Comparisons of robust methods on feedback linearization through experimental tests

Abstract

The feedback linearization is a powerfull nonlinear method based on the principle of canceling the nonlinearities of the system model. However, if the model differs from the real system, the feedback linearization is prone to fail. Several studies look to provide robustness to the feedback-linearized system, but we note a lack of evaluation among such approaches under similar conditions in practical systems. This work contributes to filling such a gap by comparing the performance of three recent approaches proposed to robustify feedback linearization loops. Therefore, we design controllers based on the robust multi-inversion (RMI), the robust dynamic inversion (RDI), and the robust granular feedback linearization (RGFL), and evaluate them through real-time experiments. The test process consists of a nonlinear surge tank where the level must be controlled. Two experiments are performed to evaluate the controllers in the tracking and regulation modes when the system is subjected to disturbances. Classical quantitative indexes evaluate the performance of the closed-loop system. The experimental tests indicate that the RGFL controller outperforms the other approaches in both regulation and tracking.