Genetic Fuzzy Control for Path-Tracking of an Autonomous Robotic Bicycle

Abstract

Due to its non-holonomic constraints and a highly unstable nature, the autonomous bicycle is difficult to be controlled for tracking a target path while retaining its balance. As a result of the non-holonomic constraint conditions, the instantaneous velocity of the vehicle is limited to certain directions. Constraints of this kind occur under the no-slip condition. In this study, the problem of optimization of fuzzy logic controllers (FLCs) for path-tracking of an autonomous robotic bicycle using genetic algorithm (GA) is focused. In order to implement path-tracking algorithm, strategies for balancing and tracking a given roll-angle are also addressed. The proposed strategy optimizes FLCs by keeping the rule-table fixed and tuning their membership functions by introducing the scaling factors (SFs) and deforming coefficients (DCs). The numerical simualtions prove the effectiveness of the proposed structure of the genetic fuzzy controller for the developed bicycle system.