Grusins Metric
A Short Introduction to Optimal Control, Ugo Boscain, SISSA, Italy
3.4 A Detailed Application: the Grusin's Metric
Contents
Problem Description
Find u over t in [0; 1 ] to minimize:

subject to:


![$$ x(t_0) = [0 \ 0 ] $$](xgrusinsMetric_eq06868.png)
![$$ x(t_f) = [-0.001 \ -1 ] $$](xgrusinsMetric_eq08888.png)
% Copyright (c) 2007-2008 by Tomlab Optimization Inc.
Problem setup
toms t p = tomPhase('p', t, 0, 1, 60); setPhase(p); tomStates x1 x2 tomControls u1 u2 % Boundary constraints cbnd = {initial({x1 == 0; x2 == 0}) final({x1 == -0.01; x2 == -1})}; % ODEs and path constraints ceq = collocate({dot(x1) == u1 dot(x2) == u2.*x1}); % Objective objective = integrate(u1.^2+u2.^2);
Solve the problem
options = struct;
options.name = 'Grusins Metric';
solution = ezsolve(objective, {cbnd, ceq}, [], options);
t = subs(collocate(t),solution);
x1 = subs(collocate(x1),solution);
x2 = subs(collocate(x2),solution);
u1 = subs(collocate(u1),solution);
u2 = subs(collocate(u2),solution);
Problem type appears to be: qpcon
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TOMLAB - Tomlab Optimization Inc. Development license 999001. Valid to 2010-02-05
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Problem: --- 1: Grusins Metric f_k 6.333416558770673900
sum(|constr|) 0.000000007863215256
f(x_k) + sum(|constr|) 6.333416566633888900
f(x_0) 0.000000000000000000
Solver: snopt. EXIT=0. INFORM=1.
SNOPT 7.2-5 NLP code
Optimality conditions satisfied
FuncEv 1 ConstrEv 81 ConJacEv 81 Iter 74 MinorIter 326
CPU time: 1.062500 sec. Elapsed time: 1.062000 sec.
Plot result
subplot(2,1,1) plot(x1,x2,'*-'); legend('x1 vs x2'); title('Grusins Metric state variables'); subplot(2,1,2) plot(t,u1,'+-',t,u2,'+-'); legend('u1','u2'); title('Grusins Metric control');