Anti-swing control of the overhead crane system based on the harmony search radial basis function neural network algorithm

Abstract

The swing of the grab is a main factor affecting the working efficiency of overhead cranes. Thus, planning the optimal motion path can reduce the adverse effects caused by the grab swing and improve the loading and unloading efficiency. The dynamic model of the trolley–grab system is established by considering factors like the change of rope length, wind load, and air resistance. First, the radial basis function neural network is applied to generate a feasible motion trajectory of the crane trolley. Taking the swing angle and angular velocity of the grab at the discharge point as evaluation, the harmony search algorithm is then applied to optimize the neural network parameters and obtain the optimal anti-swing motion trajectory. The numerical simulation and practical testing results show that the harmony search–radial basis function algorithm generates a smooth motion trajectory with good convergence, achieving anti-swing control of the trolley–grab system.