This paper proposes a multi-innovation stochastic gradient (MISG) parameter estimation algorithm for an input nonlinear controlled autoregressive (IN-CAR) model, i.e., a Hammerstein nonlinear CAR system, by expanding the innovation length. The analysis and simulation results indicate that the proposed MISG algorithm can generate more accurate parameter estimates for IN-CAR systems compared with the stochastic gradient algorithm.