The development of mathematical models of balance and the identification of their respective parameters may aid understanding the behavior of postural control. Continuous and intermittent control models have been suggested to represent human balance, but the applicability of their parameters is still subject of discussion. The aims of this work are to identify the parameters of continuous and intermittent balance control models from experimental body sway signals, test the plausibility of these models and explore the reliability of the employed identification method. Continuous proportional-integral-derivative (PID) and proportional-derivative (PD), and intermittent PD control parameters were identified using the least squares and the non-negative least squares methods. The models were calculated from data available in a public dataset containing body sway records. Then, these models were simulated and four sway parameters (mean velocity, range, signal RMS and frequency band) were calculated to compare the simulation output with the experimental data. Lastly, the simulated system parameters were re-identified. All the three estimated control models presented a high coefficient of determination R2 (mean R2 ≥ 0.93) and similar root-mean-square error. The simulations of the system with continuous and intermittent PD controllers were able to reproduce body sway patterns. The re-identification of the simulated model suggested that the least squares method may be used to estimate the parameters of the continuous and intermittent PD controllers in balance control, however using the non-negative least squares method with the intermittent model. These identification methods offer a low computational cost and may be employed in future research or clinical assessment of balance.