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An Optimization of Suspension Linkages for Wheel-Legged Vehicle
Technical Paper
2019-01-0167
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
The guiding mechanism of vehicle suspension can keep the wheels moving along planned trajectory. The geometrical design of the reasonable suspension guide mechanism can reduce the vibration transmitted to the body, improve trafficability and handling stability. The vehicle suspension design method was applied to the wheel-legged vehicle, enhancing ride performance. The optimization of suspension hard points can be obtained by using single variable method, adjusting each hard point coordinate independently. It is also widely recommended by using intelligent algorithm to solve well-designed multi-objective parameter optimization function. In this study, the multi-objective parameter optimization function was solved by using the NSGA-II (Non-dominated Sorted Genetic Algorithm-II). Computer simulations with half-car model were used to support the analysis in this study. ADAMS multibody dynamics software was also used to verify the reliability of the results. The advantages in using this methodology are emphasized by an example of the multi-objective parameter function design of suspension hard points and the results are compared with primary values in ADAMS. It was found that the optimized suspension was substantially improved in the respect of wheel alignment, wheelbase and roll center change. By solving the problem of multi-objective function, the NSGA-II exhibited higher efficiency and accuracy than traditional genetic algorithm. The validity of the algorithm was verified. Besides, this paper presented an alternative methodology to improve the design of intelligent robot. More vehicle design methods can be applied to the intelligent vehicle-robot domain. The process of wheel-legged vehicle suspension design provides reference for relevant applications.
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Authors
Citation
Ma, F., Nie, J., Yang, Y., Wang, J. et al., "An Optimization of Suspension Linkages for Wheel-Legged Vehicle," SAE Technical Paper 2019-01-0167, 2019, https://doi.org/10.4271/2019-01-0167.Data Sets - Support Documents
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References
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