Transfer learning for fuzzy control of two-mass drive system using state variables feedback

The aim of this paper is to present a novel approach to the fuzzy controller tuning process. The mentioned approach is somehow like transfer learning know in Artificial Intelligence (AI) systems; it is based on the idea that some prior knowledge about the controlled system is usually in the possession of an engineer. General idea is to take a known control function for a simplified but similar control system and use it as a base for a more complex system, as it would better start than usual random weights and coefficients. In this study, a classical PI controller with different feedbacks for a two-mass drive system is used as the source for the fuzzy controller with analogous feedback. By showing that regarding the mathematical complexity of finite bit precision computations, these planes are identical it would be proven that the fuzzy system is not worse than the classical and would be a great base for future improvements such as adaptivity. After the introduction, the MATLAB-Simulink model of the two-mass drive system is described. Then the classical fuzzy controller and the algorithm for control function reproduction of classical to the fuzzy controller is presented. The paper ends with a summary.