5 accepted papers
This paper presents a data-driven control optimization framework for flexible joint robots (FJR) based on frequency response function (FRF) data, enabling automated controller synthesis without explicit model identification. Unlike conventional model-based approaches that rely on accurate parameter
This paper presents a data-driven control optimization framework for flexible joint robots (FJR) based on frequency response function (FRF) data, enabling automated controller synthesis without explicit model identification. Unlike conventional model-based approaches that rely on accurate parameter …
This paper presents a robust control algorithm for precise orientation control of robot manipulators using a disturbance observer (DOB) specifically designed for orientation dynamics. Our approach addresses the challenges of 3D orientation control by incorporating various orientation representations
This paper presents a novel, nonlinearity robust identification method for deriving the inertia matrix of multi-DOF Flexible Joint Robots (FJR), utilizing resonance and anti-resonance frequencies in the Frequency Response Functions (FRF). Our proposed method overcomes the limitations of conventional…
Detecting anomalies in real-world multivariate time series data is challenging due to complex temporal dependencies and inter-variable correlations. Recently, reconstruction-based deep models have been widely used to solve the problem. However, these methods still suffer from an over-generalization…