Dynamic Parameter Identification of a Lower Extremity Exoskeleton Using RLS-PSO

Abstract

The lower extremity exoskeleton is a device for auxiliary assistance of human movement. The interaction performance between the exoskeleton and the human is determined by the lower extremity exoskeleton's controller. The performance of the controller is affected by the accuracy of the dynamic equation. Therefore, it is necessary to study the dynamic parameter identification of lower extremity exoskeleton. The existing dynamic parameter identification algorithms for lower extremity exoskeletons are generally based on Least Square (LS). There are some internal drawbacks, such as complicated experimental processes and low identification accuracy. A dynamic parameter identification algorithm based on Particle Swarm Optimization (PSO) with search space defined by Recursive Least Square (RLS) is developed in this investigation. The developed algorithm is named RLS-PSO. By defining the search space of PSO, RLS-PSO not only avoids the convergence of identified parameters to the local minima, but also improves the identification accuracy of exoskeleton dynamic parameters. Under the same experimental conditions, the identification accuracy of RLS-PSO, PSO and LS was quantitatively compared and analyzed. The results demonstrated that the identification accuracy of RLS-PSO is higher than that of LS and PSO. Appl. Sci. 2019, 9, 324 2 of 17 dynamics 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], Justin 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. ...

Payload Parameter Identification of a Flexible Space Manipulator System via Complex Eigenvalue Estimation

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“…Then, the complex eigenvalue matrix Λ c and complex eigenvector Ψ c for the dynamics Equations (12) and (13) are determined by using the OKID algorithm. In the next section, the parameters fΛ c , Ψ c g will be used to estimate the inertia parameters m e and J e of the endpoint payload object based on complex modal analysis [33,34].…”

“…Various identification methods have been proposed to estimate the inertia parameters (i.e., mass, mass center, and moment of inertia) of the manipulator and the unknown payload. Most of these methods are least-squares (LS) techniques, which are validated by JEMRMS experiments using space manipulators [9,[12][13][14]. Based on the planning for optimal maneuvers, the inertia parameters of the ETS-VII spacecraft and the manipulator payload are obtained by solving a nonlinear LS problem [9].…”

Dynamic Model and Inverse Kinematic Identification of a 3-DOF Manipulator Using RLSPSO

“…The work of [8] presents an identification of dynamic parameters of the lower extremity exoskeleton using the Particle Swarm Optimization (PSO) metaheuristic in the search space defined by Recursive Least Square (RLS), thus making it a hybrid method. During the definition in the PSO search space, the hybrid method not only avoids the convergence of parental identification to the local minimum, but also has very accurate results.…”

A Low-Cost Soft Robotic Hand Exoskeleton for Use in Therapy of Limited Hand–Motor Function

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