Decoupled Adaptive Neuro-Fuzzy Sliding Mode Control Applied in a 3D Crane System

Abstract

This paper presents the control of 3D crane system by using a decoupled adaptive neuro-fuzzy controller based on the sliding mode theory. The considered 3D crane involves a planar motion in conjunction with a hoisting motion. The control inputs are three (trolley and hoisting forces), whereas the variables to be controlled are five (the trolley position in the XOY plane, the length of the lifting cable, and the two angles of swing). The interactions between each control subsystem are not taken account explicitly, but are considered to be disturbances in control of each individual subsystem. In the proposed approach, a conventional controller (PD) is used in parallel with the neuro-fuzzy controller, the PD controller ensures the asymptotic stability in compact space, the parameter update rules of the fuzzy neural network are derived, and the proof of the online learning algorithm is verified by using the Lyapunov stability method. Experimental results are given to solve the crane position control problem of 3D crane system laboratory equipment.