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Research on Road Simulator with Iterative Learning Control
Technical Paper
2009-01-2908
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
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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Authors
- Bin Wang - School of Automobile Engineering of Wuhan University of Technology
- Xuexun Guo - School of Automobile Engineering of Wuhan University of Technology
- Zhan Xu - School of Automobile Engineering of Wuhan University of Technology
- Gangfeng Tan - School of Automobile Engineering of Wuhan University of Technology
- Baoyu Wu - School of Automobile Engineering of Wuhan University of Technology
Topic
Citation
Wang, B., Guo, X., Xu, Z., Tan, G. et al., "Research on Road Simulator with Iterative Learning Control," SAE Technical Paper 2009-01-2908, 2009, https://doi.org/10.4271/2009-01-2908.Also In
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