TY - JOUR
T1 - Constructive method for boundary control of stochastic 1D parabolic PDEs
AU - Wang, Pengfei
AU - Katz, Rami
AU - Fridman, Emilia
N1 - Publisher Copyright:
© 2022 Elsevier B.V.. All rights reserved.
PY - 2022
Y1 - 2022
N2 - Recently, qualitative methods for finite-dimensional boundary state-feedback control were introduced for stochastic 1D parabolic PDEs. In this paper, we present constructive and efficient design conditions for state-feedback control of stochastic 1D heat equations driven by a nonlinear multiplicative noise. We consider the Neumann actuation and apply modal decomposition with either trigonometric or polynomial dynamic extension. The controller design employs a finite number of comparatively unstable modes. We provide mean-square L2stability analysis of the full-order closed-loop system, where we employ Itô's formula, leading to linear matrix inequality (LMI) conditions for finding the controller gain and as large as possible noise intensity for the mean-square stabilizability. We prove that the LMIs are always feasible for small enough noise intensity. We further show that in the case of linear multiplicative noise, the system is stabilizable for noise intensities that guarantee the stabilizability of the stochastic finite-dimensional part of the closed-loop system. Numerical simulations illustrate the efficiency of our method.
AB - Recently, qualitative methods for finite-dimensional boundary state-feedback control were introduced for stochastic 1D parabolic PDEs. In this paper, we present constructive and efficient design conditions for state-feedback control of stochastic 1D heat equations driven by a nonlinear multiplicative noise. We consider the Neumann actuation and apply modal decomposition with either trigonometric or polynomial dynamic extension. The controller design employs a finite number of comparatively unstable modes. We provide mean-square L2stability analysis of the full-order closed-loop system, where we employ Itô's formula, leading to linear matrix inequality (LMI) conditions for finding the controller gain and as large as possible noise intensity for the mean-square stabilizability. We prove that the LMIs are always feasible for small enough noise intensity. We further show that in the case of linear multiplicative noise, the system is stabilizable for noise intensities that guarantee the stabilizability of the stochastic finite-dimensional part of the closed-loop system. Numerical simulations illustrate the efficiency of our method.
KW - Distributed parameter systems
KW - Lyapunov method
KW - boundary control
KW - modal decomposition
KW - stochastic parabolic PDEs
UR - http://www.scopus.com/inward/record.url?scp=85144829076&partnerID=8YFLogxK
U2 - 10.1016/j.ifacol.2022.11.037
DO - 10.1016/j.ifacol.2022.11.037
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AN - SCOPUS:85144829076
SN - 2405-8963
VL - 55
SP - 109
EP - 114
JO - IFAC-PapersOnLine
JF - IFAC-PapersOnLine
IS - 30
T2 - 25th IFAC Symposium on Mathematical Theory of Networks and Systems, MTNS 2022
Y2 - 12 September 2022 through 16 September 2022
ER -