) \right\} \\&+{{e}^{-\alpha \bar{\tau }}}\left\{ \bar{\tau }\left( t \right) {{\eta }^{T}}\left( t \right) {\tilde{M}}{{Z}_{2}}^{-1}{\tilde{M}}{}^{T}\eta \left( t \right) \right\} \\&+{{e}^{-\alpha \bar{\tau }}}\left\{ {{\eta }^{T}}\left( t \right) {\tilde{M}}{{Z}_{2}}^{-1}{\tilde{M}}{}^{T}\eta \left( t \right) \right\} \\ {}&-{{e}^{-\alpha \bar{\tau }}}\left\{ 2{{\eta }^{T}}\left( t \right) {\tilde{M}}\left( {\bar{y}}\left( t \right) -{\bar{y}}\left( t-\tau \left( t \right) \right) \right) \right\} \\&+\frac{1}{2}Tr\left\{ \bar{\Sigma }_{x}^{T}\left( t \right) \left( P+{{P}^{T}} \right) {{{\bar{\Sigma }}}_{x}}\left( t \right) \right\} \\&+\frac{1}{2}Tr\left\{ \bar{\Sigma }_{y}^{T}\left( t \right) \left( Q+{{Q}^{T}} \right) {{{\bar{\Sigma }}}_{y}}\left( t \right) \right\} \\&+\bar{\sigma }Tr\left\{ \bar{\Sigma }_{x}^{T}\left( t \right) {{Z}_{1}}{{{\bar{\Sigma }}}_{x}}\left( t \right) \right\} \\&+\bar{\tau }Tr\left\{ \bar{\Sigma }_{y}^{T}\left( t \right) {{Z}_{2}}{{{\bar{\Sigma }}}_{y}}\left( t \right) \right\} \\ \end{aligned} \end{aligned}$$
(41)

By considering \(\begin{matrix} {{{{\bar{y}}}}^{T}}\left( t-\tau \left( t \right) \right) &{} \quad {{{{\bar{f}}}}^{T}}\left( {{{{\bar{y}}}}^{T}}\left( t-\tau \left( t \right) \right) \right) \\ \end{matrix} ]^{T},\) we have:

$$\begin{aligned} \begin{aligned}&\mathcal {L}V\left( t \right) +\alpha V\left( t \right) \le \\&{{\chi }^{T}}\left( t \right) \left[ \begin{matrix} {{{\tilde{\Omega }}}_{11}} &{} \quad 0 &{} \quad {{{\tilde{\Omega }}}_{13}} &{} \quad 0 &{} \quad {{{\tilde{\Omega }}}_{15}} \\ * &{} \quad {{{\tilde{\Omega }}}_{22}} &{} \quad {{{\tilde{\Omega }}}_{23}} &{} \quad {{{\tilde{\Omega }}}_{24}} &{} \quad 0 \\ * &{} \quad * &{} \quad {{{\tilde{\Omega }}}_{33}} &{} \quad 0 &{} \quad 0 \\ * &{} \quad * &{} \quad * &{} \quad {{{\tilde{\Omega }}}_{44}} &{} \quad {{{\tilde{\Omega }}}_{45}} \\ * &{} \quad * &{} \quad * &{} \quad * &{} \quad {{{\tilde{\Omega }}}_{55}} \\ \end{matrix} \right] \chi \left( t \right) + \\&+\bar{\sigma }{{\chi }^{T}}\left( t \right) {{{{\bar{R}}}}_{x}}{{Z}_{1}}{\bar{R}}_{x}^{T}\chi \left( t \right) +\bar{\tau }{{\chi }^{T}}\left( t \right) {{{{\bar{R}}}}_{y}}{{Z}_{2}}{{{{\bar{R}}}}^{T}}_{y}\chi \left( t \right) \\&+{{e}^{-\alpha \bar{\sigma }}}\left\{ \left( \bar{\sigma }+1 \right) {{\chi }^{T}}\left( t \right) {{{{\tilde{N}}}}_{e}}{{Z}_{1}}^{-1}{\tilde{N}}_{e}^{T}\chi \left( t \right) \right\} \\&+{{e}^{-\alpha \bar{\tau }}}\left\{ \left( \bar{\tau }+1 \right) {{\chi }^{T}}\left( t \right) {{{{\tilde{M}}}}_{e}}{{Z}_{2}}^{-1}{\tilde{M}}_{e}^{T}\chi \left( t \right) \right\} \\ \end{aligned} \end{aligned}$$
(42)

where

$$\begin{aligned} \begin{aligned}&{{{\tilde{\Omega }}}_{11}}=-P{\bar{A}}-{{{{\bar{A}}}}^{T}}P+\alpha P+P{{{{\bar{B}}}}_{x}}{{{{\bar{K}}}}_{x}}+{{{{\bar{K}}}}_{x}}{{{{\bar{B}}}}^{T}}_{x}P+{{R}_{1}}\\&-{{e}^{-\alpha \bar{\sigma }}}\left( {{{{\tilde{N}}}}_{1}}+{{{{\tilde{N}}}}^{T}}_{1} \right) ,{{{\tilde{\Omega }}}_{13}}={{e}^{-\alpha \bar{\sigma }}}\left( {{{{\tilde{N}}}}_{1}}-{{{{\tilde{N}}}}^{T}}_{2} \right) ,{{{\tilde{\Omega }}}_{15}}=P{\bar{B}},\\ {}&{{{\tilde{\Omega }}}_{22}}=-Q{\bar{C}}-{{{{\bar{C}}}}^{T}}Q+Q{{{{\bar{B}}}}_{y}}{{{{\bar{K}}}}_{y}}+{\bar{K}}_{y}^{T}{{{{\bar{B}}}}^{T}}{{Q}_{y}}+\alpha Q+{{R}_{2}}\\&-{{e}^{-\alpha \bar{\tau }}}\left( {{{{\tilde{M}}}}_{1}}+{{{{\tilde{M}}}}^{T}}_{1} \right) , {{{\tilde{\Omega }}}_{23}}=Q{\bar{D}},{{{\tilde{\Omega }}}_{24}}={{e}^{-\alpha \bar{\tau }}}\left( {{{{\tilde{M}}}}_{1}}-M_{2}^{T} \right) ,\\&{{{\tilde{\Omega }}}_{33}}=-{{e}^{-\alpha \bar{\sigma }}}\left( \left( 1-{{\sigma }_{d}} \right) {{R}_{1}}+{{{{\tilde{N}}}}_{2}}+{{{{\tilde{N}}}}^{T}}_{2} \right) ,\\&{{{\tilde{\Omega }}}_{44}}=-{{e}^{-\alpha \bar{\tau }}}\left( \left( 1-{{\tau }_{d}} \right) {{R}_{2}}+{{{{\tilde{M}}}}_{2}}+{{{{\tilde{M}}}}^{T}}_{2} \right) , {{{\tilde{\Omega }}}_{45}}={\bar{K}}_{f}^{T}\bar{\Lambda },\\&{{{\tilde{\Omega }}}_{55}}=-2\bar{\Lambda }, {\bar{R}}_{x}^{T}=\left[ \begin{matrix} -{\bar{A}}+{{{{\bar{B}}}}_{x}}{{{{\bar{K}}}}_{x}} &{} \quad 0 &{} \quad 0 &{} \quad 0 &{} \quad {{\bar{B}}} \\ \end{matrix} \right] ,\\&\quad {\bar{R}}_{y}^{T}=\left[ \begin{matrix} 0 &{} \quad -{\bar{C}}+{{{{\bar{B}}}}_{y}}{{{{\bar{K}}}}_{y}} &{} \quad {{\bar{D}}} &{} \quad 0 &{} \quad 0 \\ \end{matrix} \right] ,\\&{{{{\tilde{N}}}}^{T}}_{e}=\left[ \begin{matrix} {\tilde{N}}_{1}^{T} &{} 0 &{} {\tilde{N}}_{2}^{T} &{} \quad 0 &{} \quad 0 \\ \end{matrix} \right] , \\&{{{{\tilde{M}}}}^{T}}_{e}=\left[ \begin{matrix} {\tilde{M}}_{1}^{T} &{} 0 &{} {\tilde{M}}_{2}^{T} &{} \quad 0 &{} \quad 0 \\ \end{matrix} \right] \\ \end{aligned} \end{aligned}$$

By using Schur’s lemma, pre and post multiplying with

$$\begin{aligned} \begin{aligned} diag\left\{ {{P}^{-1}},{{Q}^{-1}},{{P}^{-1}},{{Q}^{-1}},{{\Lambda }^{-1}},Z_{1}^{-1},Z_{1}^{-1},Z_{2}^{-1},Z_{2}^{-1} \right\} \end{aligned} \end{aligned}$$

and defining \(U={{P}^{-1}}\), \(V={{Q}^{-1}}\), \(X={{{\bar{K}}}_{x}}{{P}^{-1}}\), \(Y={{{\bar{K}}}_{y}}{{Q}^{-1}}\), \(\Gamma ={{\Lambda }^{-1}}\), \({{R}_{1}}={{\varepsilon }_{{{R}_{1}}}}P\), \({{R}_{2}}={{\varepsilon }_{{{R}_{2}}}}Q\), \({{N}_{1}}={{P}^{-1}}{{{\tilde{N}}}_{1}}{{P}^{-1}}\), \({{N}_{2}}={{P}^{-1}}{{{\tilde{N}}}_{2}}{{P}^{-1}}\), \({{M}_{1}}={{Q}^{-1}}{{{\tilde{M}}}_{1}}{{Q}^{-1}}\), \({{M}_{2}}={{Q}^{-1}}{{{\tilde{M}}}_{2}}{{Q}^{-1}}\), \({{Z}_{1}}={{\varepsilon }_{{{Z}_{1}}}}P\) and \({{Z}_{2}}={{\varepsilon }_{{{Z}_{2}}}}Q\), the LMIs presented in (13) can be deduced.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mohammadian, M., Sufi Karimi, H. Decentralized PI Controller Design for Robust Perfect Adaptation in Noisy Time-Delayed Genetic Regulatory Networks. Neural Process Lett 55, 6815–6842 (2023). https://doi.org/10.1007/s11063-023-11162-y

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11063-023-11162-y

Keywords

Navigation