Granular evolving fuzzy robust feedback linearization

Abstract

Exact feedback linearization is a powerful control approach, but has poor robustness properties. Lack of robustness yields inadequate performance and in practice may induce instability. This paper addresses an approach to improve the robustness of feedback linearized systems using a model reference adaptive control mechanism with an evolving participatory learning procedure. The granular evolving fuzzy robust feedback linearization approach is a way to robustly and efficiently control unknown nonlinear systems around given operating points. The result is a robust closed-loop control approach in which participatory learning is employed to estimate unknown nonlinearities online to cancel their effects in the feedback linearized system. A simulation example using a surge tank, a widely studied benchmark in the literature, shows that the performance of the granular evolving robust feedback linearization is higher than classic feedback linearization, fuzzy model reference, and indirect adaptive fuzzy control approaches.