Optimal Fuzzy Controller Design for Autonomous Robot Path Tracking Using Population-Based Metaheuristics

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