Robust pole assignment by state-derivative feedback using gradient flow approach

This paper presents a gradient flow approach for computing the robust controller for linear systems using state-derivative feedback such that the sensitivity of the closed-loop system eigenvalues to perturbations in the system and gain matrix is minimized. The approach can be applied for any controllable system with some restrictions when assigning zero poles. The nonsingular and singular open-loop state matrices are treated. The Sylvester equation based parameterization of the eigenvalue assignment problem is used to formulate the minimization problem for suitably chosen cost function. So, the available degrees of freedom are utilized to improve robustness of the closed-loop system. Finally, numerical example is introduced to demonstrate the effectiveness of the proposed solution.