This researchwas funded by projects TecNM-5654.19-P and DemocratAI PID2020-115570GB-C22.In this work, we propose, through the use of population-based metaheuristics, an optimization
method that solves the problem of autonomous path tracking using a rear-wheel fuzzy logic
controller. This approach enables the design of controllers using rules that are linguistically familiar to
human users. Moreover, a new technique that uses three different paths to validate the performance
of each candidate configuration is presented. We extend on our previous work by adding two more
membership functions to the previous fuzzy model, intending to have a finer-grained adjustment.
We tuned the controller using several well-known metaheuristic methods, Genetic Algorithms (GA),
Particle Swarm Optimization (PSO), GreyWolf Optimizer (GWO), Harmony Search (HS), and the
recent Aquila Optimizer (AO) and Arithmetic Optimization Algorithms. Experiments validate that,
compared to published results, the proposed fuzzy controllers have better RMSE-measured performance.
Nevertheless, experiments also highlight problems with the common practice of evaluating
the performance of fuzzy controllers with a single problem case and performance metric, resulting in
controllers that tend to be overtrained.TecNM-5654.19-PDemocratAI PID2020-115570GB-C2
