  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.
