Adaptive Fuzzy Control Design for Chaotic Permanent Magnet Synchronous Motors

Abstract

In this dissertation adaptive fuzzy control methods are proposed for chaotic permanent magnet synchronous motors (PMSMs) subjected unknown parameters and uncertainties The control objective is to design an adaptive fuzzy controller to suppress chaos in a PMSM and force the motor speed to follow the desired trajectory successfully even with the the existences of the unknown parameters and uncertainties In order to meet the control objective various control methods such as direct adaptive fuzzy control direct adaptive fuzzy control based on sliding mode control and improved adaptive fuzzy control are developed These controller schemes generally have two parts: fuzzy neural and supervisory controllers The fuzzy neural controllers use fuzzy neural networks to online estimate the control laws directly or to online estimate the unknown nonlinear models existing in the mathematical models of PMSMs for constructing the control laws On the other hand the supervisory controllers are employed to reduce the estimation error effects of the neural networks and ensure the robustness of the systems By using Lyapunov synthesis approach the system stability is ensured and the perfect tracking performance with zero convergence of tracking error can be obtained Moreover in the improved adaptive fuzzy control method the designed controller not only meets the control objective but also completely avoids the singularity problem which usually appears in the direct adaptive fuzzy control method based on sliding mode control Finally the numerical simulations are carried out to demonstrate that the proposed control schemes can successfully remove chaotic oscillations and allow the speed to follow the desired trajectory in a chaotic PMSM despite the existences of unknown models and uncertainties

Date of Award	2015 Jan 8
Original language	English
Supervisor	Teh-Lu Liao (Supervisor)