A New Meta-Heuristics of Optimization with Dynamic Adaptation of Parameters Using Type-2 Fuzzy Logic for Trajectory Control of a Mobile Robot

Abstract

Fuzzy logic is a soft computing technique that has been very successful in recent years when it is used as a complement to improve meta-heuristic optimization. In this paper, we present a new variant of the bio-inspired optimization algorithm based on the self-defense mechanisms of plants in the nature. The optimization algorithm proposed in this work is based on the predator-prey model originally presented by Lotka and Volterra, where two populations interact with each other and the objective is to maintain a balance. The system of predator-prey equations use four variables (α, β, λ, δ) and the values of these variables are very important since they are in charge of maintaining a balance between the pair of equations. In this work, we propose the use of Type-2 fuzzy logic for the dynamic adaptation of the variables of the system. This time a fuzzy controller is in charge of finding the optimal values for the model variables, the use of this technique will allow the algorithm to have a higher performance and accuracy in the exploration of the values.

Identification of Dynamics of Movement of the Differential Mobile Robotic Platform Controlled by Fuzzy Controller

“…Such a procedure could also be applied to other kinematic structures of robots. It is possible to use such model for testing the control algorithms based on the soft-computing methods, including fuzzy logic [13,25]. In this case, a regulator with 49 interference rules and triangular membership functions was used, and the motor speeds of the mobile robot EN 20 have been determined.…”

“…In robotics it is mainly about the design of controllers designed to control the mobile robot that is to reach the target position and it is required to monitor the robot's declination from the trajectory [12]. Fuzzy logic can be used for the dynamic adaptation of the variables of the system [13,14]. In this study, we are trying we are trying to target the potential of recognizing a dynamic system.…”

Optimization of Fuzzy Controller Using Galactic Swarm Optimization with Type-2 Fuzzy Dynamic Parameter Adjustment

“…The fuzzy system of the mobile robot controller consists of two input variables and two output variables; the first input variable is ev (error in linear velocity), the second input variable is ew (error in angular velocity), and both input variables have two trapezoidal membership functions at the ends and a triangle at the center labeled N (negative), Z (zero) and P (positive) [1,38].…”

“…in angular velocity), and both input variables have two trapezoidal membership functions at the ends and a triangle at the center labeled N (negative), Z (zero) and P (positive) [1,38].…”

“…The main goal of the optimization methods is to find the best solution of a set of possible solutions to a problem; for some cases, the search space is very extensive, which leads to a computational cost that is too high. To solve this problem there are different computational intelligence techniques that provide tools to solve these optimization problems [1].…”