NLOptControl.jl¶

Definition¶

• given \((N+1)\) unique data points
  • \((x_0,y_0),(x_1,y_1),....,(x_N,y_N)\)
• we can create an \(N^{th}\) order Lagrange interpolating polynomial

where,

\[\begin{eqnarray} f(x_0) = y_0\\ f(x_1) = y_1\\ .\\ .\\ f(x_i) = y_i\\ .\\ f(x_N) = y_N \end{eqnarray}\]

So, we are just multiplying by the given \(y_i\) values.

Lagrange Basis Polynomials¶

More information on Lagrange Basis Polynomials is here

so expanding this,

\[\begin{eqnarray} \mathcal{L}_i(x) &=\frac{x-x_0}{x_i-x_0}\frac{x-x_1}{x_i-x_1}...\\ &...\frac{x-x_{i-1}}{x_i-x_{i-1}}...\\ &...\frac{x-x_{i+1}}{x_i-x_{i+1}}...\\ &...\frac{x-x_N}{x_i-x_N} \end{eqnarray}\]

Notice that we do not include the term where \(i==j\)!

Please see Functionality for details on implementation.

Euler Method¶

Trapezoidal Method¶

Change of Interval¶

To can change the limits of the integration (in order to apply Quadrature), we introduce \(\tau \in [-1,+1]\) as a new independent variable and perform a change of variable for \(t\) in terms of \(\tau\), by defining:

\[\tau = \frac{2}{t_{{N}_{t}}-t_0}t - \frac{t_{N_t}+t_0}{t_{N_t}-t_0}\]

Polynomial Interpolation¶

Select a set of \(N_t+1\) node points:

\[\mathbf{\tau} = [\tau_0,\tau_1,\tau_2,.....,\tau_{N_t}]\]

• These none points are just numbers
  • Increasing and distinct numbers \(\in [-1,+1]\)

A unique polynomial \(P(\tau)\) exists (i.e. \(\exists! P(\tau)\)) of a maximum degree of \(N_t\) where:

\[f(\tau_k)=P(\tau_k),\;\;\;k={0,1,2,....N_t}\]

• So, the function evaluated at \(\tau_k\) is equivalent the this polynomial evaluated at that point.

But, between the intervals, we must approximate \(f(\tau)\) as:

\[f(\tau) \approx P(\tau)= \sum_{k=0}^{N_t}f(\tau_k)\phi_k(\tau)\]

with \(\phi_k(•)\) are basis polynomials that are built by interpolating \(f(\tau)\) at the node points.

Approximating Derivatives¶

We can also approximate the derivative of a function \(f(\tau)\) as:

With \(\mathbf{D}\) is a \((N_t+1)\times(N_t+1)\) differentiation matrix that depends on:

• values of \(\tau\)
• type of interpolating polynomial

Now we have an approximation of \(\dot{f}(\tau_k)\) that depends only on \(f(\tau)\)!

Approximating Integrals¶

The integral we are interested in evaluating is:

This can be approximated using quadrature:

where \(w_k\) are quadrature weights and depend only on:

• values of \(\tau\)
• type of interpolating polynomial

Legendre Pseudospectral Method¶

• Polynomial

Define an N order Legendre polynomial as:

\[L_N(\tau) = \frac{1}{2^NN!}\frac{\mathrm{d}^n}{\mathrm{d}\tau^N}(\tau^2-1)^N\]

• Nodes

• Differentiation Matrix
• Interpolating Polynomial Function

Single Phase Optimal Control¶

Find:

• The state: \(\mathbf{x}(t)\)
• The control: \(\mathbf{u}(t)\)
• The integrals: \(\mathbf{q}\)
• The initial time: \(t_0\)
• The final time: \(t_f\)

To Minimize:

\[J = \Phi(\mathbf{x}(t_0),\mathbf{x}(t_f),\mathbf{q},t_0,t_f)\]

That Satisfy the Following Constraints:

• Dynamic Constraints:

\[\frac{\mathrm{d}\mathbf{x}}{\mathrm{d}t} = \mathbf{\psi}(\mathbf{x}(t),\mathbf{u}(t),t)\]

• Inequality Path Constraints:

\[\begin{split}\mathbf{c}_{min} <= \mathbf{c}(\mathbf{x}(t),\mathbf{u}(t),t) <= \mathbf{c}_{max}\end{split}\]

• Integral Constraints:

\[q_i = \int_{t_0}^{t_f} \Upsilon_i(\mathbf{x}(t),\mathbf{u}(t),t)\, \mathrm{d}t,\;\;(i=1,....,n_q)\]

• Event Constraints:

\[\begin{split}\mathbf{b}_{min} <= \mathbf{b}(\mathbf{x}(t_0),\mathbf{x}(t_f),t_f,\mathbf{q}) <= \mathbf{b}_{max}\end{split}\]

Change of Interval¶

To can change the limits of the integration (in order to apply Quadrature), we introduce \(\tau \in [-1,+1]\) as a new independent variable and perform a change of variable for \(t\) in terms of \(\tau\), by defining:

\[t = \frac{t_f - t_0}{2}\tau + \frac{t_f + t_0}{2}\]

The optimal control problem defined above (TODO: figure out equation references), is now redefined in terms of \(\tau\) as:

Find:

• The state: \(\mathbf{x}(\tau)\)
• The control: \(\mathbf{u}(\tau)\)
• The integrals: \(\mathbf{q}\)
• The initial time: \(t_0\)
• The final time: \(t_f\)

To Minimize:

\[J = \Phi(\mathbf{x}(-1),\mathbf{x}(+1),\mathbf{q},t_0,t_f)\]

That Satisfy the Following Constraints:

• Dynamic Constraints:

\[\frac{\mathrm{d}\mathbf{x}}{\mathrm{d}\tau} = \frac{t_f-t_0}{2} \mathbf{\psi}(\mathbf{x}(\tau),\mathbf{u}(\tau),\tau,t_0,t_f)\]

• Inequality Path Constraints:

\[\begin{split}\mathbf{c}_{min} <= \mathbf{c}(\mathbf{x}(\tau),\mathbf{u}(\tau),\tau,t_0,t_f) <= \mathbf{c}_{max}\end{split}\]

• Integral Constraints:

\[q_i = \frac{t_f-t_0}{2} \int_{-1}^{+1} \Upsilon_i(\mathbf{x}(\tau),\mathbf{u}(\tau),\tau,t_0,t_f)\, \mathrm{d}\tau,\;\;(i=1,....,n_q)\]

• Event Constraints:

\[\begin{split}\mathbf{b}_{min} <= \mathbf{b}(\mathbf{x}(-1),\mathbf{x}(+1),t_f,\mathbf{q}) <= \mathbf{b}_{max}\end{split}\]

Divide The Interval \(\tau \in [-1,+1]\)¶

The interval \(\tau \in [-1,+1]\) is now divided into a mesh of K mesh intervals as:

\[[T_{k-1},T_k], k = 1,...,T_K\]

with \((T_0,...,T_K)\) being the mesh points; which satisfy:

\[\begin{split}-1 = T_0 < T_1 < T_2 < T_3 < ........... < T_{K-1} < T_K = T_f = +1\end{split}\]

Rewrite the Optimal Control Problem using the Mesh¶

Find:

• The state : \(\mathbf{x}^{(k)}(\tau)\) in mesh interval k
• The control: \(\mathbf{u}^{(k)}(\tau)\) in mesh interval k
• The integrals: \(\mathbf{q}\)
• The initial time: \(t_0\)
• The final time: \(t_f\)

To Minimize:

\[J = \Phi(\mathbf{x}^{(1)}(-1),\mathbf{x}^{(K)}(+1),\mathbf{q},t_0,t_f)\]

That Satisfy the Following Constraints:

• Dynamic Constraints:

\[\frac{\mathrm{d}\mathbf{x}^{(k)}(\tau^{(k)})}{\mathrm{d}\tau^{(k)}} = \frac{t_f-t_0}{2} \mathbf{\psi}(\mathbf{x}^{(k)}(\tau^{(k)}),\mathbf{u}^{(k)}(\tau^{(k)}),\tau^{(k)},t_0,t_f),\;\;(k=1,...,K)\]

• Inequality Path Constraints:

• Integral Constraints:

• Event Constraints:

• State Continuity

  • Also, we must now constrain the state to be continuous at each interior mesh point \((T_1,...T_{k-1})\) by enforcing:

    \[\mathbf{y}^{k}(T_k) = \mathbf{y}^{k+1}(T_k)\]

Optimal Control Problem Approximation¶

The optimal control problem will now be approximated using the Radau Collocation Method as which follows the description provided by [BGar11]. In collocation methods, the state and control are discretized at particular points within the selected time interval. Once this is done the problem can be transcribed into a nonlinear programming problem (NLP) and solved using standard solvers for these types of problems, such as IPOPT or KNITRO.

For each mesh interval \(k\in[1,..,K]\):

\[\begin{eqnarray} \mathbf{x}^{(k)}(\tau)&\approx\mathbf{X}^{(k)}(\tau)=\displaystyle\sum_{j=1}^{N_k+1}\mathbf{X}_j^{(k)}\frac{\mathrm{d}\mathcal{L}_j^{k}(\tau)}{\mathrm{d}\tau}\\ where,\;\;&\\ \mathcal{L}_j^{k}(\tau)&=\prod_{\substack{l=1 \\ l\neq j}}^{N_k+1}\frac{\tau-\tau_l^{(k)}}{\tau_j^{(k)}-\tau_l^{(k)}}\\ and,\;\;&\\ &D_{ki}=\dot{\mathcal{L}}_i(\tau_k)=\frac{\mathrm{d}\mathcal{L}_j^{k}(\tau)}{\mathrm{d}\tau} \end{eqnarray}\]

also,

• \(\mathcal{L}_j^{(k)}(\tau),\;\;(j=1,...,N_k+1)\) is a basis of Lagrange polynomials

• \((\tau_1^{k},.....,\tau_{N_k}^{(k)})\) are the Legendre-Gauss-Radau collocation points in mesh interval k

  • defined on the subinterval \(\tau^{(k)}\in[T_{k-1},T_k]\)
  • \(\tau_{N_k+1}^{(k)}=T_k\) is a noncollocated point

A basic description of Lagrange Polynomials is presented in Lagrange Interpolating Polynomials

The \(\mathbf{D}\) matrix:

• Has a size \(= [N_c]\times[N_c+1]\)

  • with \((1<=k<=N_c), (1<=i<=N_c+1)\)

  • this non-square shape because the state approximation uses the \(N_c+1\) points:

    \((\tau_1,...\tau_{N_c+1})\)

  • but collocation is only done at the \(N_c\) LGR points:

    \((\tau_1,...\tau_{N_c})\)

If we define the state matrix as:

The dynamics are collocated at the \(N_c\) LGR points using:

\(\mathbf{D}_k\mathbf{X}^{LGR} = \frac{(t_f-t_0)}{2}\mathbf{f}(\mathbf{X}_k,\mathbf{U}_k,\tau,t_0,t_f)\;\;for\;\;k = {1,...,Nc}\)

with,

• \(\mathbf{D}_k\) being the \(k^{th}\) row of the \(\mathbf{D}\) matrix.

References

Approximation of Optimal Control Problem¶

Completed Functionality¶

Lagrange Basis Polynomials¶

Functionality¶

The basic description of this functionality is detailed here Lagrange Interpolating Polynomials

lagrange_basis_poly()¶

The Lagrange basis polynomial equations where turned into a function.

interpolate_lagrange()¶

The interpolation functionality was pushed to a lower level. This allows the user to easily use code to interpolate a polynomial.

The development of these function can be:

• Viewed remotely on using the jupyter nbviewer.
• Viewed locally and interacted using IJulia

To do this in julia type:

using IJulia
notebook(dir=Pkg.dir("NLOptControl/examples/LIP/lagrange_basis_poly_dev"))

Examples¶

Simple Interpolation -> ex#1¶

In this first example, we demonstrate the functionality using the interpolating functionality.

where:

\[y(x) = x^2\]

and the interval from x=1 to x=3

with:

N = 2; # number of collocation points

Test 1a¶

• Conclusions
  • Working as expected.

Scaled Lagrange Polynomials-> ex#2¶

where:

\[y(x) = x^3\]

and the interval from x=1 to x=3

with:

N = 2; # number of collocation points

Test 2a¶

• Conclusions
  • Each scaled Lagrange polynomial passes through a control point
  • The final interpolating polynomial passes through each control point and is exact

Runge’s Phenomena-> ex#3¶

This example investigates Runge’s phenomena.

where:

\[y(x) = \frac{1}{1+25x^2}\]

and the interval from x0=-1 to xf=1

with:

N = order of Lagrange Polynomial
x_data = linspace(x0,xf,N+1);

• Conclusions
  • Be careful not to use too high of an N

To Mitigate Runge’s Phenomenon

• Could sample more near the end points
• Could use Chebyshev nodes

These examples can be:

• Viewed remotely on using the jupyter nbviewer.
• Viewed locally and interacted using IJulia

To do this in julia type:

using IJulia
notebook(dir=Pkg.dir("NLOptControl/examples/LIP/"))

Legendre Gaussian Method¶

LGL Single Interval¶

Basic Problem Definition¶

The code developed in this package uses the Legendre-Pseudospectral Method with Lagrange-Gauss-Lobatto (LGL) nodes. A basic description of this implementation presented in this documentation at Pseudospectral Methods and more much more detailed information can be found in both [ASTW11] and [AHer15].

Examples¶

In these examples we use:

• Legendre-Gauss-Lobatto (LGL) nodes
• Single interval approximations
• Approximate integrals in the range of [-1,1]
• Approximate derivatives in the range of [-1,1]

These examples can be:

• Viewed remotely on using the jupyter nbviewer.

• Viewed locally and interacted using IJulia

  To do this in julia type:

  using IJulia
  notebook(dir=Pkg.dir("NLOptControl/examples/LGL_SI/"))
  

Example 1¶

In the first example, we borrow a problem from Wikipedia.

where:

\[y(x) = 7x^3-8x^2-3x+3\]

Difference between the Wikipedia Example and this Example

The difference between Wikipedia example and this one is that the Wikipedia example uses Gauss-Legendre Quadrature while the code developed in this package uses Legendre-Pseudospectral Method with Lagrange-Gauss-Lobatto (LGL) nodes. Information on the difference between these methods and many more can be found in both [BSTW11] and [BHer15].

• Conclusions
  • We are able to exactly determine the integral

References

[BHer15]

Daniel Ronald Herber. Basic implementation of multiple-interval pseudospectral methods to solve optimal control problems. UIUC-ESDL-2015-01, 2015.

[BSTW11]

Jie Shen, Tao Tang, and Li-Lian Wang. Spectral methods: algorithms, analysis and applications. Volume 41. Springer Science & Business Media, 2011.

Example 2¶

In the second example, we increase the order of the polynomial from 3 to 7. Then we increase N until we achieve accurate enough results.

where:

\[y(x) = 7x^7-8x^6 - 3x^5 + 3x^4 + 7x^3-8x^2-3x+3\]

Test 2a¶

Test 2b¶

Test 2c¶

Conclusions¶

• We are able to determine the integral very accurately when we increase N to 4

Example 3¶

In the third example, we approximate the derivative of a function by Approximating Derivatives.

Test 3a¶

In this test:

\[y(x) = 3x^2-3x+3\]

• We are able to determine the derivative exactly when they are linear functions is \(N = 3\)

Test 3b¶

In this test we increase the order of y(x) to:

\[y(x) = 3x^3 + 3x^2-3x+3\]

• When the derivative function becomes nonlinear
  • We can no longer calculate it exactly everywhere
  • There are only \(N_{t+1} = 4\) node points
  • To calculate the derivative exactly we would need an \(∞\) amount of \(N_{t+1}\)
• We are still calculating the integral exactly and should be able to with \(N = 3\) until \(x^5\)

Test 3c¶

In this test we increase the order of y(x) to:

\[y(x) = 3x^4 + 3x^3 + 3x^2-3x+3\]

• We are still calculating the integral exactly and should be able to with \(N = 3\) until \(x^5\)

Test 3d¶

In this test we increase the order of y(x) to:

\[y(x) =3x^5 + 3x^4 + 3x^3 + 3x^2-3x+3\]

• We are still calculating the integral exactly with \(N = 3\)!!
• The percent error is = 0.000000000000000000 %

Test 3e¶

In this test we increase the order of y(x) to:

\[y(x) =3x^6 + 3x^5 + 3x^4 + 3x^3 + 3x^2-3x+3\]

• As expected, we are not still calculating the integral exactly with \(N = 3\)!!
• The percent error is = -1.818181818181822340 %

References

[AHer15]

Daniel Ronald Herber. Basic implementation of multiple-interval pseudospectral methods to solve optimal control problems. UIUC-ESDL-2015-01, 2015.

[ASTW11]

Jie Shen, Tao Tang, and Li-Lian Wang. Spectral methods: algorithms, analysis and applications. Volume 41. Springer Science & Business Media, 2011.

LGR Single Interval¶

Basic Problem Definition¶

The code developed here uses the Legendre-Pseudospectral Method with Legendre-Gauss-Radau (LGR) nodes. This example demonstrates using the LGR points to calculate the integral and the derivative of a known polynomial function. It can be seen, that it behaves as expected. One, major difference between LGR and LGL is that the LGR method does NOT use both endpoints, in fact the LGR method omits the final end point. Researchers at the University of Florida describe this method in many papers including [A1][A2][A3][A4].

Examples¶

In these examples we use:

• Legendre-Gauss-Lobatto (LGR) nodes
• Single interval approximations
• Approximate integrals in the range of [x0,xf]
• Approximate derivatives in the range of [x0,xf]

These examples can be:

• Viewed remotely on using the jupyter nbviewer.

• Viewed locally and interacted using IJulia

  To do this in julia type:

  using IJulia
  notebook(dir=Pkg.dir("NLOptControl/examples/LGR_SI/"))
  

Example 1¶

In the first example, we demonstrate the functionality using LGR nodes using N=2.

where:

\[y(x) = 3x^4+7x^3-8x^2-3x+3\]

Test 1a¶

Notice for all cases, that the end points are NOT included.

This is one of the differences between the LGL and LGR methods.

Test 1b¶

In Test 1b N=4

So we can calculate the integral of a 4th order polynomial exactly. Because 2*N-2 = 4.

Test 1c¶

Test 1d¶

• Conclusions
  • Working as expected.

References

[A1]	Christopher L Darby. hp–Pseudospectral Method for Solving Continuous-Time Nonlinear Optimal Control Problems. PhD thesis, University of Florida, 2011.

[A2]	Divya Garg. Advances in global pseudospectral methods for optimal control. PhD thesis, University of Florida, 2011.

[A3]	Divya Garg, Michael Patterson, William W Hager, Anil V Rao, David A Benson, and Geoffrey T Huntington. A unified framework for the numerical solution of optimal control problems using pseudospectral methods. Automatica, 46(11):1843–1851, 2010.

[A4]	Divya Garg, Michael A Patterson, William W Hager, Anil V Rao, David A Benson, and Geoffrey T Huntington. An overview of three pseudospectral methods for the numerical solution of optimal control problems. Advances in the Astronautical Sciences, 135(1):475–487, 2009.

LGR Multiple Interval¶

Now, using Legendre-Gauss-Radau (LGR) points with multiple intervals to calculate the integral and the derivative of a known polynomial function. This example demonstrates using the Legendre-Gauss-Radau (LGR) points to calculate the integral and the derivative of a known polynomial function using a multiple interval approach.

Researchers at the University of Florida

describe this method in many papers including [CDar11][CGar11][CGPH+10][CGPH+09].

Functionality¶

NOTE: this is not an exhaustive list and there are help notes for most functions, that can be easily seen by typing:

julia>?  # then press enter and type
         # the name of the function of interest

LGR_matrices(ps,nlp)¶

• Calculate LGR matrices
  • IMatrix
  • DMatrix

Notes:¶

• Make sure that you calculate these matrices before you try and use them in either integrate_states() or differentiate_state().

Examples¶

In these examples we use:

• Legendre-Gauss-Radau (LGR) nodes
• Multiple interval approximations
• Approximate integrals in the range of [x0,xf]
• Approximate derivatives in the range of [x0,xf]

Neglecting Non-Collocated Point \(Y^{(k)}(τ)\) -> ex#1¶

In this first example, we demonstrate the functionality using LGR nodes

where:

\[y(x) = -3x^5+3x^4+7x^3-8x^2-3x+3\]

Test 1a¶

with:

Nc = Int64(10); # number of collocation points in each interval
Ni = Int64(4);  # number of intervals

Test 1b¶

with:

Nc = Int64(3); # number of collocation points in each interval
Ni = Int64(2);  # number of intervals

Test 1c¶

with:

Nc = Int64(4); # number of collocation points in each interval
Ni = Int64(2);  # number of intervals

• Conclusions

  • It seems, that using the multiple interval formulation sacrifices the property where

    • We can calculate a Pth order polynomial exactly with \(2*N-2\) collocation points
    • We do not approximate a 5th order polynomial with 6 total collocation points
    • Looking at Test 1c, we can see that when we use N=4 we calculate the intergral exactly
      • So the property applies only to each interval

  • Test 1b and Test 1c both show that we are not calculating the end point

Approximation of \(Y^{(k)}(τ)\) -> ex#2¶

In the previous example, we neglected the approximation of the final state in each interval \(Y^(k)(τ)\).

In this example, we will demonstrate calculation of this state.

where:

\[y(x) = 3x^2-3x+3\]

with:

Nc = Int64(3); # number of collocation points in each interval
Ni = Int64(2);  # number of intervals

Why Do We Need This State?

It is needed to make the constraint that the states at the end of each mesh grid are equal.

Approximation of State Derivative at of Mesh Grids -> ex#3¶

In this example, the state derivative at the end of each mesh.

where:

\[y(x) = -0.3x^2+3x-0.3\]

with:

Nc = Int64(3); # number of collocation points in each interval
Ni = Int64(2);  # number of intervals

Why Do We Need This Derivative At the End Point?

Actually, we do not need this. There is no constraint on state dynamics at the end of each mesh grid using the method discussed in [DGar11]. This is an important point, that is described here hp-psuedospectral method.

Now, we will look at the \(\mathbf{D}\) matrix used to calculate the derivatives above:

D =
4×4×2 Array{Float64,3}:
[:, :, 1] =
 -1.0         1.50639   -1.10639    0.6
 -0.210639   -0.155051   0.713568  -0.347878
  0.0506395  -0.233568  -0.644949   0.827878
 -0.0666667   0.276429  -2.00976    1.8

[:, :, 2] =
 -1.0         1.50639   -1.10639    0.6
 -0.210639   -0.155051   0.713568  -0.347878
  0.0506395  -0.233568  -0.644949   0.827878
 -0.0666667   0.276429  -2.00976    1.8

Notice that for each interval the \(\mathbf{D}\) matrix is actually identical. This is quite an interesting observation indeed, because different inputs where used to calculate it, these are the nodes.

For the first interval:

0.0
1.77526
4.22474
5.0

For the second interval:

5.0
6.77526
9.22474
10.0

These nodes depend on the interval \(t_0->t_f\) as well as the \(\tau\):

-1.0
-0.289898
 0.689898

Which are the LGR nodes when \(N_c=3\)

So, it seems that maybe we can calculate the weights beforehand as well as the \(\mathbf{D}\) matrix and cache the result.

Neglecting Derivative At End Of Mesh¶

For the purposes of using this method for control, again we do not need to calculate the derivative of the state at the ends of each mesh. So, we can remove the bottom row of the \(\mathbf{D}\) matrix as:

D =
[:, :, 1] =
 -1.0         1.50639   -1.10639    0.6
 -0.210639   -0.155051   0.713568  -0.347878
  0.0506395  -0.233568  -0.644949   0.827878

[:, :, 2] =
 -1.0         1.50639   -1.10639    0.6
 -0.210639   -0.155051   0.713568  -0.347878
  0.0506395  -0.233568  -0.644949   0.827878

So, at the end of each mesh grid, we still approximate the state, but neglect it’s derivative.

References

[DGar11]

Divya Garg. Advances in global pseudospectral methods for optimal control. PhD thesis, University of Florida, 2011.

Higher Level Functionality -> ex#4¶

In this example, we are working on preparing the code for use with optimization by creating higher level functionality. Examine the IJulia notebook to see differences in code.

where:

\[y(x) = -0.3x^2+3x-0.3\]

with:

Nc = Int64(3); # number of collocation points in each interval
Ni = Int64(2);  # number of intervals

These examples can be:

• Viewed remotely on using the jupyter nbviewer.

• Viewed locally and interacted using IJulia

  To do this in julia type:

  using IJulia
  notebook(dir=Pkg.dir("NLOptControl/examples/LGR_MI/"))
  

References

[CDar11]

Christopher L Darby. hp–Pseudospectral Method for Solving Continuous-Time Nonlinear Optimal Control Problems. PhD thesis, University of Florida, 2011.

[CGar11]

Divya Garg. Advances in global pseudospectral methods for optimal control. PhD thesis, University of Florida, 2011.

[CGPH+10]

Divya Garg, Michael Patterson, William W Hager, Anil V Rao, David A Benson, and Geoffrey T Huntington. A unified framework for the numerical solution of optimal control problems using pseudospectral methods. Automatica, 46(11):1843–1851, 2010.

[CGPH+09]

Divya Garg, Michael A Patterson, William W Hager, Anil V Rao, David A Benson, and Geoffrey T Huntington. An overview of three pseudospectral methods for the numerical solution of optimal control problems. Advances in the Astronautical Sciences, 135(1):475–487, 2009.

Optimal Control Problem Formulation¶

NLP Problem Initialization¶

Here we are developing software to set up and keep track of all of the variables in the problem.

Functionality¶

ps, nlp = initialize_NLP(numStates=1,numControls=1,Ni=1,Nck=[2])¶

• To initialize the problem

To use:

using NLOptControl
ps, nlp = initialize_NLP(numStates=1,numControls=1,Ni=2,Nck=[2,3]);

ps = pseudospectral method related data:

NLOptControl.PS_data
  Nck: [2,3]
  Ni: 2
  τ: Array{Float64,1}[[-1.0,0.333333],[-1.0,-0.289898,0.689898]]
  ts: Array{Float64,1}[[-0.0,0.0],[-0.0,-0.0,0.0]]
  ω: Array{Float64,1}[[0.5,1.5],[0.222222,1.02497,0.752806]]
  ωₛ: Array{Float64,1}[[0.0,0.0],[0.0,0.0,0.0]]
  t0: 0.0
  tf: 0.0
  DMatrix: Array{Float64,2}[[0.0 0.0 0.0; 0.0 0.0 0.0],[0.0 0.0 0.0 0.0; 0.0 0.0 0.0 0.0; 0.0 0.0 0.0 0.0]]
  IMatrix: Array{Float64,2}[[0.0 0.0; 0.0 0.0],[0.0 0.0 0.0; 0.0 0.0 0.0; 0.0 0.0 0.0]]
  stateMatrix: Array{Float64,2}[[0.0; 0.0; 0.0],[0.0; 0.0; 0.0; 0.0]]
  controlMatrix: Array{Float64,2}[[0.0; 0.0],[0.0; 0.0; 0.0]]

nlp = nonlinear programming problem related data:

NLOptControl.NLP_data
numStates: 1
numControls: 1
numStatePoints: [3,4]
numControlPoints: [2,3]
lengthStateVector: 7
lengthControlVector: 5
lengthDecVector: 14
timeStartIdx: 13
timeStopIdx: 14
stateIdx: Tuple{Int64,Int64}[(1,3),(4,7)]
controlIdx: Tuple{Int64,Int64}[(8,9),(10,12)]
stateIdx_all: Tuple{Int64,Int64}[(-99,-99)]
controlIdx_all: Tuple{Int64,Int64}[(-99,-99)]
stateIdx_st: Tuple{Int64,Int64}[(-99,-99)]
controlIdx_ctr: Tuple{Int64,Int64}[(-99,-99)]
decisionVector: [0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]

Comments¶

• Lots of zeros right now because we still have a lot of initializing to do.
• numControls and numStates can both be greater than 1.
• The value of Ni must match the length of Nck

generate_Fake_data(nlp,ps,γ)¶

• Generating some data to play with is useful:

nlp2ocp(nlp,ps)¶

• Turning the nonlinear-programming problem into an optimal control problem
  • This function basically takes a large design variable an sorts it back into the original variables

ζ, approx_int_st = integrate_state(ps,nlp)¶

Approximates integral.

To use this function with Gaussian Quadrature (the default method):

ζ, approx_int_st = integrate_state(ps,nlp)

To use this function with the LGRIM:

integrate_state(ps,nlp;(:mode=>:LGRIM))

dζ = differentiate_state(ps,nlp)¶

Approximate derivatives.

• Currently only using LGRDM as a method.

Examples¶

In these examples we use:

• Demonstrate functionality to setup optimal control problem
• Also include the development scripts of these functions
• There is not a webpage for all examples, but the interested user can check out them out using IJulia

NLP and OCP Functionality -> ex#2¶

In this example, we are continuing to work on preparing the code for use with optimization by creating higher level functionality. Examine the IJulia notebook to see code.

Aside from using the new data structures we demonstrate:

• Using the integration matrix for the first time
• Using the higher level functionality

where:

\[y(x) = -0.3x^2+3x-0.3\]

Single Interval Problem¶

with:

ps, nlp = initialize_NLP(numStates=1,numControls=1,Ni=1,Nck=[2]);

Multiple Interval Problem A¶

with:

ps, nlp = initialize_NLP(numStates=1,numControls=1,Ni=3,Nck=[2,3,5]);

Multiple Interval Problem B¶

with:

ps, nlp = initialize_NLP(numStates=1,numControls=1,Ni=5,Nck=[200,3,100,5,100]);

Multiple States -> ex#3¶

In this example, we are going to approximate the 5th order Taylor series polynomial for sin() and cos() expanded about x=0.

where:

\[sin(x) ≈ P_5(x) = x - \frac{x^3}{3!} + \frac{x^5}{5!}\]

\[cos(x) ≈ P_5(x) = 1 - \frac{x^2}{2!} + \frac{x^4}{4!}\]

and:

t0 = Float64(0); tf = Float64(2);

Problem A¶

with:

ps, nlp = initialize_NLP(numStates=1,numControls=1,Ni=5,Nck=[200,3,100,5,100]);

Comments on failing to calculate integral in Problem A

This is expected. Looking at the smallest mesh grid size Nck=1, we can only expect to calculate a the integral for a 2*1-2=0th order polynomial exactly.

Problem B¶

with:

ps, nlp = initialize_NLP(numStates=numStates,numControls=1,Ni=2,Nck=[4,4]);

These examples can be:

• Viewed remotely on using the jupyter nbviewer.

• Viewed locally and interacted using IJulia

  To do this in julia type:

  using IJulia
  notebook(dir=Pkg.dir("NLOptControl/examples/NLP2OCP/"))
  

Researchers at the University of Florida

Describe this method in many papers including [EDar11][EGar11][EGPH+10][EGPH+09].

References

[EDar11]

Christopher L Darby. hp–Pseudospectral Method for Solving Continuous-Time Nonlinear Optimal Control Problems. PhD thesis, University of Florida, 2011.

[EGar11]

Divya Garg. Advances in global pseudospectral methods for optimal control. PhD thesis, University of Florida, 2011.

[EGPH+10]

Divya Garg, Michael Patterson, William W Hager, Anil V Rao, David A Benson, and Geoffrey T Huntington. A unified framework for the numerical solution of optimal control problems using pseudospectral methods. Automatica, 46(11):1843–1851, 2010.

[EGPH+09]

Divya Garg, Michael A Patterson, William W Hager, Anil V Rao, David A Benson, and Geoffrey T Huntington. An overview of three pseudospectral methods for the numerical solution of optimal control problems. Advances in the Astronautical Sciences, 135(1):475–487, 2009.

Developing Code¶

This site documents current progress and functionality that is being developed.

Current Focus¶

Old Records¶

This is for documentation that was created where something was still being worked on where:

1. Everything was not working as expected, but has now been fixed or is obsolete.
2. Removed Functionality