Modeling And Simulation Research On Vehicle Handling Inverse Dynamics

A brief review of the developing history of vehicle handling inverse dynamics is introduced in this paper. Using computer simulation technology and experimentation design method, the inversion solution study of vehicle handling dynamics is carried out, based on the method of computation intelligent algorithms, inverse system theory and virtual prototype technology. After the solution of inverse problems, an optimization approach is proposed for the purpose of improving the vehicle maneuverability, and then the motion stability of vehicle driving is analyzed based on the result of optimization.The main contributions of this work are summarized as follows:1. A method of obtaining the steering wheel angle input and steering moment input is presented for further investigation of the vehicle handling inverse dynamics, according to some vehicle responses. Using Radial Basis Function neural networks, the mapping relationship between yaw velocity, lateral acceleration and steering wheel angle is founded. The inverse solution results showed that the proposed inverse solution method is not only practical, but also with high accuracy, little computation requirement and good stability.2. Based on the characteristic of handling-stability performance and the vehicle configurations, the parameter 3D model of the vehicle is created successfully, which includes suspension, steering, tire etc. According to the mechanism of ADAMS closed-loop control, the steering angles can be identified with the input of road track. The identification shows this method is effectual, and the trustful vehicle model for handling inverse problems is given.3. Based on the two-degree-freedom system of vehicle, the inverse system theory is applied for the vehicle handling inverse dynamics. An inverse system is founded relativing to the primary system and the output of the inverse system is solved. The relationship between lateral acceleration and steering angle can be found in the inverse system, and the inverse solution results shows that this method is not only applicable, but also with high accuracy, little computation requirement.4. The identification results of the three methods, which include computation intelligent algorithms, inverse system theory and virtual prototype technology, is compared with each other and validated by the real experiment. The comparison shows that the steering angles which are solved through the three methods are approximate. The value of simulation is consistent with real experiment.5. Using the method of computation intelligent algorithms, the inversion solution study for the real experiment is carried out. The Radial Basis Function neural networks are established based on the test of vehicle handling and stability. The data of yaw velocity, lateral acceleration are put into the networks, and the identification shows its validity. The precision of identification can be higher, if the experimental data are enough and representative.6. Based on solution of inverse problems, an optimization approach is proposed for the purpose of improving maneuverability of driver-vehicle-road closed-loop systems. The mapping relationship between vehicle lateral displacement and steering wheel angle and other responses can be found utilizing Radial Basis Function neural networks. One prescribed path is taken as input of the trained RBF neural networks, then the steering wheel angle and other vehicle responses can be obtained and the maneuverability index of the closed-loop system can be obtained and optimized. It can be seen that different vehicle configurations, based on the inversion solution study, have the inherent ability to follow the same prescribed path, therefore the optimal vehicle configuration has the best maneuverability among all vehicle configurations when they follow some typical path. After the optimization, the motion stability of vehicle driving on different roads is analyzed based on the modal of vehicle with steering angle input and steering moment input.