Abstract
This paper presents three configurations for evolving the membership functions’ parameters for a path-following fuzzy controller. Designers of fuzzy controllers have many options when selecting which parameters and how the optimization algorithm adjusts them. The experiments obtained excellent results with a relatively simple controller using three membership for each of the variables. We tested the proposed controller by a robot simulation that follows a path using a rear-wheel tracking technique. We optimized the controller’s parameters using a genetic algorithm that consisted of a population of chromosomes containing the parameters of a controller. Each individual’s fitness in the populations was determined by calculating the root-mean-square error resulting from each simulation. We have observed, changes in the selection of parameters that parameters to optimize, we tested four, eight, and 18, affect the results obtained and are essential to minimize the total time of evaluations and a minimum possible error. We also tested two ranges for the random values used for initializing the population and verify that this selection also impacts the algorithms’ performance.
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Mancilla, A., Castillo, O., Valdez, M.G. (2022). Evolutionary Approach to the Optimal Design of Fuzzy Controllers for Trajectory Tracking. In: Kahraman, C., Cebi, S., Cevik Onar, S., Oztaysi, B., Tolga, A.C., Sari, I.U. (eds) Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation. INFUS 2021. Lecture Notes in Networks and Systems, vol 307. Springer, Cham. https://doi.org/10.1007/978-3-030-85626-7_54
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