Load swing suppression for rotary crane using neuro-controller optimized by genetic algorithm

In this paper, we propose a control method for a rotary crane system using neuro-controller (NC) optimized by real-coded genetic algorithm (GA). The rotary crane is known to be a nonholonomic system. We have been successful to suppress the load swing from an initial rotation angle using NC. However, the trained NC have low control performance with untrained angles. In this study, the evaluation function of GA is improved in order to control the load swing from multiple initial positions. The validity of the proposed NC is verified through computer simulation. Simulation results show that the proposed NC has good control performance and robustness with noise and fluctuation of the initial states.