Continual Uncertainty Learning for Robust Control of Nonlinear Systems with Multiple Heterogeneous Uncertainties
Read the original on arXiv AI →The paper introduces Continual Uncertainty Learning (CUL), a curriculum-based continual learning framework that decomposes robust control of nonlinear systems with multiple heterogeneous uncertainties into a sequence of tasks. By progressively expanding and diversifying plant uncertainties and applying memory-efficient anti-forgetting regularization, CUL enables a policy to acquire strategies for each uncertainty sequentially while a model-based controller provides a shared baseline performance. Applied to an active vibration controller for automotive powertrains, the approach demonstrates robustness to structural nonlinearities and dynamic variations, improving control performance and sample efficiency.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.