## 1 Introduction
In [19], the authors proposed an identification method that involves solving a weighted least squares problem for an over-determined linear system derived from sampling the dynamic model along a closed-loop tracking trajectory. An experimental study was conducted on a mechanical system to validate the effectiveness of the proposed method. In [20], the authors proposed a non-linear modeling approach based on the Hammerstein structure and used recursive least squares to identify the mechanical parameters of the system, including Coulomb friction and dead zones. They found that the non-linear approach performed better than the linear approach for low-speed operation, where the non-linearities in the system had a significant impact, resulting in a reduction in the identification error. The following examples highlight some more identification methods, emphasizing the similarities and differences amongst them. In [21], the authors studied closed-loop identification procedures for multi-mass systems by comparing different identification models, sampling periods, and model orders. Simulation and experimental results showed that reducing the sampling time can limit high-frequency noise, improve the ratio of useful signal to noise, and accurately localize the crucial points in the spectrum by limiting the sampling frequency. In [4], [PERSON] et al. proposed an identification method that combines a Luenberger observer and the variable forgetting factor recursive least squares method to accurately estimate the mechanical parameters of a 2 MM system. This method was compared with the standard forgetting factor recursive least squares method and found to achieve good results with high identification accuracy in a short amount of time. In a study by [22], a disturbance observer (DOB) method was proposed to identify the mechanical parameters of series elastic actuators (SEAs) modeled as 2 MM systems. The identification process was performed with two closed-loop motor position-tracking experiments, one for identifying the motor-side parameters and spring stiffness, and one for the load-side parameters. Experimental results were provided to validate the proposed method, but the authors noted that further investigation is needed due to non-negligible errors in the Coulomb friction coefficient caused by high nonlinear frictions affecting the device. In [23], [PERSON] and [PERSON] developed an identification method in the frequency domain that combines the Welch method with the Levenberg-Marquardt algorithm to identify the parameters of 2 MM and three-mass (3 MM) systems. The results of this method were found to be effective for the parameter identification of 2 MM and 3 MM systems, as well as more complex mechanical systems. In a study by [PERSON] and [PERSON] [7], a technique was presented that involves the repeated integration of data and the use of the recursive least squares method to identify the mechanical parameters of a 2 MM system. Numerical results demonstrated that the estimated parameters converged to the actual transfer function parameters with high accuracy. An intriguing method was introduced by [PERSON] and [PERSON] [24], where they used the standard particle swarm optimization (PSO) method to determine the mechanical parameters of a 2 MM system with backlash. The PSO is an optimization technique inspired by the social behavior of birds flocking or fish schooling. It is a population-based stochastic optimization algorithm designed to find the global optimum in a problem space. In the context of parameter identification, the PSO algorithm is used to optimize a cost function to determine the best system parameters. The authors compared the results obtained using two different cost functions, and the experimental setup was excited using three different excitation signals for the identification process. However, the cost functions used resulted in relatively large errors, and the experimental results did not match the simulated responses perfectly.