Reliable learning-based controllers and how structured simulation is a path towards them
| Type of publication: | Inproceedings |
| Citation: | |
| Publication status: | Published |
| Booktitle: | Proceedings of 5th International Conference on Advances in Signal Processing and Artificial Intelligence (ASPAI’ 2023) |
| Year: | 2023 |
| Pages: | 268 -- 274 |
| Location: | TENERIFE (CANARY ISLANDS), SPAIN |
| Abstract: | New approaches to control stochastic non-linear time-variant processes include the application of machine learning techniques. One of the problems with learning- based controllers is their reliability in a wide area of process parameters as the controller is trained using a limited set of representative scenarios, either chosen by the designer or taken from historic records. Thus, reliable controller behavior can be guaranteed only in scenarios applied during controller training. Due to the very larger number of random variables and possible scenarios, not all variations can be applied in the controller training process using simulators to guarantee good controller behavior when applied in a real system. One case is traffic control (signal programs, variable speed limit, ramp metering) having large travel patterns variety. The concept of Structured Simulations Framework (SSF) can cover most probable learning scenarios. Thus, applying SSF enables a systematic controller training approach by complementing existing scenarios with synthesized ones that evoke or replicate substantial aspects of real traffic. Such training is necessary to ensure reliable learning-based controllers. This paper discusses the concept of applying SSF to ensure the reliability of learning-based controllers and proposes the application in traffic control for the case of variable speed limits on motorways. |
| Keywords: | Controller reliability , Learning-based controller , structured simulation, Variable speed limit |
| Authors | |
| Added by: | [] |
| Total mark: | 0 |
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