Modeling and parameter identification of seated human body with the reference vector guided evolutionary algorithm

Abstract

The low frequency vibration of the vehicle in motion has a great influence on the ride comfort of occupants. The research on the vibration response characteristics of human body plays a great role in analyzing and improving ride comfort. The purpose of this study was to investigate the parameter identification of seated human body dynamic model. A seven-degree-of-freedom (DOF) lumped parameter model was established to describe the vibration response characteristics of human body. The derivation processes of apparent mass (AM) and seat to head transmissibility (STHT) were performed. After the theoretical calculation of the human body vibration characteristics, we used several different evolutionary algorithms to identify the 23 parameters of the model, including the mass, stiffness and damping parameters. By comparing the results of the five optimization algorithms and comprehensively analyzing the convergence and distribution of the non-dominated solution set, we found that the reference vector guided evolutionary algorithm (RVEA) shows good competitiveness in solving many-objective optimization problem (MaOP), that is, parameter identification of seated human body model in this paper. The AM and STHT calculated by model identification were compared with their measured by experiment. The result shows that the selected seven-DOF model can well describe the vertical vibration characteristics of seated human body and the identification method used in this paper can accurately identify the parameters of lumped parameter model, which provides convenience for the establishment of a complete “road-vehicle-seat-human body” system dynamic model.

Sensitivity Analysis of a Driver’s Lumped Parameter Model in the Evaluation of Ride Comfort

“…The transmission of vibration through a seat depends on the mechanical impedance of the body supported by the seat. Thus, SEAT and STHT have been used in the literature to predict or optimize ride comfort [1,10,[28][29][30]. In the following Equations ( 9) and ( 10), SEAT and STHT are defined, respectively.…”

Development of a 7-DOF Biodynamic Model for a Seated Human and a Hybrid Optimization Method for Estimating Human-Seat Interaction Parameters

“…For instance, Bae and Kang, Shao et al, and Huang et al (2020) [10,15,16] optimized the parameters of 2-, 3-, 4-, and 5-DOF models using the gradient-based MATLAB function, called "fmincon". Zhang et al [17] introduced a reference vector-guided evolutionary algorithm to obtain the parameters of a 7-DOF human model, whereas Zhao et al [18] explored multi-objective and genetic algorithms (GAs) for parameter identification of a 5-DOF model. From these studies, it was found that the separate or hybrid application of these optimization techniques is usually associated with trade-offs that include an exhaustive search for initial values, randomly generated optimal solutions, high computation time, and generation of estimated parameters that do not reflect the dynamic response of the human body to WBV [6,19].…”