scholarly journals Evolutionary Algorithms in Stabilization of Inverted Pendulum

2020 ◽  
Vol 8 (6) ◽  
pp. 1447-1453
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Fuzzy Logic ◽  
Evolutionary Algorithms ◽  
Inverted Pendulum ◽  
Fuzzy Logic Controller ◽  
Test Bench ◽  
Design Parameters ◽  
Characteristic Parameters ◽  
Linkage Learning

A novel optimization algorithm of fuzzy logic controller (FLC) using genetic algorithms is used to characterize the major design parameters of an FLC known as characteristic parameters. The characteristic parameters simplify the design of FLC which are encoded into a chromosome as an integer string. These are optimized by maximizing the evaluated fitness through genetic operations to achieve the optimized FLC .An effective Genetic Algorithm (GA) is proposed using linkage learning, or building block identification. The genes arranged to have a fitness enhancement is the essence of linkage learning. A perfect and faster extended GA is suggested using an effective method to learn distributions and then by linking them. Stabilization of Inverted pendulum pole angle is taken as test bench.

scholarly journals Genetic Algorithm Tuned Fuzzy Logic Controller for Rotary Inverted Pendulum

2013 ◽  
Vol 6 (5) ◽  
pp. 907-913
Author(s):  
Tzu-Chun Kuo ◽  
Ying-Jeh Huang ◽  
Ping-Chou Wu
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Logic ◽  
Inverted Pendulum ◽  
Fuzzy Logic Controller ◽  
Rotary Inverted Pendulum

scholarly journals Social spider optimization algorithm for tuning parameters in PD-like Interval Type-2 Fuzzy Logic Controller applied to a parallel robot

2021 ◽  
Vol 54 (3-4) ◽  
pp. 303-323
Author(s):  
Amjad J Humaidi ◽  
Huda T Najem ◽  
Ayad Q Al-Dujaili ◽  
Daniel A Pereira ◽  
Ibraheem Kasim Ibraheem ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Trajectory Tracking ◽  
Fuzzy Logic Controller ◽  
Optimization Technique ◽  
Parallel Robot ◽  
Design Parameters ◽  
Social Spider ◽  
Social Spider Optimization ◽  
Interval Type

This paper presents control design based on an Interval Type-2 Fuzzy Logic (IT2FL) for the trajectory tracking of 3-RRR (3-Revolute-Revolute-Revolute) planar parallel robot. The design of Type-1 Fuzzy Logic Controller (T1FLC) is also considered for the purpose of comparison with the IT2FLC in terms of robustness and trajectory tracking characteristics. The scaling factors in the output and input of T1FL and IT2FL controllers play a vital role in improving the performance of the closed-loop system. However, using trial-and-error procedure for tuning these design parameters is exhaustive and hence an optimization technique is applied to achieve their optimal values and to reach an improved performance. In this study, Social Spider Optimization (SSO) algorithm is proposed as a useful tool to tune the parameters of proportional-derivative (PD) versions of both IT2FLC and T1FLC. Two scenarios, based on two square desired trajectories (with and without disturbance), have been tested to evaluate the tracking performance and robustness characteristics of proposed controllers. The effectiveness of controllers have been verified via numerical simulations based on MATLAB/SIMULINK programming software, which showed the superior of IT2FLC in terms of robustness and tracking errors.

Genetic algorithm based fully automated and adaptive fuzzy logic controller

2011 ◽  
Author(s):  
Pintu Chandra Shill ◽  
Kishore Kumar Pal ◽  
Md. Faijul Amin ◽  
Kazuyuki Murase
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Adaptive Fuzzy ◽  
Adaptive Fuzzy Logic

A Fuzzy Logic Controller for Double Inverted Pendulum on a Cart

2021 ◽  
Author(s):  
George S. Maraslidis ◽  
Theodore L. Kottas ◽  
Markos G. Tsipouras ◽  
George F. Fraqulis
Keyword(s):  
Fuzzy Logic ◽  
Inverted Pendulum ◽  
Fuzzy Logic Controller

Provide A Method Based on Genetic Algorithm to Optimize the Fuzzy Logic Controller for the Inverted Pendulum

2021 ◽  
Author(s):  
Shahrooz Alimoradpour ◽  
Mahnaz Rafie ◽  
Bahareh Ahmadzadeh
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy System ◽  
Inverted Pendulum ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Fuzzy Rule ◽  
Rule Base ◽  
Base System ◽  
Control Rules ◽  
Dynamics And Control

Abstract One of the classic systems in dynamics and control is the inverted pendulum, which is known as one of the topics in control engineering due to its properties such as nonlinearity and inherent instability. Different approaches are available to facilitate and automate the design of fuzzy control rules and their associated membership functions. Recently, different approaches have been developed to find the optimal fuzzy rule base system using genetic algorithm. The purpose of the proposed method is to set fuzzy rules and their membership function and the length of the learning process based on the use of a genetic algorithm. The results of the proposed method show that applying the integration of a genetic algorithm along with Mamdani fuzzy system can provide a suitable fuzzy controller to solve the problem of inverse pendulum control. The proposed method shows higher equilibrium speed and equilibrium quality compared to static fuzzy controllers without optimization. Using a fuzzy system in a dynamic inverted pendulum environment has better results compared to definite systems, and in addition, the optimization of the control parameters increases the quality of this model even beyond the simple case.

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