Road simulation experiment in laboratory is a most important method to enhance the design quality of vehicle products. Presently, two main control techniques for road simulation—remote parameter control (RPC) and minimum variance adaptive control—are both defective: the former becomes an open-loop control after generating the drive signals, however the latter is essentially a kind of gradual control. To realize the closed-loop control and increase the control quality, this article brings forward a PID open-closed loop control method. Firstly taking the original road simulator as a group to identify, a nonlinear autoregressive moving average (NARMA) model was built with the dynamic neural network. Subsequently, this plant model was used to build the open-closed loop control system mentioned above. In the closed-loop a discrete PID controller was introduced to stabilize the system, while a P-type iterative learning control (ILC) was adopted to increase the control quality. Simulation results show that by using open-closed loop ILC, system convergence rate is fast, so this method can be applied to physical system.


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    Title :

    Research on Road Simulator with Iterative Learning Control


    Additional title:

    Sae Technical Papers


    Contributors:
    Wu, Baoyu (author) / Wang, Bin (author) / Tan, Gangfeng (author) / Guo, Xuexun (author) / Xu, Zhan (author)

    Conference:

    SAE 2009 Commercial Vehicle Engineering Congress & Exhibition ; 2009



    Publication date :

    2009-10-06




    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English




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