A new approach to model reduction of nonlinear control systems using smooth orthogonal decomposition

Abstract

A new approach to model order reduction of nonlinear control systems is aimed at developing persistent reduced order models (ROMs) that are robust to the changes in system's energy level.A multivariate analysis method called smooth orthogonal decomposition (SOD) is used to identify the dynamically relevant modal structures of the control system. The identified SOD subspaces are used to develop persistent ROMs. Performance of the resultant SOD-based ROM is compared with proper orthogonal decomposition (POD)-based ROM by evaluating their robustness to the changes in system's energy level. Results show that SOD-based ROMs are valid for a relatively wider range of the nonlinear control system's energy when compared with POD-based models.In additions, the SOD-based ROMs show considerably faster computation times compared to the POD-based ROMs of same order. For the considered dynamic system SOD provides more effective reduction in dimension and complexity compared to POD.

Model Order Diminution of Discrete Interval Systems Using Kharitonov Polynomials

“…In some articles [1], [22]- [24], relevant works on DIS are presented by exploiting VOLUME X, XXXX different order diminution methods. Along with these available MOD techniques in literature, Kharitonov polynomials technique of MOD is also available and implemented in several areas, related to control applications [25]- [27], microgrid systems [28], [29], power systems [30], [31], etc. This technique is oriented towards the solutions which are relevant to expletive of dimensionality in HOSs.…”