劣性遺伝構造を有するニュｰロ制御器を用いた段階的GA学習法による多重トレｰラの制御系設計法

概要

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In a previous study, we proposed a step-up training method for the multitrailer truck control system using neurocontrollers (NCs) evolved by a genetic algorithm (GA) and showed its efficiency. However, the method does not enable the training of NCs for a five-trailer connected truck system. In this paper, we present a new version of the step-up training method that enables the training of NCs for a five-trailer connected truck system. The proposed method is as follows: First, NCs are trained only to avoid the “jackknife phenomenon". Second, NCs are trained for minimizing squared errors starting from easy initial configurations. Finally, NCs are trained for minimizing the squared errors starting from more difficult initial configurations. The difficulty of training steps increases gradually. To improve training performance, we applied a recessive gene model to network weight coding and genetic operations. In this study, we applied the recessive gene model to the classic exclusive-or (XOR) training problem and showed its convergence performance. The GA training of NCs with the recessive gene model maintains diversity in the population and avoids evolutionary stagnation. Simulation shows that NCs with the recessive gene model and the proposed step-up training method are useful in the controller design of the multitrailer system.
2005-01-01