A New MRA and AA based Algorithm for the Higher Order Reduction Uncertain Systems

The MRA and AA algorithms are utilized in this paper to present a novel approach for the reduction of high-order uncertain systems. The development of the program makes use of both of these mathematical approaches. As a result of this creative approach, the Continuous SISO system might be reduced. Measurement mistakes brought on by variations in the environment have the potential to render the system ambiguous, in contrast to other systems that have been reported in the literature. This strategy, in contrast to the methods that were used in the prior iteration literature, has the potential to build a stable reduced order (r-o) model from a stable initial high order uncertain system. By avoiding many of the constraints of interval arithmetic, affine arithmetic is used. One of the problems is that there is not a solution that is unbounded in many of the key scenarios. Through the utilization of typical numerical examples derived from interval literature, the methodology was validated. Among the topics that we investigate are uncertain systems, lower order models, affine arithmetic, and the modified Routh approximation approach.