Research on Road Simulator with Iterative Learning Control
2009-01-2908
10/06/2009
- Event
- Content
- 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.
- Pages
- 6
- 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.