The combination of characteristic parameters is the key and difficult point to improving the vibration attenuation of scissor seat suspension. This paper proposes a multi-objective optimization method based on entropy weight gray correlation to optimize the combination of characteristic parameters with better vibration attenuation. The differential equation of seat suspension motion is derived through mechanical analysis, and a simplified driver seat suspension single degree of freedom model is constructed. The range of spring stiffness and damper damping is calculated theoretically. Through main effect analysis and analysis of contribution, the main influencing factors of seat suspension vibration attenuation are studied, and the influence correlation of the main factors is analyzed. On this basis, the spring stiffness and damper damping are taken as control variables, and the upper plane acceleration, displacement, and transfer rate of the seat suspension are taken as optimization objectives. The Optimal Latin Hypercube Sampling (OLHS) was used to sample the Design of Experiments (DoE), fit the RBF surrogate model, and screen the optimal solution based on the MNSGA-II algorithm and entropy weight gray relation ranking method. The comparative analysis of the performance before and after optimization shows that the vibration reduction performance response indexes of the acceleration, displacement, and transmissibility of the optimized seats are increased by 66.41%, 2.31%, and 8.19%, respectively. The design and optimization method proposed in this study has a significant effect on the vibration reduction of seats, which provides a reference for the optimization of the vibration reduction performance of seat suspension.

This paper takes the scissor type seat of a domestic combine harvester as the research object, uses CATIA software to carry out three-dimensional modeling of the seat, and then imports the seat model into ANSYS Workbench software for static analysis and modal analysis, so as to judge whether the scissor type seat of the combine harvester meets the design requirements. The results of static analysis show that the maximum stress of the seat under the two different loads is less than the yield strength of the material, and the overall strength of the seat meets the design requirements. The modal analysis results show that the first six natural frequencies of the seat completely avoid the sensitive frequencies of the human body, which basically guarantees the working comfort of the driver. This study provides a theoretical basis for the structural design and optimization of combine seat.

To solve the problems of the Bouc-Wen model with multi-identification parameters, low accuracy, complex methods, and difficulty in implement, this study proposes a new way for parameter identification of the Bouc-Wen model of the magnetorheological (MR) damper by parameter sensitivity analysis and modified PSO algorithm. The one-at-a-time method (OAT) of local sensitivity analysis is utilized to analyze the unknown parameters in the Bouc-Wen model to complete the model simplification. Then, the modified PSO algorithm is used to identify the parameters of the simplified Bouc-Wen model. Finally, with the relationship between the currents and identified parameters, a Bouc-Wen model for current control is constructed by the curve fitting method. The results confirm that the parameter identification efficiency achieved via the parameter sensitivity analysis is improved by 50% by reducing the parameters of the Bouc-Wen model from 8 to 4. Then, compared with the standard PSO (SPSO) algorithm, the modified one is accurate and stable, and the convergence speed is increased by 17.65% on average. At last, compared with the test data under three different sinusoidal excitations, the model’s accuracy is 89.11%, 92.56%, and 87.45%, respectively. The method proposed in this research can rapidly and accurately identify the Bouc-Wen model and lays a theoretical foundation for applying the MR damper model in vibration control.