A Feasible Genetic Optimization Strategy for Parametric Interval Type-2 Fuzzy Logic Systems

Optimal Position Fuzzy Control of an Underactuated Robotic Finger

“…In [37] a method for reducing the number of parameters for a fuzzy set control through the use of symmetrical discourses was proposed. In basic terms, these variables can distribute several sets to the positive part of the discourse universe, and then re ect them all in the negative part.…”

“…In the methodology proposed in [37], GA was used as an optimization method, while in this paper the auto adaptive differential evolution proposed by [40] is used. is is because it tends to converge to the solution in fewer iterations (ten), and does not depend on the number of the population as with GA. A description of the algorithm is presented in the next section.…”

“…e fuzzy system has three variables, error (e) and error change (c) are the inputs, and voltage (v) is the system output. Moreover, the variables have their own discourse universe range (Table 2) [37]. e con guration of the fuzzy system is established in 9 rules as shown in Table 3.…”

“…Motivated by the limitations of GA and other optimization methods, this paper presents the implementation of an auto adaptive differential evolution algorithm (AADE) [37] for optimizing the parameters of a fuzzy logic controller (FLC) for an underactuated robotic finger in order to track the trajectory that represents the flexion-extension finger movements. is optimization algorithm does not require gradient information, which means that the optimization problem does not need to be differentiable.…”