Enriched global–local multi‑objective optimization scheme for fuzzy logic controller-driven magnetorheological damper-based structural system

Abstract

To construct an optimal and reliable fuzzy logic controller (FLC) for magnetorheological (MR) damper-based structural systems, a reliability-based design optimization (RBDO) problem is formulated considering two competing objectives, namely, minimizing structural responses and control cost. This work incorporates a two-stage enrichment scheme with the non-dominated sorting genetic algorithm (NSGA-II)-based multi-objective optimization method to solve the RBDO problem. In the global stage of the proposed method (denoted as TS-MO method), an initial Kriging surrogate model is constructed in the augmented space and updated by sequentially adding enrichment points within the whole design space to obtain a coarse Kriging surrogate. In the local stage, within the multi-objective optimization iterations, the surrogate model is further refined using enrichment points selected in the vicinity of the current set of design points near the limit state function based on a distance-refined U learning function. Two convergence criteria with dynamic thresholds are employed to ensure the local precision of the trained surrogate model. The efficacy of the TS-MO method is demonstrated by conducting comparison works with two previous methods, i.e., the deterministic design optimization and global Kriging-based multi-objective optimization method (DDO-GK-MO) and the unified method.