CusADi: A GPU Parallelization Framework for Symbolic Expressions and Optimal Control

The parallelism afforded by GPUs presents significant advantages in training controllers through reinforcement learning (RL). However, integrating model-based optimization into this process remains challenging due to the complexity of formulating and solving optimization problems across thousands of instances. In this work, we present CusADi, an extension of the CasADi symbolic framework to support the parallelization of arbitrary closed-form expressions on GPUs with CUDA. We also formulate a closed-form approximation for solving general optimal control problems, enabling large-scale parallelization and evaluation of MPC controllers. Our results show a ten-fold speedup relative to similar MPC implementation on the CPU, and we demonstrate the use of CusADi for various applications, including parallel simulation, parameter sweeps, and policy training.RAL 2024 submissio

Photo-induced charge carrier dynamics in a semiconductor-based ion trap investigated via motion-sensitive qubit transitions

Ion trap systems built upon microfabricated chips have emerged as a promising platform for quantum computing to achieve reproducible and scalable structures. However, photo-induced charging of materials in such chips can generate undesired stray electric fields that disrupt the quantum state of the ion, limiting high-fidelity quantum control essential for practical quantum computing. While crude understanding of the phenomena has been gained heuristically over the past years, explanations for the microscopic mechanism of photo-generated charge carrier dynamics remains largely elusive. Here, we present a photo-induced charging model for semiconductors, whose verification is enabled by a systematic interaction between trapped ions and photo-induced stray fields from exposed silicon surfaces in our chip. We use motion-sensitive qubit transitions to directly characterize the stray field and analyze its effect on the quantum dynamics of the trapped ion. In contrast to incoherent errors arising from the thermal motion of the ion, coherent errors are induced by the stray field, whose effect is significantly imprinted during the quantum control of the ion. These errors are investigated in depth and methods to mitigate them are discussed. Finally, we extend the implications of our study to other photo-induced charging mechanisms prevalent in ion traps.Comment: 27 pages, 11 figure

Pattern graph tracking-based stock price prediction using big data

Stock price forecasting is the most difficult field owing to irregularities. However, because stock prices sometimes show similar patterns and are determined by a variety of factors, we propose determining similar patterns in historical stock data to achieve daily stock prices with high prediction accuracy and potential rules for selecting the main factors that significantly affect the price, while simultaneously considering all factors. This study is intended at suggesting a new complex methodology that finds the optimal historical dataset with similar patterns according to various algorithms for each stock item and provides a more accurate prediction of daily stock price. First, we use a Dynamic Time Warping algorithm to find patterns with the most similar situation adjacent to a current pattern. Second, we select the determinants most affected by the stock price using feature selection based on Stepwise Regression Analysis. Moreover, we generate an artificial neural network model with selected features as training data for predicting the best stock price. Finally, we use Jaro–Winkler distance with Symbolic Aggregate approXimation (SAX) as a prediction accuracy measure to verify the accuracy of our model

Advanced Backstepping Trajectory Control for Skid-Steered Duct-Cleaning Mobile Platforms

In recent years, a novel skid-steered duct-cleaning mobile platform was developed to remove dust accumulated on the inner surface of an air-ventilation duct with its rolling brushes. During the cleaning process, the irregular brushing pressure acting on the upper arm makes it difficult to control the platform through the duct path. In fact, the repulsive external force due to the brushing pressure is not directly measurable or computable because of the nonlinear deformation of the brush. In addition, dynamic uncertainties in platform motion can occur during reciprocating motion of the upper arm. Therefore, a model-based trajectory-tracking controller is required to control the mobile cleaning platform by considering irregular external forces. The robustness of the developed controller based on the adaptable PD(Proportional-Derivative)-backstepping method has been proposed and evaluated through numerical analysis and experiments. For the turning motion in a narrow space, a skid-steered platform model considering wheel slippage has been also implemented. The result shows that tracking control can be successfully achieved under various conditions of frequencies in brushing-arm motion and torque limitation of the traction motors