Research On Adaptive Switching Method Of Automobile Suspension Control Under Different Working Conditions

In order to improve the ride comfort and driving safety of vehicles,the continuous development of automotive electronic control technology has made the vehicle suspension system more intelligent.By adjusting the adjustment factor of the active suspension in real-time control,the vehicle can achieve good riding and stability under different road excitations.Therefore,the active suspension has the advantage that the traditional suspension can't be accomplish.The paper relies on the National Natural Science Foundation of China Youth Fund Project(51605213)and Liaoning Science and Technology Department Joint Fund Project(201602367),Take the damped adjustable suspension as the research object,select the three types of working conditions of the urban road including the random straight road,the slope road and the continuous speed bump,the character of road is identified by the modal energy.The vehicle suspension local controller under driving conditions is constructed then the adaptive damping control strategy of suspension damping based on weighted sum of characteristic coefficients is constructed,and the neural network algorithm is used to design the switching controller to realize the smooth switching of suspension damping control under different roads.Specific research includes the following aspects:Firstly,the vehicle vibration model and the comprehensive road surface input model with different road conditions including random straight road,slope road and continuous speed reducer are constructed.The modal energy method is used to analyze the vehicle motion state.The modal energy ratio of the moving mode and its rate of change enable the identification of pavement features in different working conditions.Secondly,three local controllers are constructed based on different control algorithms for three different road conditions.For the random straight road conditions,the PID algorithm controller is constructed.For the slope road conditions,the fuzzy algorithm controller is constructed.For the continuous speed reducer,the LQG algorithm controller is constructed.Finally,based on the neural network control algorithm,the local controller and the weighting coefficient switching strategy are constructed.The weighting coefficient sum method is used to identify the road condition according to the real-time monitoring data of the vehicle.According to the different control characteristics of the vehicle response parameters under the three road conditions,the control equation including the weighting coefficient of the switching feature is used as the suspension adaptive switching output to realize smooth switching of three different road conditions and corresponding control.The adaptive suspension method of active suspension based on neural network switching is simulated and verified in Matlab/Simulink,and the control results are compared with passive suspension.The simulation results show that the road recognition method based on modal energy ratio change rate can accurately identify three kinds of pavement conditions: random straight road,slope road and continuous speed reducer.The adaptive control algorithm for active suspension based on neural network switching can get accurate feature weighting coefficients.The research method in this paper achieves smooth switching and improves the ride comfort of the vehicle.