Intelligent Controller Design for Precise Trajectory Control in Magnetic Levitation Systems - Tien-Loc Le; Minh-Triet Nguyen; Trong-Hien Chiem; Van-Phong Vu; Huu-Hung Nguyen; Xuan Dung Huynh; Duc-Tri Do (Journal of Technical Education Science, 2024)
As a form of soft computing technique, the application of fuzzy controllers for managing uncertain nonlinear systems has garnered significant attention from researchers. Although many fuzzy control methods have been proposed, most of them exhibit obvious limitations in weight learning and optimizing network structure. This paper aims to propose a design of a type-2 fuzzy cerebellar model articulation controller for uncertain nonlinear systems, which achieves high stability and accuracy for controlling magnetic levitation systems. The proposed controller is a combination of a type 2 fuzzy logic system and a cerebellar model articulation controller. A self-organizing algorithm is utilized to automatically construct the network structure. The adaptation laws based on the gradient descent method are derived to online update the network parameters. To ensure system stability, a Lyapunov stability function is employed. Finally, the numerical simulation results on trajectory tracking control of the magnetic levitation systems are given to illustrate the effectiveness and practicability of the proposed control method.