Difference between revisions of "Control of Heat Equation with Actuator Placement"

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For the heat equation, we use a five-point-stencil in space and the implicit euler in time.
 
For the heat equation, we use a five-point-stencil in space and the implicit euler in time.
 
For <math>i=1,\dots, N-1</math>, <math>j=1,\dots, M-1</math>, and <math>k=0,\dots, T_n-1</math>, this yields:
 
For <math>i=1,\dots, N-1</math>, <math>j=1,\dots, M-1</math>, and <math>k=0,\dots, T_n-1</math>, this yields:
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+
<p
 
  <math>  \frac{1}{h_t}(u_{i,j,k}-u_{i,j,k-1}) -  \frac{\kappa_{i,j}}{h_x h_y}(u_{i-1,j,k}+u_{i+1,j,k}+u_{i,j-1,k}+u_{i,j+1,k}-4u_{i,j,k}) =  \sum\limits_{l=1}^L(v_{k+1,l}+v_{k  ,l} )f_l(ih_x,jh_y),  </math>
 
  <math>  \frac{1}{h_t}(u_{i,j,k}-u_{i,j,k-1}) -  \frac{\kappa_{i,j}}{h_x h_y}(u_{i-1,j,k}+u_{i+1,j,k}+u_{i,j-1,k}+u_{i,j+1,k}-4u_{i,j,k}) =  \sum\limits_{l=1}^L(v_{k+1,l}+v_{k  ,l} )f_l(ih_x,jh_y),  </math>
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</p>
 
with <math>u_{i,j,k}=u(ih_x,jh_y,kh_t)</math>,<math>v_{k  ,l}=v_l(kh_t)</math>, <math>\kappa_{i,j}=\kappa(ih_x,jh_y)</math>, the stepsizes in space <math>h_x,h_y</math>, and in time <math>h_t</math>, respectively.
 
with <math>u_{i,j,k}=u(ih_x,jh_y,kh_t)</math>,<math>v_{k  ,l}=v_l(kh_t)</math>, <math>\kappa_{i,j}=\kappa(ih_x,jh_y)</math>, the stepsizes in space <math>h_x,h_y</math>, and in time <math>h_t</math>, respectively.
  

Revision as of 23:03, 23 February 2016

Control of Heat Equation with Actuator Placement
State dimension: 1
Differential states: 1
Continuous control functions: 9
Discrete control functions: 9
Path constraints: 3
Interior point equalities: 2


This problem is governed by the heat equation and is adapted from Iftime and Demetriou ([Iftime2009]Author: Orest V. Iftime; Michael A. Demetriou
Journal: {A}utomatica
Number: 2
Pages: 312--323
Title: {O}ptimal control of switched distributed parameter systems with spatially scheduled actuators
Volume: 45
Year: 2009
Link to Google Scholar). Its goal is to choose a place to apply an actuator in a given area depending on time. The objective function is quadratic, its first term captures the desired final state \bar{u}\equiv 0, the second term regularize the state over time and the third term regularize the continuous controls. The constraints are a source budget, which limits the quantity of placed actuators, and the two-dimensional heat equation with some source function. Additionally, we assume Dirichlet boundary conditions and initial conditions. Originally, the problem formulation was non-convex. We overcome this issue by substitution of v(t)w_l(t) by v_l(t) and adding the Big M formulation.


Mathematical formulation

\begin{array}{llcl} \min\limits_{u,v,w}~~ &||u(t_f)||_{2,\Omega}^2 +2||u||_{2,\Omega\times T}^2+\frac{1}{500}\sum\limits_{l=1}^L||v_l||^2_{2,T} & \\[10pt] \text{ s.t.} ~~~~ &\frac{\partial u}{\partial t}- \kappa \Delta u=\sum\limits_{l=1}^L v_l f_l &\text{ in }&\Omega\times T\\[10pt] & u =0 &\text{ on } &\partial\Omega\times T \\[10pt] & u(0) = 100 \sin(\pi x)\sin(\pi y) &\text{ in }& \Omega\\[10pt] & -M w_l\leq v_l\leq M w_l \text{ for all } l\in \{1,\dots,L\} &\text{ in } & T \\[10pt] & \sum\limits_{l=1}^L w_l \leq W &\text{ in } & T\\[10pt] & w_l(t)\in \{0,1\} \text{ for all } l\in \{1,\dots,L\} &\text{ in } &T. \end{array}

Parameters

We define the source term for all locations l\in \{1,\dots,L\} and a fix parameter \varepsilon\in \R_+: f_l(x,y) = \frac{1}{\sqrt{2\pi\varepsilon}}e^{\frac{-((x_l-x)^2+(y_l-y)^2)}{2\varepsilon}} where (x_l,y_l) is the coordinate of the mesh point of the lth possible actuator location.

The parameters used are:

\begin{array}{rcl} \Omega &=& [0,1] \times [0,2],\\ \kappa &=& 0.01,\\ L &=& 9, \\ W &=& 1,\\ t_f &=&10,\\ \varepsilon &=& 0.01 . \end{array}


The parameter \kappa describes the thermal dissipativity of the material in the domain \Omega , it may vary in space: \kappa(x,y) . The parameter L indicates the number of possible actuator locations. They are distributed as indicated in the picture. The source budget is limited by W and t_f denotes the final time.

Discretization

To solve the problem we apply a "first discretize, then optimize" approach an discretize the components of the problem. For the heat equation, we use a five-point-stencil in space and the implicit euler in time. For i=1,\dots, N-1, j=1,\dots, M-1, and k=0,\dots, T_n-1, this yields: <p 

   \frac{1}{h_t}(u_{i,j,k}-u_{i,j,k-1}) -  \frac{\kappa_{i,j}}{h_x h_y}(u_{i-1,j,k}+u_{i+1,j,k}+u_{i,j-1,k}+u_{i,j+1,k}-4u_{i,j,k}) =   \sum\limits_{l=1}^L(v_{k+1,l}+v_{k  ,l} )f_l(ih_x,jh_y),

</p> with u_{i,j,k}=u(ih_x,jh_y,kh_t),v_{k ,l}=v_l(kh_t), \kappa_{i,j}=\kappa(ih_x,jh_y), the stepsizes in space h_x,h_y, and in time h_t, respectively.

It holds for the source buget with the discretized binary controls w_{l,k} for all k\in \{0,\dots,T_n\}: \sum\limits_{l=1}^L w_{k,l}\leq W.

This condition is called SOS-W conditon.

We remark that the number of discretized binary variables does not depend on the space discretization but it depends on the time discretization. Thats why we taged this problem that it contains mesh-independend and mesh-dependend integer variables.

Reference Solution

Source Code

References

[Iftime2009]Orest V. Iftime; Michael A. Demetriou (2009): {O}ptimal control of switched distributed parameter systems with spatially scheduled actuators . {A}utomatica, 45, 312--323Link to Google Scholar
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