A Smart Sugeno Interval Type-2 Fuzzy Bee Colony Optimization to Stable an Autonomous Mobile Robot Controller

In this paper the main objective consists in the implementation of a Sugeno inference system applied to an interval type-2 Fuzzy logic system (SIT2FLS) in the adjustment dynamic of parameters in a Bee Colony Optimization algorithm (BCO) to have a stable trajectory of a line. This paper present two important ideas, the first goal of BCO is found of the optimal design of the Membership Functions (MFs) in a Mamdani Type-1 Fuzzy Logic System (MT1FLS), the second idea was implemented a SIT2FLS to find the optimal values in alpha and beta parameters in the BCO algorithm, which is the originality in this paper, and an analysis of the execution time and the errors obtained by the Mean Square Error (MSE) is presented. The results indicated that the BCO algorithm has better stabilization and excellence results when the executed a SIT2FLS to find the optimal values in \(\beta \) and \(\alpha \) parameters the BCO algorithm is presented. The main conclusions found with the proposal are that convergence is faster in finding minor results as well as the stabilization of the problem studied. In addition, to highlight the efficiency and good results of the SIT2FLS a comparative with a Mamdani IT2FLS (MIT2FLS) is presented.