Controller Reduction via Weighted Interpolation

The important analytical control designs which are based on the state-space model of the linear time-invariant system yield a controller whose order is almost the same as that of the plant model. If a plant is described by a high-order model, the resulting controller cannot be implemented without reducing its order to a practically acceptable value. This is achieved using weighted model order reduction wherein the weights represent a specific closed-loop performance criterion. In this paper, we present a weighted model order reduction algorithm, which is computationally efficient and ensures less weighted error. The algorithm tends to achieve the weighted-H2 error optimality and guarantee the stability of the reduced-order model, unlike the existing weighted interpolation algorithms. The proposed algorithm is an effective design tool to obtain a lower order controller for large-scale plants in a computationally efficient way. The application of the proposed technique in achieving this objective is also demonstrated on benchmark problems.