Identificaion of Fuzzy Control Rules Utilizing Genetic Algorithms and its Application to Mobile Robot

1992 ◽  
Vol 25 (20) ◽  
pp. 249-254
Author(s):  
Hee Soo Hwang ◽  
Young Hoon Joo ◽  
Hyun Ki Kim ◽  
Kwang Bang Woo
Keyword(s):  
Genetic Algorithms ◽  
Fuzzy Control ◽  
Mobile Robot ◽  
Control Rules

Application of genetic algorithms and fuzzy control to a combined sewer pumping station

1999 ◽  
Vol 39 (9) ◽  
pp. 217-224 ◽  
Author(s):  
S. Yagi ◽  
S. Shiba
Keyword(s):  
Genetic Algorithms ◽  
Fuzzy Control ◽  
Fuzzy Inference ◽  
Fitness Function ◽  
Pumping Station ◽  
Fitness Functions ◽  
Control Rules ◽  
Combined Sewer ◽  
Pumping Rates ◽  
Input Variables

In the present study, fuzzy logic control and genetic algorithms are applied to achieve improved pump operations in a combined sewer pumping station. Pumping rates are determined by fuzzy inference and fuzzy control rules corresponding to input variables. Genetic algorithms are used to automatically improve the fuzzy control rules through genetic operations such as selection, crossover and mutation. The effects of different fitness functions and learning conditions are investigated using a stormwater runoff model. It is found that current pump operations can be improved by adding the sewer water quality to the input variables and to the fitness function; the improved operations can reduce not only floods in the drainage area but also pollutant loads discharged to the receiving waters.

scholarly journals Multivariable Fuzzy Control Based Mobile Robot Odor Source Localization via Semitensor Product

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Ping Jiang ◽  
Yuzhen Wang ◽  
Aidong Ge
Keyword(s):  
Control System ◽  
Fuzzy Control ◽  
Mobile Robot ◽  
Source Localization ◽  
Odor Source ◽  
Algebraic Equations ◽  
Fuzzy Control System ◽  
Control Rules ◽  
Control Scheme ◽  
Set Up

In order to take full advantage of the multisensor information, a MIMO fuzzy control system based on semitensor product (STP) is set up for mobile robot odor source localization (OSL). Multisensor information, such as vision, olfaction, laser, wind speed, and direction, is the input of the fuzzy control system and the relative searching strategies, such as random searching (RS), nearest distance-based vision searching (NDVS), and odor source declaration (OSD), are the outputs. Fuzzy control rules with algebraic equations are given according to the multisensor information via STP. Any output can be updated in the proposed fuzzy control system and has no influence on the other searching strategies. The proposed MIMO fuzzy control scheme based on STP can reach the theoretical system of the mobile robot OSL. Experimental results show the efficiency of the proposed method.

A learning process for fuzzy control rules using genetic algorithms

1998 ◽  
Vol 100 (1-3) ◽  
pp. 143-158 ◽  
Author(s):  
F. Herrera ◽  
M. Lozano ◽  
J.L. Verdegay
Keyword(s):  
Genetic Algorithms ◽  
Fuzzy Control ◽  
Learning Process ◽  
Control Rules

Fuzzy Control of Target Approaching and Object-Grabbing for a Four-Wheeled Vision-Based Mobile Robot

2013 ◽  
Vol 17 (2) ◽  
pp. 343-350
Author(s):  
Chih-Hung Wu ◽  
◽  
I-Sheng Lin ◽  
Ming-Liang Wei
Keyword(s):  
Fuzzy Control ◽  
Mobile Robot ◽  
Fuzzy Sets ◽  
Fuzzy Controller ◽  
Ccd Camera ◽  
Control Rules ◽  
Practical Experiences ◽  
And Control

This paper presents practical experiences in deploying a fuzzy controller on a vision-based fourwheeled mobile robot for target-approaching and object-grabbing. The robot senses the environment using a simple CCD camera in its patrol. One of the robot’s missions is to actuate a mechanical grabber for picking up specific objects detected in its patrol routine. To overcome the control errors caused by physical friction and inertia, a fuzzy controller is designed and implemented for wheel-driving and objectgrabbing. Definitions are presented for fuzzy sets and control rules that consider hardware specifications. The feasibility of the method has been verified under various ground friction. Experiments in the performance of the proposed method are presented and analyzed.

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