# Adaptive robust tracking control with active learning for linear systems with ellipsoidal bounded uncertainties

arXiv: https://arxiv.org/abs/2308.03727 · HTML: https://mlsanity.com/paper/2308.03727/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Aug 2023.

## Built on by (most cited first, 2 of 2)

- [Ultra-short-term solar power forecasting by deep learning and data reconstruction](https://mlsanity.com/paper/2509.17095/) (2025)
- [Fine-tuning for Data-enabled Predictive Control of Noisy Systems by Reinforcement Learning](https://mlsanity.com/paper/2505.24572/) (2025)

## Abstract (arXiv)

This paper is concerned with the robust tracking control of linear uncertain systems, whose unknown system parameters and disturbances are bounded within ellipsoidal sets. We propose an adaptive robust control that can actively learn the ellipsoid sets. Particularly, the proposed approach utilizes the recursive set-membership state estimation in learning the ellipsoidal sets, aiming at mitigating uncertainties in the system control. Upon the learned sets representing the recognized uncertainties, we construct a robust control with one-step prediction for system output tracking. In deriving an optimized control law, we reformulate the optimization objective into a second-order cone programming problem that can be solved in a computationally friendly way. To further stimulate the active learning of uncertainties over the control procedures, we enrich the information used for the learning by maximizing the volume of the ellipsoid set, supposed to lead to increased learning accuracy and accelerated uncertainty reduction. To verify our approach, we conduct numerical simulations to compare the fixed-ellipsoidal-set robust control with ours, and investigate the positive effect of the designed active learning in the uncertain system control process.