Optimised State-Dependent Sampling Control for Heavy-Haul Trains

Optimised State-Dependent Sampling Control for Heavy-Haul Trains

Breysens, G. (TU Delft Electrical Engineering, Mathematics and Computer Science; TU Delft Mechanical, Maritime and Materials Engineering)

Mazo, M. (mentor)
de Albuquerque Gleizer, G. (graduation committee)
Boskos, D. (graduation committee)

Delft University of Technology

Electrical Engineering | Embedded Systems

2023-05-04

This thesis investigates the potential of state-dependent sampling strategies (SDSS) for the control of heavy-haul trains. Event-triggered control (ETC) is a control approach in which data is only sent when some state-dependent condition, the triggering condition, is satisfied. In this way, the number of communications required to stabilise a system can be drastically reduced. Periodic event-triggered control (PETC) is a variant of ETC in which the triggering condition is checked periodically. By sometimes sampling earlier than a PETC-generated deadline, long-term pay-offs in the average inter-sample times are possible. As searching for formal models of early-triggering controllers quickly becomes computationally infeasible as system dimensions grow, a sample-based approach using reinforcement learning was utilised to find the SDSS controller, which takes the form of a neural network mapping states to the time until the following sample, trained to hopefully yield long-term payoffs in sample efficiency. It was found that, by choosing suitable control parameters, the SDSS controller can outperform the PETC baseline in terms of inter-sample times (IST). Next, a hardware-in-the-loop (HIL) setup was made to evaluate the optimised controller’s performance in a real-time context controlling a non-linear train system subject to noise, disturbances, and tasked with attaining different speed setpoints while minimising the inter-wagon forces. It was found that the optimised controller did not perform well during these scenarios. Since the controller’s robustness was not considered during the training process, performance suffered under these conditions. To improve the robustness of the controller, the system must be trained on realistic (noisy) data. Simulations with more wagons must be performed to assess whether improvements can still be attained when using larger models. Finally, the accuracy of the HIL setup needs to be improved by using an appropriate network stack that would allow each wagon to send its own data and turn off the sensor node radios when otherwise idly listening for incoming data.

freight transport
event-triggered control
reinforcement learning

http://resolver.tudelft.nl/uuid:d1f8515c-b1b8-4d7e-841a-89ac2b80ccc3

Student theses

master thesis

© 2023 G. Breysens