The Optimization Of Ride Comfort Mining Truck Based On Genetic Algorithm And Uncertainty

The riding comfort is one of the important performances of vehicle, especially for heavy mining ones. It not only affects the working environment and efficiency of the driver, the safety of goods in transportation process, but also makes the spare parts of vehicle worn and fatigue damaged earlier than usual. However, traditional optimization of riding comfort is certain optimization, the models of which are constructed by certain methods. That is, apart from design variable, other variables are definite which are varying in practical work, the result of optimization lakes instability. The uncertainty-based optimization method has been rising to a research hotpot in recent years and is applied to various fields. This paper aims to improve the riding comfort by constructing MATLAB/SIMULINK models of overall mining vehicle vibration, and optimizing the riding comfort of mining vehicle through genetic algorithm.In this dissertation, the significance and present status of vehicle riding comfort and uncertain optimization are introduced; the principle of genetic algorithm is systematically exposed and modular modeling of MATLAB/SIMULINK is illustrated briefly. This lays a foundation for modeling, simulation and optimization.In this dissertation, models of eight degrees of freedom mathematical is build, random road grade C is made in a white noise-based method, visual model of module constructed in MATLAB/SIMULINK. After that,this dissertation verifies the correctness and effectiveness of the model though simulation. Than an approximate model of high-dimensional module is constructed in accordance with simulation results. The design variable is concluded by the analysis approach of single factor and the uncertainly variable by practical statistics of vehicle, on basis of which the partially uncertainty optimization and traditional deterministic optimization are made. The analysis of comparison between uncertainty optimization and traditional certainty optimization shows that the partially uncertainty optimization reaches a better robustness of riding comfort than the traditional deterministic optimization.