Design and Implementation of an Optimal Fuzzy Logic Controller Using Genetic Algorithm

This article presents a new control method based on fuzzy controller, time delay estimation, deep learning, and non-dominated sorting genetic algorithm-III for the nonlinear active mount systems. The proposed method, intelligent adapter fractions proportional–integral–derivative controller, is a smart combination of the time delay estimation control and intelligent fractions proportional–integral–derivative with adaptive control parameters following the speed range of engine rotation via the deep neural network with the optimal non-dominated sorting genetic algorithm-III deep learning algorithm. Besides, we proposed optimal fuzzy logic controller with optimal parameters via particle swarm optimization algorithm to control reciprocal compensation to eliminate errors for intelligent adapter fractions proportional–integral–derivative controller. The control objective is to deal with the classical conflict between minimizing engine vibration impacts on the chassis to increase the ride comfort and keeping the dynamic wheel load small to ensure the ride safety. The results of this control method are compared with that of traditional proportional–integral–derivative controller systems, optimal proportional–integral–derivative controller parameter adjustment using genetic algorithms, linear–quadratic regulator control algorithms, and passive drive system mounts. The results are tested in both time and frequency domains to verify the success of the proposed optimal fuzzy logic controller–intelligent adapter fractions proportional–integral–derivative control system. The results show that the proposed optimal fuzzy logic controller–intelligent adapter fractions proportional–integral–derivative control system of the active engine mount system gives very good results in comfort and softness when riding compared with other controllers.

Download Full-text

Path optimization for navigation of a humanoid robot using hybridized fuzzy-genetic algorithm

International Journal of Intelligent Unmanned Systems ◽

10.1108/ijius-11-2018-0032 ◽

2019 ◽

Vol 7 (3) ◽

pp. 112-119 ◽

Cited By ~ 3

Author(s):

Asita Kumar Rath ◽

Dayal R. Parhi ◽

Harish Chandra Das ◽

Priyadarshi Biplab Kumar ◽

Manoj Kumar Muni ◽

...

Keyword(s):

Genetic Algorithm ◽

Fuzzy Logic ◽

Path Planning ◽

Humanoid Robot ◽

Fuzzy Logic Controller ◽

Target Location ◽

Target Position ◽

Content Type ◽

Hybrid Controller ◽

Fuzzy Genetic

Purpose Humanoids have become the center of attraction for many researchers dealing with robotics investigations by their ability to replace human efforts in critical interventions. As a result, navigation and path planning has emerged as one of the most promising area of research for humanoid models. In this paper, a fuzzy logic controller hybridized with genetic algorithm (GA) has been proposed for path planning of a humanoid robot to avoid obstacles present in a cluttered environment and reach the target location successfully. The paper aims to discuss these issues. Design/methodology/approach Here, sensor outputs for nearest obstacle distances and bearing angle of the humanoid are first fed as inputs to the fuzzy logic controller, and first turning angle (TA) is obtained as an intermediate output. In the second step, the first TA derived from the fuzzy logic controller is again supplied to the GA controller along with other inputs and second TA is obtained as the final output. The developed hybrid controller has been tested in a V-REP simulation platform, and the simulation results are verified in an experimental setup. Findings By implementation of the proposed hybrid controller, the humanoid has reached its defined target position successfully by avoiding the obstacles present in the arena both in simulation and experimental platforms. The results obtained from simulation and experimental platforms are compared in terms of path length and time taken with each other, and close agreements have been observed with minimal percentage of errors. Originality/value Humanoids are considered more efficient than their wheeled robotic forms by their ability to mimic human behavior. The current research deals with the development of a novel hybrid controller considering fuzzy logic and GA for navigational analysis of a humanoid robot. The developed control scheme has been tested in both simulation and real-time environments and proper agreements have been found between the results obtained from them. The proposed approach can also be applied to other humanoid forms and the technique can serve as a pioneer art in humanoid navigation.

Download Full-text

Optimizing fuzzy logic controller for diabetes type I by genetic algorithm

2010 The 2nd International Conference on Computer and Automation Engineering (ICCAE) ◽

10.1109/iccae.2010.5451208 ◽

2010 ◽

Cited By ~ 7

Author(s):

Kambiz Ghaemi Osgouie ◽

Aydin Azizi

Keyword(s):

Genetic Algorithm ◽

Fuzzy Logic ◽

Fuzzy Logic Controller ◽

Diabetes Type ◽

Type I ◽

Diabetes Type I

Download Full-text

Optimal design of building environment with hybrid genetic algorithm, artificial neural network, multivariate regression analysis and fuzzy logic controller

Building and Environment ◽

10.1016/j.buildenv.2020.106810 ◽

2020 ◽

Vol 175 ◽

pp. 106810 ◽

Cited By ~ 5

Author(s):

Tianhu Zhang ◽

Yuanjun Liu ◽

Yandi Rao ◽

Xiaopeng Li ◽

Qingxin Zhao

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Artificial Neural Network ◽

Fuzzy Logic ◽

Regression Analysis ◽

Optimal Design ◽

Multivariate Regression ◽

Fuzzy Logic Controller ◽

Hybrid Genetic Algorithm ◽

Multivariate Regression Analysis

Download Full-text

Speed control of brushless direct current motor using a genetic algorithm–optimized fuzzy proportional integral differential controller

Advances in Mechanical Engineering ◽

10.1177/1687814019890199 ◽

2019 ◽

Vol 11 (11) ◽

pp. 168781401989019 ◽

Cited By ~ 6

Author(s):

Huangshui Hu ◽

Tingting Wang ◽

Siyuan Zhao ◽

Chuhang Wang

Keyword(s):

Genetic Algorithm ◽

Fuzzy Logic ◽

Direct Current ◽

Fuzzy Logic Controller ◽

Speed Control ◽

Operating Conditions ◽

Superior Performance ◽

Membership Functions ◽

Proportional Integral ◽

Differential Type

In this article, a genetic algorithm–based proportional integral differential–type fuzzy logic controller for speed control of brushless direct current motors is presented to improve the performance of a conventional proportional integral differential controller and a fuzzy proportional integral differential controller, which consists of a genetic algorithm–based fuzzy gain tuner and a conventional proportional integral differential controller. The tuner is used to adjust the gain parameters of the conventional proportional integral differential controller by a new fuzzy logic controller. Different from the conventional fuzzy logic controller based on expert experience, the proposed fuzzy logic controller adaptively tunes the membership functions and control rules by using an improved genetic algorithm. Moreover, the genetic algorithm utilizes a novel reproduction operator combined with the fitness value and the Euclidean distance of individuals to optimize the shape of the membership functions and the contents of the rule base. The performance of the genetic algorithm–based proportional integral differential–type fuzzy logic controller is evaluated through extensive simulations under different operating conditions such as varying set speed, constant load, and varying load conditions in terms of overshoot, undershoot, settling time, recovery time, and steady-state error. The results show that the genetic algorithm–based proportional integral differential–type fuzzy logic controller has superior performance than the conventional proportional integral differential controller, gain tuned proportional integral differential controller, conventional fuzzy proportional integral differential controller, and scaling factor tuned fuzzy proportional integral differential controller.

Download Full-text