Neurocontrollers for Truck Systems with Multiple Trailers Using Step-up Training by Genetic Algorithm

Abstract

In this paper, we propose a design method for neurocontrollers (NCs) evolved by a genetic algorithm (GA) for the backward movement control of multitrailer truck systems. The difficulty of the backward movement control depends on the number of connected trailers. In order to search for the best NCs for the multitrailer systems, we propose a step-up training method. The step-up training sequence is as follows. First, the initial NCs, that are set to random values, are trained for an easy control object. Second, the set of NCs is trained for more difficult control objects. In this study, firstly, the initial NCs are trained for a two-trailer-connected truck system, secondly the NCs are trained for a three-trailer system, and finally the NCs are trained for a four-trailer system. The step-up training method is able to advance NCs which can successfully control multitrailer systems. The simulation results show that the step-up training method is useful for multitrailer systems.