Abstract
This paper analyzes the performance of neural networks for identification and optimal control of active pneumatic suspensions of high speed railway vehicles. It is shown that neural networks can be efficiently trained to identify the dynamics of nonlinear pneumatic suspensions as well as trained to work as optimal nonlinear controllers. The performance of the nonlinear suspension with neuro-controller is compared with the performance of the suspension with LQ controller designed after linearizing the suspension components around the equilibrium point.
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