Research On Reproduction Methods Of Road Roughness Using Road Simulator

In recent years, due to the rapid development of Chinese automobile industry, new products pile up one after another and development cycle becomes faster and faster. At the same time, with the people's living standard growing, requirements for automobile quality, security and amenity are also continuously increasing. Road simulation test for components and vehicles in laboratory is an active means to accelerate new models development and to improve the quality of products. While in traditional road simulation test, the load or acceleration from a running vehicle is commonly recurred, thus two problems follow:â‘ It needs precede special data measurement beforehand so that when lack of pertinent data at developing new models, the test is hardly to run.â‘¡The test signals have closely connections with the tested vehicle and the road, so it is difficult to compare the test results with different vehicles. Aiming at these issues, this text researches the methods and processes that are applied to reproducing the most direct road surface input of vehicle-road roughness. The main innovation points are:1) developing a software to reconstruct road roughness with MATLAB, which integrating the current main reconstruction methods; 2) building a nonlinear autoregressive moving average (NARMA) dynamic neural network model for road simulator; 3) by using iterative learning control (ILC), the desired signals are represented successfully in test rigs.This paper looks into the road roughness mathematical model and description method; utilizes regression analysis to find correlation between the different descriptions. Based on the comprehensive analysis of some existing typical methods' features, the software, facilitating practical use to reconstruct road unevenness, is developed.For a 4-poster road simulator is a very complex system, it is difficult to describe it by the general nonlinear model. Thus the author presents a NARMA dynamic neural network model to identify the system. Because of this nonlinear test bed model which is built through test data, the results show high degree of precisionThis paper researches the iterative method of the traditional remote parameter control (RPC) and combining physical characteristics of road roughness, it is successfully used to reproduce this unevenness signal. Dissert on prime identification method of frequency response function, estimation method of coherence function and iteration process, etc. By applying these to single input-single output and multiple input-multiple output system to recur road roughness, the tests achieve preferable results. After realizing the above controlling means, against the insufficiency existing in recurring road unevenness using RPC, this article further presents a method that uses ILC and the prior knowledge of test bed directly devise a smooth filter based ILC controller in time domain, while without identifying system FRF. Compared with RPC, this technique is easy to implement, computing speed fast, and the test person need not intervene the iteration process. Devising iterative learning law is the key to ILC, for further study on it, this article adopts simulation technique. With having been built non-linear model as the controlled object, a P-type open-loop iterative learning law has been designed, however, its convergence rate is too slow to use in practical application. To increase the convergence rate, a discrete PID controller based open-closed loop ILC controller has been presented and the simulation proves the designed controller comes to the expected control purpose.