Transferability of Multi-Objective Neuro-Fuzzy Motion Controllers: Towards Cautious and Courageous Motion Behaviors in Rugged Terrains

Adham Salih, Joseph Gabbay and Amiram Moshaiov ()
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Adham Salih: Mechanical Engineering Department, Braude College of Engineering, Karmiel 2161002, Israel
Joseph Gabbay: School of Mechanical Engineering, Tel-Aviv University, Tel-Aviv 6997801, Israel
Amiram Moshaiov: School of Mechanical Engineering, Tel-Aviv University, Tel-Aviv 6997801, Israel

Mathematics, 2024, vol. 12, issue 7, 1-19

Abstract: This study is motivated by the need to develop generic neuro-fuzzy motion controllers for autonomous vehicles that may traverse rugged terrains. Three types of target problems are investigated. These problems differ in terms of the expected motion behavior, including cautious, intermediate, and courageous behaviors. The target problems are defined as evolutionary multi-objective problems aiming to evolve near optimal neuro-fuzzy controllers that can operate in a variety of scenarios. To enhance the evolution, sequential transfer optimization is considered, where each of the source problems is defined and solved as a bi-objective problem. The performed experimental study demonstrates the ability of the proposed search approach to find neuro-fuzzy controllers that produce the required motion behaviors when operating in various environments with different motion difficulties. Moreover, the results of this study substantiate the hypothesis that solutions with performances near the edges of the obtained approximated bi-objective Pareto fronts of the source problems provide better transferability as compared with those that are associated with performances near the center of the obtained fronts.

Keywords: multi-objective optimization; Pareto-optimization; fuzzy-inference system; evolutionary transfer optimization; fuzzy control; non-specialized controllers (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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