2023 Volume 59 Issue 5 Pages 252-258
Nonlienar compensator design is an important issue that affects performance of control systems as a whole. Despite its importance, the actual nonlinearity we encounter differs from case to case. Thus systematic design and adjustment of nonlinear compensator is still not easy, and often depend on individual know-hows.
In this paper, we focus on a nonlinear compensator called Data-driven Feedback Modulator (DDFBM), and propose a method to adjust its parameters using the system's data under operation. The DDFBM is supposed to be inserted between the pre-designed feedback controller and the plant to be controlled, which enables us to separate the nonlinear compensator design from the feedback controller design. As for the data-driven adjustment of the DDFBM parameters, we use Bayesian optimization, and show that it is effective against plant parameter perturbations.
