Dynamic Petri Fuzzy Cerebellar Model Articulation Controller Design for a Magnetic Levitation System and a Two-Axis Linear Piezoelectric Ceramic Motor Drive System

This brief aims to propose a more efficient control algorithm for a magnetic levitation system (MLS) and a two-axis linear piezoelectric ceramic motor (LPCM) drive system. A dynamic Petri fuzzy cerebellar (DPFC) model articulation controller is developed to allow control of the position of the metallic sphere of an MLS and the trajectory tracking of a two-axis LPCM drive system. In the DPFC, the concept of a Petri net and a fuzzy frame are incorporated into a cerebellar model articulation controller (CMAC), which alleviates the computational burden that accompanies the learning of parameters and enhances the fuzzy reasoning inference of CMACs. The supervised gradient-descent method is used to develop the online-training algorithm for the DPFC. To guarantee the convergence of trajectory tracking errors, analytical methods that use a discrete-type Lyapunov function are proposed for the DPFC. The experimental results demonstrate the effectiveness of the proposed DPFC for different experimental systems.