AbstractThis article elaborates the iterative learning mechanism for time‐varying system identification, and describes the learning algorithms that could achieve the consistent estimation for time‐varying parameters under persistent repetitive‐excitation conditions. A dynamic parametrization approach, in this article, is presented for modeling and analysis a general class of nonlinear systems. The derivations are conducted to give linear‐in‐the‐parameters models with time‐varying coefficients. The resultant models can be in a unified form, with the aid of the variable difference representation, and the iterative learning least squares algorithm and its variant are applicable for the purpose of parameter estimation. Moreover, a learning control scheme is adopted for demonstrating effectiveness of the dynamically‐parametrized modes, which are simulated and fully compared with the presented numerical results.