Parameter optimisation of path‐following fuzzy controller for a six‐wheel lunar rover. Issue 1 (1st March 2013)

• Record Type: Journal Article
• Authors: Ge, Pingshu; Guo, Lie; Zhang, Ronghui; Shao, Qiang; Zhao, Xiuchun
• Is Part Of: IET intelligent transport systems. Volume 7:Issue 1(2013)
• Page Start: 1
• Page End: 9
• Publication Date: 2013-03-01
• Subjects: angular velocity -- control system synthesis -- fuzzy control -- genetic algorithms -- mobile robots -- position control -- tracking; parameter optimisation -- path‐following fuzzy controller design -- six‐wheel lunar rover -- genetic algorithm -- quantisation factors -- scale factor -- output variables -- input variables -- path‐following process -- floating‐point encoding scheme -- angular velocity -- crossover and mutation -- path tracking error -- GA; Intelligent transportation systems -- Periodicals; Electronics in transportation -- Periodicals; 388.31205
• DOI: 10.1049/iet-its.2011.0176
• Languages: English
• ISSNs: 1751-956X

Abstract

Abstract : Path following is a main and fundamental task for future lunar rover autonomous navigation. This study adopts genetic algorithm (GA) to optimise the parameters of a path‐following fuzzy controller designed for a six‐wheel lunar rover. Considering the influences of the orientation deviation and its variation rate to the controller performance, two quantisation factors and one scale factor are utilised to limit both the input and output variables. However, it is difficult to manually achieve the proper factors because they are coupling themselves complicatedly. They are adjusted and modified automatically during the path‐following process based on GA to achieve the auto‐tradeoff the parameter and its control performance. Those parameters are coded using floating‐point encoding scheme, respectively. The minimum tracking error and the angular velocity are taken as the target function. After several iterations of crossover and mutation, the best parameters are determined aiming at achieving lower path tracking error with small angular velocity. Simulation on different paths and comparisons with traditional fuzzy controller results show the designed controller with optimised parameters has better performance than the one with manually regulated parameters.