# Clustering Techniques for Stable Linear Dynamical Systems with applications to Hard Disk Drives

arXiv: https://arxiv.org/abs/2311.10322 · HTML: https://mlsanity.com/paper/2311.10322/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 26 references that are in the map.
- First posted on arXiv 17 Nov 2023.

## Builds on (most cited first, 4 of 4)

- [A Data-driven Approach to Robust Control of Multivariable Systems by Convex Optimization](https://mlsanity.com/paper/1610.08776/) (2016)
- [System Identification in Multi-Actuator Hard Disk Drives with Colored Noises using Observer/Kalman Filter Identification (OKID) Framework](https://mlsanity.com/paper/2109.12460/) (2021)
- [Data-Driven Strictly Positive Real System Identification with prior System Knowledge](https://mlsanity.com/paper/2110.05672/) (2021)
- [Data-Driven Track Following Control for Dual Stage-Actuator Hard Disk Drives](https://mlsanity.com/paper/2304.00720/) (2023)

## Abstract (arXiv)

In Robust Control and Data Driven Robust Control design methodologies, multiple plant transfer functions or a family of transfer functions are considered and a common controller is designed such that all the plants that fall into this family are stabilized. Though the plants are stabilized, the controller might be sub-optimal for each of the plants when the variations in the plants are large. This paper presents a way of clustering stable linear dynamical systems for the design of robust controllers within each of the clusters such that the controllers are optimal for each of the clusters. First a k-medoids algorithm for hard clustering will be presented for stable Linear Time Invariant (LTI) systems and then a Gaussian Mixture Models (GMM) clustering for a special class of LTI systems, common for Hard Disk Drive plants, will be presented.