Position and Swing Angle Control of Nonlinear Gantry Crane System

Crane systems are widely used in logistics due to their efficiency of transportation. The major control problem of gantry crane system is some oscillations while crying load to the desired location. This work developed Radial Basis Function Neural Network (RBFNN) supervised PID controller for position and swing (swing angle suppression) angle of crane system. The supervising RBFNN for position control has two inputs namely instantaneous values of position controller and crane position; while the supervising RBFNN for swing angle control is based on angular acceleration of the swing and anti-sway controller output. The simulation result showed that the proposed control is more robust to the testing conditions in terms of tracking position, swing angle suppression as compared to conventional PID and LQR controllers. Although LQR controller takes less time to settle to its final value for swing angle control under all testing conditions.