Data Dependency of DeePC Performance: Case Study with Metro Trains

Data-enabled Predictive Control (DeePC) allows controlling dynamic systems soley based on its input/output data. This approach is based on behavioral theory, which guarantees precise prediction of the output for given input as long as the collected input data satisfy Persistency of Excitation (PE) condition and the system is linear time invariant. In practice, however, DeePC faces to control nonlinear dynamics and it is necessary to investigate whether there is a preferred way of collecting input and output data for DeePC besides the PE condition. This paper investigate the issue using an Automatic Train Operation (ATO) simulator that represents existing metro train control systems including time delays and nonlinearities. We implement DeePC using two different datasets to control metro train. Comparison and discussion are provided.