This paper presents a unified treatment of the tracking analysis of adaptive filters with data normalization and error nonlinearities. The approach we develop is based on the celebrated energy-conservation framework, which investigates the energy flow through each iteration of an adaptive filter. Aside from deriving earlier results in a unified manner, we obtain new performance results for more general filters without restricting the regression data to a particular distribution. Simulations show good agreement with the theoretical findings.