PROPT Van der Pol Oscillator

This page is part of the PROPT Manual. See PROPT Manual.

Restricted second order information for the solution of optimal control problems using control vector parameterization. 2002, Eva Balsa Canto, Julio R. Banga, Antonio A. Alonso Vassilios S. Vassiliadis

Case Study 6.1: van der Pol oscillator

This case study has been studied by several authors, for example Morison, Gritsis, Vassiliadis and Tanartkit and Biegler.

Problem Description

The dynamic optimization problem is to minimize:

$J=x_{3}(t_{f})$

subject to:

${\frac {dx_{1}}{dt}}=(1-x_{2}^{2})*x_{1}-x_{2}+u$ ${\frac {dx_{2}}{dt}}=x_{1}$ ${\frac {dx_{3}}{dt}}=x_{1}^{2}+x_{2}^{2}+u^{2}$ $-0.3<=u<=1.0$ $x(t_{0})=[0\ 1\ 0]'$ $t_{f}=5$

% Copyright (c) 2007-2008 by Tomlab Optimization Inc.

Problem setup

toms t
p = tomPhase('p', t, 0, 5, 60);
setPhase(p);

tomStates x1 x2 x3
tomControls u

% Initial guess
x0 = {icollocate({x1 == 0; x2 == 1; x3 == 0})
    collocate(u == -0.01)};

% Box constraints
cbox = {-10  <= icollocate(x1) <= 10
    -10  <= icollocate(x2) <= 10
    -10  <= icollocate(x3) <= 10
    -0.3 <= collocate(u)   <= 1};

% Boundary constraints
cbnd = initial({x1 == 0; x2 == 1; x3 == 0});

% ODEs and path constraints
ceq = collocate({dot(x1) == (1-x2.^2).*x1-x2+u
    dot(x2) == x1; dot(x3) == x1.^2+x2.^2+u.^2});

% Objective
objective = final(x3);

Solve the problem

options = struct;
options.name = 'Van Der Pol';
solution = ezsolve(objective, {cbox, cbnd, ceq}, x0, options);
t  = subs(collocate(t),solution);
x1 = subs(collocate(x1),solution);
x2 = subs(collocate(x2),solution);
x3 = subs(collocate(x3),solution);
u  = subs(collocate(u),solution);

Problem type appears to be: lpcon
Time for symbolic processing: 0.11775 seconds
Starting numeric solver
===== * * * =================================================================== * * *
TOMLAB - TOMLAB Development license  999007. Valid to 2011-12-31
=====================================================================================
Problem: ---  1: Van Der Pol                    f_k       2.867259538084708100
                                       sum(|constr|)      0.000000020744545091
                              f(x_k) + sum(|constr|)      2.867259558829253300
                                              f(x_0)      0.000000000000000000

Solver: snopt.  EXIT=0.  INFORM=1.
SNOPT 7.2-5 NLP code
Optimality conditions satisfied

FuncEv    1 ConstrEv   26 ConJacEv   26 Iter   23 MinorIter  348
CPU time: 0.218401 sec. Elapsed time: 0.222000 sec. 

Plot result

subplot(2,1,1)
plot(t,x1,'*-',t,x2,'*-',t,x3,'*-');
legend('x1','x2','x3');
title('vanDerPol state variables');

subplot(2,1,2)
plot(t,u,'+-');
legend('u');
title('vanDerPol control');