Finite determining parameters feedback control for distributed nonlinear dissipative systems -a computational study

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December 2017, 6(4): 535-557. doi: 10.3934/eect.2017027

Finite determining parameters feedback control for distributed nonlinear dissipative systems -a computational study

Evelyn Lunasin ^1,, and Edriss S. Titi ^2,3,

1.	Department of Mathematics, United States Naval Academy, Annapolis, MD 21402, USA
2.	Departments of Mathematics, Texas A & M University, College Station, TX 77843-3368, USA
3.	Department of Computer Science and Applied Mathematics, The Weizmann Institute of Science, Rehovot 76100, Israel

* Corresponding author: Evelyn Lunasin

Received March 2017 Revised August 2017 Published September 2017

We investigate the effectiveness of a simple finite-dimensional feedback control scheme for globally stabilizing solutions of infinite-dimensional dissipative evolution equations introduced by Azouani and Titi in [7]. This feedback control algorithm overcomes some of the major difficulties in control of multi-scale processes: It does not require the presence of separation of scales nor does it assume the existence of a finite-dimensional globally invariant inertial manifold. In this work we present a theoretical framework for a control algorithm which allows us to give a systematic stability analysis, and present the parameter regime where stabilization or control objective is attained. In addition, the number of observables and controllers that were derived analytically and implemented in our numerical studies is consistent with the finite number of determining modes that are relevant to the underlying physical system. We verify the results computationally in the context of the Chafee-Infante reaction-diffusion equation, the Kuramoto-Sivashinsky equation, and other applied control problems, and observe that the control strategy is robust and independent of the model equation describing the dissipative system.

Keywords: Globally stabilizing feedback control, Chafee-Infante, Kuramoto-Sivashinsky, reaction-diffusion, Navier-Stokes equations, feedback control, data assimilation, determining modes, determining nodes, determining volume elements.

Mathematics Subject Classification: Primary: 35K57, 37L25, 37L30, 37N35, 93B52, 93C20, 93D15.

Citation: Evelyn Lunasin, Edriss S. Titi. Finite determining parameters feedback control for distributed nonlinear dissipative systems -a computational study. Evolution Equations & Control Theory, 2017, 6 (4) : 535-557. doi: 10.3934/eect.2017027

References:

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References:

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[22]	S. Dubljevic, N. El-Farra and P. Christofides, Predictive control of transport-reaction processes, Computers and Chemical Engineering, 29 (2005), 2335-2345. doi: 10.1016/j.compchemeng.2005.05.008. Google Scholar
[23]	S. Dubljevic, N. El-Farra and P. Christofides, Predictive control of parabolic pdes with state and control constraints, Cinter. J. Rob. & Non. Contr., 16 (2006), 749-772. doi: 10.1002/rnc.1097. Google Scholar
[24]	N. H. El-Farra, A. Armaou and P. D. Christofides, Analysis and control of parabolic PDE systems with input constraints, Automatica, 39 (2003), 715-725. doi: 10.1016/S0005-1098(02)00304-7. Google Scholar
[25]	A. Farhat, M. S. Jolly and E. S. Titi, Continuous data assimilation for the 2D Bénard convection through velocity measurements alone, Physica D, 303 (2015), 59-66. doi: 10.1016/j.physd.2015.03.011. Google Scholar
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Figure 1. (a) Closed-loop profile showing stability of the $u(x,t)=~0$ steady state solution. (b) Top-view

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Figure 2. (a) Open-loop profile showing stability of the $u(x,t)=~0$ steady state solution when $\nu = 1.1 > 1$ (b) Profile of $u(x, t=200)$

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Figure 3. (a) Open-loop profile showing instability of the $u(x,t)=0$ steady state solution when $\nu = 4/15 < 1$. (b) Top view profile of $u(x, t)$

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Figure 4. (a) Open-loop profile showing instability of the $u(x,t)=0$ steady state solution for $0<t<40$ for $\nu = 4/15 < 1$, then the feedback control with $\mu=20$ is turned on for $t>40$ which exponentially stabilizes the system. (b) Top view profile of $u(x, t)$

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Figure 5. (a) Closed-loop profile showing fast stabilization of the $u(x,t)=0$ steady state solution for $\nu = 4/20 < 1$, and with $\mu=20$. (b) Top view profile of $u(x, t)$

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Figure 6. (a) With $u_0 = 1e^{-10}\cos x (1 + \sin x)$, the film height starts to destabilize around $t = 32$ and then once feedback control is turned on at $t_c=40$, the solution stabilizes to zero again. (b) A top view of the controlled profile

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Figure 7. (a) Open-loop profile showing instability of the $u(x,t)=0$ steady state solution. (b) Top-view of $u(x,t)$

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Figure 8. (a) Closed-loop profile showing stabilization to $u(x,t)=0$ steady state solution. (b) Top-view

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Figure 9. (a) Closed-loop profile showing eventual stability. (b) Top-view

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Table 1. Model parameters and type of interpolant operator for the controlled and uncontrolled 1D Chafee-Infante equations

Figure	# Actuators	$\mu$	$\nu$	$\alpha$	Interpolant operator
1	10	300	1	100	finite volume elements

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Figure	# Actuators	$\mu$	$\nu$	$\alpha$	Interpolant operator
1	10	300	1	100	finite volume elements

Table 2. Model parameters and type of interpolant operator for the un-controlled and controlled 1D Kuramoto-Sivashinksy equations

Figure	# Actuators	$\mu$	$\nu$	$t_c$	Interpolant Operator
2	0	0	1.1	0
3	0	0	4/15	0
4	4	20	4/15	0	Fourier modes
5	4	20	4/20	0	finite volume
6	4	20	4/20	40	nodal values

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Figure	# Actuators	$\mu$	$\nu$	$t_c$	Interpolant Operator
2	0	0	1.1	0
3	0	0	4/15	0
4	4	20	4/15	0	Fourier modes
5	4	20	4/20	0	finite volume
6	4	20	4/20	40	nodal values

Table 3. Model parameters and type of interpolant operator for the un-controlled and controlled catalytic rod problem

Figure	# Actuators	$\mu$	$\nu$	$\beta_T$	$\beta_U$	$\gamma$	interpolant operator
7	0	0	1	50	2.0	4.0
8	1	30	1	50	2.0	4.0	finite volume
9	1	30	1	varying	2.0	4.0	finite volume
similar to Fig 8	1	30	1	50	2.0	4.0	nodal values

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Figure	# Actuators	$\mu$	$\nu$	$\beta_T$	$\beta_U$	$\gamma$	interpolant operator
7	0	0	1	50	2.0	4.0
8	1	30	1	50	2.0	4.0	finite volume
9	1	30	1	varying	2.0	4.0	finite volume
similar to Fig 8	1	30	1	50	2.0	4.0	nodal values

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