Fuzzy Adaptive Speed and Position Control for Permanent Magnet Synchronous Motors

Abstract

The use of knowledge-based linguistic regulators constitutes a powerful tool for controlling complex processes. The synthesis of most of these regulators is primarily based on the experience of the operator or process engineer, on which the regulator's performance strongly depends. Much of the research dedicated to linguistic knowledge-based regulators has focused on developing specialized regulators for specific applications. These studies do not provide a synthesis methodology enabling a general analysis of control schemes' performance, particularly their stability. Studies on fuzzy systems have shown that certain classes possess the quality of being universal function approximations. This significant property has opened new avenues for the application of fuzzy systems in control. Consequently, many research efforts have been directed toward combining fuzzy systems with control techniques such as adaptive control. In these control schemes, the fuzzy system serves to approximate nonlinear functions. In recent years, fundamental contributions in adaptive control—both theoretical and practical—have provided essential insights for a better understanding of adaptive systems. The primary objective of adaptive control is the synthesis of adaptation laws to automatically adjust loop regulators, ensuring that a desired performance level is achieved or maintained despite unknown, poorly known, or time-varying process parameters.Simulations of MATLAB/SIMULINK environment of the present work shows the efficacy.