Learning Based Target Following Control for Underwater Vehicles

Abstract

Target following of underwater vehicles has attracted increasingly attentions on their potential applications in oceanic resources exploration and engineering development. However, underwater vehicles confront with more complicated and extensive difficulties in target following than those on the land. This study proposes a novel learning based target following control approach through the integration of type-II fuzzy system and support vector machine (SVM). The type-II fuzzy system allows researchers to model and minimize the effects of uncertainties of changing environment in the rule-based systems. In order to improve the vehicle capacity of self-learning, an SVM based learning approach has been developed. Through genetic algorithm generating and mutating fuzzy rules candidate, SVM learning and optimization, one can obtain optimized fuzzy rules. Tank experiments have been performed to verify the proposed controller.