Research on rotary crane control using a neural network optimized by an improved bat algorithm

Abstract

In this paper, we propose a three-layered neural network controller (NC) optimized using an improved bat algorithm (BA) for a rotary crane system. In our previous study, the simulation results showed that an NC optimized using the original BA exhibits good control and evolutionary performance. However, the simulation execution time was long. Therefore, to address this problem, we propose an improved BA that reduces the execution time. We show that the NC optimized by the improved BA exhibits the same control performance as that optimized via conventional methods. It is also shown that the time for evolutionary calculations can be reduced.