Fuzzy control of an inverted pendulum systems in MATLAB/Simulink

Abstract

This paper presents the effect of the performance tuning a fuzzy controller in terms of the integral of the absolute error value in order to control the position of classical nonlinear problem of inverted pendulum. The methodological development is based on the theory of fuzzy logic controller design considering the expertise acquired on the dynamics of the mechanical system under consideration, thus avoiding the development of rigorous models to represent the dynamics. A set of simulations to evaluate the driver capabilities were developed for the nonlinear system model to different tuning values, which uses 7 membership functions for position and 3 for speed, allowing to determine the sensitivity of the membership functions based on the operational response of this system.

Design of Interval Type-2 Fuzzy Logic Controllers Based on Several Evolutionary Optimization Algorithms for Inverted Pendulum System

“…Proportional Integral Derivative (PID) controller [10],Fuzzy logic inference (FLI) controller [2,8,9], Fractional Order PID (FOPID) controller [7].The fuzzy logic inference are widely used to control an inverted pendulum [8,9]. In [11][12][13] the fuzzy type-1 and fuzzy type-2 controllers combined with PID to product fuzzy like PID controller.…”

Optimum of fractional order fuzzy logic controller with several evolutionary optimization algorithms for inverted pendulum

“…Using Lagrange equations to explain the equations of motion, the researchers of [5,6] gave explicit stages in mathematical modeling to the system. Inverted pendulum control methods and design strategies include the Integer Order Proportional Integral Derivative (IOPID) controller [7], Fuzzy logic controller (FLC) [8][9][10], and Fractional Order PID (FOPID) controller [11]. The fuzzy controllers were integrated with FOPID in [12][13][14] to produce fuzzy like FOPID controllers.…”

Design and Implementation of a Fuzzy Logic Controller for Inverted Pendulum System Based on Evolutionary Optimization Algorithms

“…The researchers of [5,6] provided clear steps in mathematical modeling to the system using Euler-Lagrange's equations to present the equations of motion. There are many different controllers algorithms and design techniques for stabilization of cart position and pendulum angle in inverted pendulum controlling such as Proportional Integral Derivative (PID) controller [10], Fractional Order PID (FOPID) controller [7], Fuzzy logic inference (FLI) controller [2,8,9]. The fuzzy logic inference is widely used to control an inverted pendulum [8,9].…”

“…There are many different controllers algorithms and design techniques for stabilization of cart position and pendulum angle in inverted pendulum controlling such as Proportional Integral Derivative (PID) controller [10], Fractional Order PID (FOPID) controller [7], Fuzzy logic inference (FLI) controller [2,8,9]. The fuzzy logic inference is widely used to control an inverted pendulum [8,9]. In [11][12][13], fuzzy type one and fuzzy type two controllers combined with PID to product fuzzy like PID controller.…”