Since you are using nested functions, you can use their scoping rules to mimic the behavior of global variables.
It's easiest to (ab)use an output function for this purpose:
function solveODE
% ....
%(Initialization of parameters are above this line)
% initialize "global" variable
rDotDot = [];
% Specify output function
options = odeset(...
'Mass', @massMatrix,...
'OutputFcn', @outputFcn);
% solve ODE
[T,q] = ode45(@f, tspan,q0,options);
% show the rDotDots
rDotDot
% derivative
function dqdt = f(~,q)
% q = [qDot phi phiDot r rDot]';
dqdt = [...
-R/L*q(1) - kb/L*q(3) + vs/L
q(3)
kt*q(1) + mp*sin(q(2))*lp*g
q(5)
mp*lp*cos(q(2))*q(3)^2 - ks*q(4) - (mb+mp)*g
];
end % q-dot function
% mass matrix
function M = massMatrix(~,q)
M = [
1 0 0 0 0;
0 1 0 0 0;
0 0 mp*lp^2 0 -mp*lp*sin(q(2));
0 0 0 1 0;
0 0 mp*lp*sin(q(2)) 0 (mb+mp)
];
end % mass matrix function
% the output function collects values for rDotDot at the initial step
% and each sucessful step
function status = outputFcn(t,q,flag)
status = 0;
% at initialization, and after each succesful step
if isempty(flag) || strcmp(flag, 'init')
deriv = f(t,q);
rDotDot(end+1) = deriv(end);
end
end % output function
end
The output function only computes the derivatives at the initial and all successful steps, so it's basically doing the same as what Adrian Ratnapala suggested; re-run the derivative at each of the outputs of ode45
; I think that would even be more elegant (+1 for Adrian).
The output function approach has the advantage that you can plot the rDotDot
values while the integration is being run (don't forget a drawnow
!), which can be very useful for long-running integrations.