Study on suppression of load swing with gust for rotary crane

In this paper we propose a neural network as a controller (NC) for suppression of load swing with disturbance like a gust wind in a rotary crane system. The rotary crane system has structure which rotates around the vertical axis, resulting in a nonholonomic system. In general, it is difficult to design the classical control method for nonholonomic system using a static continuous state feedback law. So, it is necessary for nonholonomic system to design a time-varying feedback controller or a discontinuous feedback controller. Previous research had been successful in constructing the suppression controller of the load swing by using NC with a single initial rotation angle and three initial rotation angles when a gust occurred. The NC optimized by genetic algorithm (GA) had good control performance, but could not stabilize the load swing for the untrained condition. The purpose of this paper is to improve the performance of the NC optimized by GA when the gust occurred at random.