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The initial values and <math>t_f = 10</math> are fixed. We are interested in how to fish and when to measure, with an upper bound <math>M</math> on the measuring time. We can measure the states directly, <math>h^1(x(t)) = x_1(t)</math> and <math>h^2(x(t)) = x_2(t)</math>. We use two different sampling functions, <math>w^1(\cdot)</math> and <math>w^2(\cdot)</math> in the same experimental setting. This can be seen either as a two-dimensional measurement function <math>h(x(t))</math>, or as a special case of a multiple experiment, in which <math>u(\cdot), x(\cdot)</math>, and <math>G(\cdot)</math> are identical.
The initial values and <math>t_f = 10</math> are fixed. We are interested in how to choose the control <math>u</math> and when to measure, with an upper bound <math>M</math> on the measuring time. We can measure the states directly, <math>h^1(x(t)) = x_1(t)</math> and <math>h^2(x(t)) = x_2(t)</math>. We use two different sampling functions, <math>w^1(\cdot)</math> and <math>w^2(\cdot)</math> in the same experimental setting. This can be seen either as a two-dimensional measurement function <math>h(x(t))</math>, or as a special case of a multiple experiment, in which <math>u(\cdot), x(\cdot)</math>, and <math>G(\cdot)</math> are identical.


Now we formulate the OED problem:
Now we formulate the OED problem:

Revision as of 07:33, 27 January 2026

Van der Pol OED
State dimension: 1
Differential states: 11
Discrete control functions: 3


The Van der Pol problem is a variation of the Van der Pol Oscillator problem. It looks for optimal time intervals to measure the two states in order to minimize the uncertainty of a follow-up parameter estimation problem for the two unknown parameters.

The mathematical equations form a small-scale ODE model. It also includes state sensitivities, the Fisher information matrix entries and integrated sampling states.

Mathematical formulation

We are interested in estimating the parameters p1 and p2 of the initial value problem

x1˙(t)=(p1x2(t)2)x1(t)x2(t)+u(t),t[0,tf],x1(0)=0,x2˙(t)=p2+x1(t),t[0,tf],x2(0)=1.

Additionally, we add the constraint x1(t)0.5,t[0,tf].

The initial values and tf=10 are fixed. We are interested in how to choose the control u and when to measure, with an upper bound M on the measuring time. We can measure the states directly, h1(x(t))=x1(t) and h2(x(t))=x2(t). We use two different sampling functions, w1() and w2() in the same experimental setting. This can be seen either as a two-dimensional measurement function h(x(t)), or as a special case of a multiple experiment, in which u(),x(), and G() are identical.

Now we formulate the OED problem:

miny,G,F,z,wtrace(F1(tf))subject toy˙(t)=f(y(t),θ)G˙(t)=fy(y(t),θ)G(t)+fθ(y(t),θ)F˙(t)=i=1nowi(t)(hyi(y(t))G(t))T(hyi(y(t))G(t))z˙(t)=w(t),y(0)=y0G(0)=y(0)θF(0)=0,z(0)=0w(t)𝒲zi(tf)Mi

The evolution of the symmetric matrix F:[0,tf]2×2 is given by the weighted sum of observability Gramians hyi(y(t))G(t), i=1,2 for each observed function of states.

Parameters

We use tf=10 and p1=p2=1. The upper bound on the measurement time intervals is chosen as M1=M2=2.

Reference Solutions

Here is one local solution to the above control problem.

References

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