We report the experimental results of controlling the pedestal-top electron density by applying resonant magnetic perturbation (RMP) with in-vessel control coils and the main gas puff in the 2024-2025 KSTAR experimental campaign. The density is reconstructed using a parameterized ψ N grid and five channels of line-averaged density measured by the two-colored interferometer (TCI). The reconstruction procedure is accelerated by deploying a multi-layer perceptron to run in approximately 120 µ s and is sufficiently fast for real-time control. A proportional-integral controller was adopted, with the controller gains estimated from the system identification procedure. The experimental results demonstrate that the developed controller can follow a dynamic target while exclusively using both actuators. The absolute percentage errors between the electron density at ψ N = 0.89 and the target were approximately 1.5% median and a 2.5% average, respectively. The developed controller can even lower the density by using the pump-out mechanism under RMP, and it can follow a more dynamic range of density targets than a single actuator controller. The developed controller will enable experimental scenario exploration within a shot by dynamically setting the density target or maintaining a constant electron density within a discharge.
In recent years, autonomous vehicles are rapidly growing as an innovation in modern transportation. However, the dynamic and complex nature of real-world environments can pose a challenge to traditional control algorithms. Direct solution methods are restricted by their assumptions of linearity and/or convexity, and sampling-based methods are restricted by the time to analyze such complex environments. Reinforcement Learning (RL) provides a solution by approximating optimal control strategies without increased computation time, allowing vehicles to traverse complex environments. Despite this, RL is rarely utilized in real-world applications due to the lack of its ability to understand risk, which can pose a threat in safety-critical systems and high-risk environments. In this paper, we utilize a combination of distributional and ensemble RL to provide the agents with an understanding of risk and uncertainty. By modelling the distribution of environment rewards as a Gaussian mixture, we use risk-aware metrics to improve safety and stability. With ensemble methods we can isolate the uncertainty due to limitation in exploration and knowledge, giving agents an estimate of their situational awareness. We can improve an agent's awareness by pushing it to explore areas it is uncertain about more and we can prevent, by simple throttling, agents from taking actions when their uncertainty is too high. Our paper will compare the methods with previous work, modelling the distribution of rewards as a quantile-based discretization, and against a baseline of soft-actor critic in a sample Unmanned Aerial System (UAS) environment.
