Optimal Trajectory, bounded end point

The same is true for ##\ddot{z}##.In summary, we are looking for a function ##z## such that ##\ddot{z} = 4z## and z(0) = x(0) - 1 = 0, \: z(1) = x(1) - 1 = 3, or \ddot{z} = 4z, z(0) = 0, z(1) = 3.
  • #1
Number2Pencil
208
1

Homework Statement



Find the optimal trajectory x*(t) that minimizes:

[tex]
J = \int_{0}^{1} \left( \frac{\dot{x}(t)^2}{2} + 3x(t) \dot{x}(t) + 2x^2(t) + 4x(t) \right) dt
[/tex]

with x(0) = 1 and x(1) = 4

Homework Equations



Euler's equation:

[tex]
\frac{\partial g}{\partial x} - \frac{d}{dt} \left[ \frac{\partial g}{\partial \dot{x}} \right] = 0
[/tex]
where g is the portion inside the integral.

The Attempt at a Solution



Implementing Euler's equation on g:

[tex]
-\ddot{x}(t) + 4x(t) + 4 = 0
[/tex]

In which the question becomes, how do I solve this differential equation? In other classes, I would take the laplace transform, solve for X(s), and then do an inverse laplace transform. The problem is that I need to know the initial condition of x'(0) to do this, which I wasn't provided.

I looked at my notes from class, and in a similar example problem, my professor wound up with the following diff-eq:

[tex]
\ddddot{x}(t) = 16x(t)
[/tex]

He said he took the laplace transform and wound up with:

[tex]
s^4 - 16 = 0
[/tex]

But where did the X(s) go, and why didn't he use the initial conditions? His next step was to solve for s:

[tex]
s = \pm 2, \pm j2
[/tex]

Then he did some kind of generic form of the answer:

[tex]
x^*(t) = A_1 e^{2t} + A_2 e^{-2t} + A_3 e^{j2t} + A_4 e^{-j2t}
[/tex]

at this point, he created 4 simultaneous equations using the original problem's provided points to solve for A1,A2,A3, and A4. So when I try to do the same steps on my problem:

[tex]
-\ddot{x}(t) + 4x(t) + 4 = 0
[/tex]

I take the laplace, ignoring initial conditions:

[tex]
-s^2X(s) + 4X(s) + \frac{4}{s} = 0
[/tex]

But right here, can I really just "drop" the X(s) term and solve for s? Is that mathematically legal?
If I just solve for X(s) as I normally would and then do an inverse laplace, the answer does not satisfy the bounded points. ...and trying to do the laplace transform without the initial x'(0) point is confusing as well.

How do I solve this?
 
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  • #2
Number2Pencil said:

Homework Statement



Find the optimal trajectory x*(t) that minimizes:

[tex]
J = \int_{0}^{1} \left( \frac{\dot{x}(t)^2}{2} + 3x(t) \dot{x}(t) + 2x^2(t) + 4x(t) \right) dt
[/tex]

with x(0) = 1 and x(1) = 4

Homework Equations



Euler's equation:

[tex]
\frac{\partial g}{\partial x} - \frac{d}{dt} \left[ \frac{\partial g}{\partial \dot{x}} \right] = 0
[/tex]
where g is the portion inside the integral.

The Attempt at a Solution



Implementing Euler's equation on g:

[tex]
-\ddot{x}(t) + 4x(t) + 4 = 0
[/tex]

In which the question becomes, how do I solve this differential equation? In other classes, I would take the laplace transform, solve for X(s), and then do an inverse laplace transform. The problem is that I need to know the initial condition of x'(0) to do this, which I wasn't provided.

I looked at my notes from class, and in a similar example problem, my professor wound up with the following diff-eq:

[tex]
\ddddot{x}(t) = 16x(t)
[/tex]

He said he took the laplace transform and wound up with:

[tex]
s^4 - 16 = 0
[/tex]

But where did the X(s) go, and why didn't he use the initial conditions? His next step was to solve for s:

[tex]
s = \pm 2, \pm j2
[/tex]

Then he did some kind of generic form of the answer:

[tex]
x^*(t) = A_1 e^{2t} + A_2 e^{-2t} + A_3 e^{j2t} + A_4 e^{-j2t}
[/tex]

at this point, he created 4 simultaneous equations using the original problem's provided points to solve for A1,A2,A3, and A4. So when I try to do the same steps on my problem:

[tex]
-\ddot{x}(t) + 4x(t) + 4 = 0
[/tex]

I take the laplace, ignoring initial conditions:

[tex]
-s^2X(s) + 4X(s) + \frac{4}{s} = 0
[/tex]

But right here, can I really just "drop" the X(s) term and solve for s? Is that mathematically legal?
If I just solve for X(s) as I normally would and then do an inverse laplace, the answer does not satisfy the bounded points. ...and trying to do the laplace transform without the initial x'(0) point is confusing as well.

How do I solve this?

Solve it symbolically, with an unknown parameter ##a = x'(0)##. Then figure out what value of ##a## will give you ##x(1) = 4##. Alternatively, solve the DE without specified boundary conditions; the solution will contain two unknown constants. Determine the two constants from the conditions ##x(0)=1, x(1)= 4##.
 
Last edited:
  • #3
Thanks Ray, I did get an satisfactory answer using the symbolic variable for x'(0), and then solving at the end for x(1) = 4.

You mentioned another way to just ignore initial conditions and I will end up with 2 constants that I need to solve for. This is what I originally tried, using the laplace/partial fraction method...but I did not end up with any constants (I basically got a solution where x(0) = 0 and x'(0) = 0). Care to describe this secondary method?
 
  • #4
Number2Pencil said:
Thanks Ray, I did get an satisfactory answer using the symbolic variable for x'(0), and then solving at the end for x(1) = 4.

You mentioned another way to just ignore initial conditions and I will end up with 2 constants that I need to solve for. This is what I originally tried, using the laplace/partial fraction method...but I did not end up with any constants (I basically got a solution where x(0) = 0 and x'(0) = 0). Care to describe this secondary method?

First, let ##x+1 = z## and notice that the DE is ##\ddot{z} - 4z = 0##. If you use Laplace transforms, you might as well use whatever initial conditions you are given, but if you use another method you get the type of problem I mentioned above. The characteristic equation for the DE is ##r^2 - 4 = 0##, or ##r = \pm 2##, so the solutions will be multiples of ##e^{\pm 2 t}##. That is,
[tex] z(t) = c_1 e^{2t} + c_2 e^{-2t} [/tex]
for some constants ##c_1,c_2##. Use the boundary conditions on ##z## to find ##c_1,c_2##.

Alternatively, you can use the fact that
[tex] {\cal L}[\ddot{z}(t)](s) = s {\cal L}[\dot{z}(t)](s) - \dot{z}(0)\\
= - \dot{z}(0) + s [ s {\cal L}[z(t)](s) - z(0)] = s^2 \tilde{z}(s) - s z(0) - \dot{z(0)},[/tex]
where
[tex] \tilde{z}(s) = {\cal L}[z(t)](s) [/tex]
is the Laplace transform of ##z##. This involves two constants ##z(0), \: \dot{z(0)}##.
 
  • #5
Thanks Ray! Just so I'm clear, was the substitution of x+1=z because of that +4 offset that was impeding you from directly obtaining a characteristic polynomial equation? After finding a solution for z(t), I can just convert back to x knowing that x = z-1
 
  • #6
Number2Pencil said:
Thanks Ray! Just so I'm clear, was the substitution of x+1=z because of that +4 offset that was impeding you from directly obtaining a characteristic polynomial equation? After finding a solution for z(t), I can just convert back to x knowing that x = z-1

Of course.
 

1. What is an optimal trajectory?

An optimal trajectory is the path or route that minimizes or maximizes a certain objective, such as time, cost, or energy, while satisfying certain constraints.

2. What is a bounded end point?

A bounded end point is a constraint or limitation on the final destination or endpoint of the trajectory. This can be a physical boundary or a certain limit that the trajectory must stay within.

3. How is the optimal trajectory with a bounded end point calculated?

The optimal trajectory with a bounded end point is typically calculated using mathematical optimization techniques, such as calculus or linear programming. These methods take into account the constraints and objective to determine the best route.

4. What are some examples of applications for optimal trajectory with bounded end point?

Optimal trajectory with bounded end point can be used in a variety of fields, such as aerospace engineering, robotics, and transportation planning. For example, it can be used to determine the most efficient path for a satellite or a robot to take, or to optimize the route for a delivery truck to minimize time or fuel costs.

5. What are the challenges in finding an optimal trajectory with a bounded end point?

One of the main challenges in finding an optimal trajectory with a bounded end point is balancing the objective and constraints. Often, there may be trade-offs between minimizing time or cost and staying within certain boundaries. Additionally, the complexity of the problem may increase with multiple variables and constraints, making it more difficult to find the optimal solution.

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