GB
G. Breysens
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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.
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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.
Bachelor thesis
(2019)
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Marjolein Rebers, G. Breysens, Henk van Zeijl, Brahim el Mansouri, Jaap Hoekstra, Bori Hunyadi, Anton Montagne
The goal of this bachelor thesis is to develop a control system that controls the temperature of a microthruster. This system also needs to acquire data for research. The microthruster contains a resistor that is used both as a heater and as a sensor. To facilitate the acquisition of data and the testing of the control system, a lab setup with a Keithley 2450 SourceMeter power supply was used. LabVIEW was used to control the power supply and to execute the control algorithms. The final system consists of a microcontroller that runs the control algorithms based on proportional-integral-derivative (PID) control developed in this thesis. The PID values can be adapted with use of the graphical user interface (GUI). A read-out circuit and current supply will be part of the integrated system. These circuits will be developed by other groups that are part of this bachelor project.
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The goal of this bachelor thesis is to develop a control system that controls the temperature of a microthruster. This system also needs to acquire data for research. The microthruster contains a resistor that is used both as a heater and as a sensor. To facilitate the acquisition of data and the testing of the control system, a lab setup with a Keithley 2450 SourceMeter power supply was used. LabVIEW was used to control the power supply and to execute the control algorithms. The final system consists of a microcontroller that runs the control algorithms based on proportional-integral-derivative (PID) control developed in this thesis. The PID values can be adapted with use of the graphical user interface (GUI). A read-out circuit and current supply will be part of the integrated system. These circuits will be developed by other groups that are part of this bachelor project.