A processor implemented method, comprising: inputting, via the one or more hardware processors, one or more time synchronized command input signals to a moving differential drive vehicle; determining, based on the one or more time synchronized command input signals, a corresponding change in one or more motion parameters of the moving differential drive vehicle; training a non-linear model using the one or more time synchronized command input signals and the determined corresponding change in the one or more motion parameters, wherein the non-linear model is a Hammerstein-Wiener model comprising a static input non-linearity block, a dynamic linear block, and a static output non-linearity block, in series, wherein the static input non-linearity block captures nonlinearity behavior present in the one or more time synchronized command input signals, wherein the dynamic linear block captures dynamic response of the Hammerstein-Weiner model, and wherein the static output non-linearity block captures non-linearity present in the dynamic response of the Hammerstein-Weiner model; generating, using the trained non-linear model, an experimental model by computing a mapping function between the one or more time synchronized command input signals and the one or more motion parameters of the moving differential drive vehicle, wherein the one or more motion parameters comprises position coordinates and velocity coordinates of the moving differential drive vehicle; estimating, using the generated experimental model, value of the one or more motion parameters based on the one or more incoming time synchronized command input signals and orientation information obtained from an inertial measurement unit of the moving differential drive vehicle; performing, using a plurality of data obtained from one or more sensors, a validation of the estimated value of the one or more motion parameters; and adaptively updating the experimental model to estimate an updated value of the one or more motion parameters based on the validation, by learning model parameters in real time. inputting, via the one or more hardware processors, one or more time synchronized command input signals to a moving differential drive vehicle; determining, based on the one or more time synchronized command input signals, a corresponding change in one or more motion parameters of the moving differential drive vehicle; training a non-linear model using the one or more time synchronized command input signals and the determined corresponding change in the one or more motion parameters, wherein the non-linear model is a Hammerstein-Wiener model comprising a static input non-linearity block, a dynamic linear block, and a static output non-linearity block, in series, wherein the static input non-linearity block captures nonlinearity behavior present in the one or more time synchronized command input signals, wherein the dynamic linear block captures dynamic response of the Hammerstein-Weiner model, and wherein the static output non-linearity block captures non-linearity present in the dynamic response of the Hammerstein-Weiner model; generating, using the trained non-linear model, an experimental model by computing a mapping function between the one or more time synchronized command input signals and the one or more motion parameters of the moving differential drive vehicle, wherein the one or more motion parameters comprises position coordinates and velocity coordinates of the moving differential drive vehicle; estimating, using the generated experimental model, value of the one or more motion parameters based on the one or more incoming time synchronized command input signals and orientation information obtained from an inertial measurement unit of the moving differential drive vehicle; performing, using a plurality of data obtained from one or more sensors, a validation of the estimated value of the one or more motion parameters; and adaptively updating the experimental model to estimate an updated value of the one or more motion parameters based on the validation, by learning model parameters in real time.