Filtering a signal to have a constant exponential moving average

Problem

I wish to filter a discrete-time input signal, $x[n]$, so as the output signal's, $y[n]$, exponential moving average, $a[n+1] = a[n] + \alpha(y[n] - a[n])$, tracks around a constant reference $\mu_x$ such that $\mathbb{E}\{y[n]\} \approx \mu_x$, and also an approximately constant variance, $\text{var}\{y[n]\} \approx \sigma_x^2$, while maintaining as much information about the incoming signal as possible.

Naive Motivating Solution

1) Maintain an average of the input, $b[n+1] = b[n] + \alpha(x[n] - b[n])$ and an approximation of the signal variance, $c[n]$.

2) Define the output to be $y[n] = \frac{x[n] - b[n]}{\sqrt{c[n]}}$ which tracks a reference of 0 with unity variance.

Question

There are a number of problems with this approach which make it not very numerically robust, such as when low values of variance occur.

Is there a more intelligent way of doing this? Perhaps using a dynamic linear filter? Or high pass filter?

Other Points

The inputs can be considered independent, and are drawn from one of several dynamic distributions.

The application, if you are interested, is comparing the amplitudes of recent inputs with each other by assigning them a "reward" output. This is high reward for relatively higher input values and a low for lower lower values. However, I desire the mean reward to be an approximately constant output mean. Furthermore, there may be some medium-time dynamics in the inputs, such as decaying toward zero over time.