Technically, if is plotted by a function f(x) with a uniform step size h, then you can use the nabla = gradient(f)/h to compute the slope of f(x).
To find the gradient of the transient response, you need to pick a point in that region, for example, , and then find the index idx that is nearest to, or exactly at this point.
You can try @Faraz Hedayati's code or this code. Both are good learning experiences for you
h = 0.01;
x = 0:h:10;
y = 1 - exp(-x/sqrt(2)).*(cos(x/sqrt(2)) + sin(x/sqrt(2)));
plot(x, y)
hold on
nabla = gradient(y)/h;
[M, idx] = max(nabla);
plot(x(idx), y(idx), 'o', 'linewidth', 1.5)
m = nabla(idx) % slope at point p
c = y(idx) - m*x(idx) % y-intercept
z = m*x + c; % line equation at point p
plot(x, z, 'linewidth', 1.5)
hold off
grid on
xlabel('x')
ylabel('y')
title('y = f(x) and the tangent line at the steepest slope')
legend('function f(x)', 'the point at f(p)', 'tangent line', 'location', 'best')
Result:
Note: If the spread of the data points are not uniform, then you probably need to use the interpolation technique, interp1().
You can also select a web site from the following list
How to Get Best Site Performance
Select the China site (in Chinese or English) for best site performance. Other MathWorks country sites are not optimized for visits from your location.