Fuzzy controllers: synthesis and equivalences

Robotic machining from programming to process control

“…The inputs are divided in levels in accordance with the observed sensor characteristics and fuzzyfied using triangular membership functions. (Galichet & Foulloy, 1995) The output is fuzzyfied in the same way. The rule base is constructed using a methodology similar to that in the work of (Li, & Gatland, 1996).…”

Stable and robust fuzzy control for uncertain nonlinear systems

“…Still, in both cases, when G j is designed such that H j = A j +BG j = H which is a constant matrix for all j, and the system has no parameter uncertainty (i.e., 1Aj = 1Bj = 1Hj = 0 for all j), a linear closed-loop system can be obtained. If (18) holds, the linear system is obtained by feedback compensation (i.e., pole placement technique); otherwise, it is obtained by feedback linearization with respect to linear sub-systems satisfying (19). The structure of the fuzzy controller for the latter case is more complicated than that of the former case.…”

“…Prior knowledge of the plant is not required. Under a particular design of the membership functions, a fuzzy PID controller is proved to be equivalent to a conventional PID controller [18], [19], [26], or a nonlinear PID controller [7]. Some methodologies on tuning adaptive fuzzy PI, PD, and PID controllers can be found in [9], [27], [31], [77], and [78].…”

A New Methodology for Tuning PID‐Type Fuzzy Logic Controllers Scaling Factors Using Genetic Algorithm of a Discrete‐Time System

“…[2,6] are used in our case of study. Table 1 gives the linguistic levels, assigned to the variables e k , Δe k and Δu k , as follows: NL: negative large; N: negative; ZR: zero; P: positive; PL: positive large.…”