# Dynamic Parameter Identification of a Lower Extremity Exoskeleton Using RLS-PSO
## 1 Introduction  
Over the past decades, advances in electromechanical technology have accelerated the development of lower extremity exoskeletons. Unless otherwise stated, the exoskeleton referred to in this article refers to the lower extremity exoskeleton. The exoskeleton is a device that interacts with the human. The main applications of exoskeletons are physical assistance and rehabilitation [1; 2; 3; 4; 5; 6; 7; 8; 9; 10]. During human-machine interaction, under no circumstances should the exoskeleton directly or indirectly cause injury to the human, either in the routine operation or in fault [11]. Therefore, exoskeleton research should not be confined to mechanical design. It is also challenging and important to improve the performance of the exoskeleton's controller and to realize compliance control in the process of human-machine interaction [12; 13; 14]. One of the effective methods to enhance the flexibility of the exoskeleton's controller is to build an accurate dynamic model [15; 16; 17; 18; 19]. Dynamic parameters are used to build the dynamic model of the exoskeleton. There are three main methods to obtain the dynamic parameters of the exoskeleton. First, obtaining the CAD data directly from the manufacturers. This method is not applicable to non-standard products such as exoskeletons. Second, obtaining the CAD data from 3D design software. This method ignores many factors such as the dynamics of hoses and wires, and the internal dynamics of actuators that affect thedynamics of the exoskeleton. Third, obtaining the dynamic parameters through dynamic parameter identification algorithms. This method has high accuracy and requires a series of experiments for data acquisition [20; 21; 22]. The exoskeleton is made up of many non-standard parts, such as pipes and actuators [6; 7; 8; 9; 10], which cause the dynamic parameters of the exoskeleton to fluctuate during working. Obviously, it is difficult to obtain the dynamic parameters from the manufacturers or 3D design software. Therefore, parameter identification algorithms are expected to be an effective way to obtain more accurate dynamic parameters [23; 24]. In order to obtain more accurate dynamic parameters, researchers are increasingly paying attention to the application of parameter identification algorithms for the exoskeleton. Researchers have carried out some preliminary research works. Targeting the swing phase [25; 26], [PERSON] et al. identified the dynamic parameters of Berkeley Exoskeleton (BLEEX) [21] by means of Least Square (LS) [27; 28]. The joint friction coefficients of BLEEX were identified by static experiments, and the inertial parameters [29] were identified by dynamic experiments. The method did not make full use of the non-correlation between the parameters and the linear relationship between the parameters and the parameterized dynamic equations [30; 31]. Therefore, the method only identified one parameter in each experiment. Designing a corresponding identification experiment for each parameter was necessary. The process of parameter identification experiment was very complicated, and the friction parameters on the hip joint lost identifiability because of the limited range of motion. The parameter identification accuracy of this method is low. [PERSON] et al. did not compare the identification accuracy of LS with other algorithms. [PERSON] et al. also used LS for dynamic parameters identification of the exoskeleton [16]. However, the identified parameters were only compared with the CAD data calculated by Solidworks. Due to the existence of a large number of non-standard parts [16], it is difficult to prove the accuracy of this method. [PERSON] [20] proposed a weighted LS method and verified it on an exoskeleton with flexible actuators. The accuracy of the algorithm was also compared with the CAD data of 3D design software. The CAD data could not offer the real value of all the parameters to be identified. Therefore, the accuracy of the method was also not verified.