Convergence Analysis of Natural Power Method and Its Applications to Control
This work provides a theoretical foundation and practical applications for control engineers seeking to reduce model complexity and synthesize low-rank controllers for both LTI and LTV systems.
This paper analyzes the discrete-time natural power method, proving its convergence to the dominant r-dimensional subspace corresponding to the r eigenvalues with the largest absolute values. This property is then leveraged to develop methods for model order reduction and low-rank controller synthesis for discrete-time LTI systems, and extended to slowly-varying LTV systems for tracking time-varying dominant subspaces.
This paper analyzes the discrete-time natural power method, demonstrating its convergence to the dominant $r$-dimensional subspace corresponding to the $r$ eigenvalues with the largest absolute values. This contrasts with the Oja flow, which targets eigenvalues with the largest real parts. We leverage this property to develop methods for model order reduction and low-rank controller synthesis for discrete-time LTI systems, proving preservation of key system properties. We also extend the low-rank control framework to slowly-varying LTV systems, showing its utility for tracking time-varying dominant subspaces.