Backward control of multitrailer systems using neurocontrollers evolved by a genetic algorithm

Abstract

We propose a design method for neurocontrollers (NCs) evolved by a genetic algorithm (GA) for the control of the backward movement of multitrailer truck systems. The difficulty of controlling backward movement increases with the number of connected trailers. In order to search for the best NCs for 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, the initial NCs are first trained for a two-trailer connected truck system, then 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 to NCs which can successfully control multitrailer systems. Simulation results show that the step-up training method is useful for multitrailer systems.