| It has been nearly a hundred years since the birth of the first artificial vehicle.From the first means of transportation to the present various vehicles,people’s pursuit of vehicle performance has included comfort,safety,functionality and other requirements.The range of vehicle travel includes urban roads,rural roads,mountain and jungle,unmanned disaster relief areas and other occasions.Road conditions are closely related to vehicle performance.Therefore,it is of great significance and research value to identify the road surface information during vehicle driving and combine it with the suspension control system to achieve the optimal vehicle performance under various kinds road surface driving conditions.In this paper,the vehicle suspension is taken as the research object,and the control of vehicle suspension is studied with the recognition information of road roughness grade.The details are as follows:First of all,a 1/4 vehicle two-degree-of-freedom suspension model was established,and the random road input was simulated by using the filtered white noise method.The basis for road roughness grade identification and the suspension inverse model theory were introduced,and various vehicle responses were generated by simulation to lay the foundation for the subsequent realization of road roughness grade identification and suspension control.Secondly,the random forest algorithm was used to identify the level of road roughness with the acceleration information of vehicle tires.The wavelet transform method was used to de-noise the data,and then the time-domain statistics feature extraction was carried out.The principal component analysis method is used to reduce the dimension of the feature data,and the extracted parameters were used for pavement grade recognition.Then the algorithm parameters are optimized to optimize the accuracy of pavement recognition.The influence of different condition parameters on the pavement recognition results is analyzed,and the robustness of the recognition method was verified.Then,based on the above level recognition results,performance adjustment modules under different road levels were proposed.And the linear quadratic optimal controller was designed to select the corresponding parameters to adjust the suspension performance indicators.Simulation analysis and comparison with passive suspension showed that the control algorithm could significantly improve the vehicle performance under different driving conditions.Finally,the vehicle test is carried out under the classical road surface in the test yard.The acceleration sensor at the wheel center was used to collect tire vertical acceleration information,and the processed data was used for testing.The feasibility of the identification algorithm of road roughness grade on the actual road surface data was verified by comparing with the pavement grade of elevation information.Verify the control effect on the actual road surface input. |
