COMPARATIVE ANALYSIS OF OPTIMAL AND FUZZY LOGIC CONTROLLERS FOR NONLINEAR TECHNOLOGICAL PROCESSES

Abstract

This paper proposes a methodological framework for the comparative evaluation of the dynamic performance of optimal and fuzzy logic controllers applied to nonlinear technological processes. An automatic yarn tension control system used in the warping process is selected as the case study. Variations in the process parameters and external disturbances are considered. A linear quadratic regulator (LQR), designed on the basis of a linearized mathematical model, is compared with a Mamdani-type fuzzy logic controller governed by a set of linguistic rules. The evaluation criteria include settling time, overshoot, root mean square error (RMSE), integral of absolute error (IAE), and sensitivity to parameter variations. The present study establishes the evaluation methodology; conclusions regarding the quantitative superiority of either controller require subsequent simulation and validation.