You certainly can make such a controller, or any other kind of controller. As a matter of fact, there are many other kinds of controllers out there (adaptive controllers, non-linear controllers, etc.). However, PIDs are by far the easiest and more manageable ones out there.
In a PID, the proportional constant deals with \$y_r-y\$ (position errors), where \$y\$ is the measured state and \$y_r\$ is the wanted state, the D-constant applies to \$\dot{y}_r-\dot{y}\$ (velocity errors) and the I-constant with \$\int_{t_0}^{t} (y_r - y) dt\$ (cumulative position errors). Now, adding the D-term can make the overall system unstable, so one have to be careful. You could easily add an acceleration error term [\$A\times(\ddot{y}_r - \ddot{y})\$], but again, you would have to be careful. Notice, that this corresponds to saying that the acceleration of the error signal should have a say in the control of the system.
The usefulness of using the cumulative error of the cumulative position error is rather questionable, but I suppose it could also be of use in some rare cases.
From a pure mathematical view-point, if you deal with a first-order linear system, in Laplace form the equation for the system is \$y(s) = M(s) u(s)\$, where \$u\$ is the "input" to the system. A PID implements \$u(s) = C(s)e(s) = (P + Is^{-1} + sD)e(s)\$, where \$e(s) = y_r(s)-y(s)\$ is the error signal. So,
$$y(s) = M(s)C(s)[y_r(s)-y(s)],$$hence$$y(s) = \frac{M(s)C(s)}{1+M(s)C(s)}y_r(s).$$
Now we can't change the model (M) of the "system", but we can change the controller (C). The "trick" is to find a polynomial \$C(s)\$ such that the above fraction behaves in a nice stable manner while still responding quick enough to changes in the reference signal.