Implementation of a Fuzzy Logic Controller for the Irrigation of Rose Cultivation in Mexico

The design and implementation of a fuzzy logic controller (FLC) are presented, offering a solution to improve the irrigation of rose crops. The objective is to reduce the water consumption and operative costs, taking advantage of intelligent controllers and environmental characteristics in a specific region. Considering that the main controllable variables that affect the growth of plants are relative humidity (RH) and temperature (T), in this study, these variables are used to create a system whose aim is to provide an adequate amount of water for a rose crop in the State of Mexico. The Mamdani method was used for the FLC design and the membership functions, while the area centroid was considered as the defuzzification strategy. After implementing the FLC proposal using a field-programmable gate array (FPGA) in a domestic greenhouse, integrated by an array of [5 × 3] rose plants under natural restrictions, a reduction of 0.2 L per week with respect to the traditional manual irrigation system was found. The proposed design highlights the technological advantages of using a fuzzy logic-controlled irrigation system over traditional methods.

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Fuzzy Logic Controller for Obstacle Avoidance of Mobile Robot

International Journal of Nonlinear Sciences and Numerical Simulation ◽

10.1515/ijnsns-2018-0038 ◽

2019 ◽

Vol 20 (1) ◽

pp. 51-62

Author(s):

Rajmeet Singh ◽

Tarun Kumar Bera

Keyword(s):

Fuzzy Logic ◽

Mobile Robot ◽

Dynamic Model ◽

Obstacle Avoidance ◽

Fuzzy Logic Controller ◽

Fuzzy Controller ◽

Bond Graph ◽

Membership Functions ◽

Current Position ◽

Dc Motors

AbstractThis work describes design and implementation of a navigation and obstacle avoidance controller using fuzzy logic for four-wheel mobile robot. The main contribution of this paper can be summarized in the fact that single fuzzy logic controller can be used for navigation as well as obstacle avoidance (static, dynamic and both) for dynamic model of four-wheel mobile robot. The bond graph is used to develop the dynamic model of mobile robot and then it is converted into SIMULINK block by using ‘S-function’ directly from SYMBOLS Shakti bond graph software library. The four-wheel mobile robot used in this work is equipped with DC motors, three ultrasonic sensors to measure the distance from the obstacles and optical encoders to provide the current position and speed. The three input membership functions (distance from target, angle and distance from obstacles) and two output membership functions (left wheel voltage and right wheel voltage) are considered in fuzzy logic controller. One hundred and sixty-two sets of rules are considered for motion control of the mobile robot. The different case studies are considered and are simulated using MATLAB-SIMULINK software platform to evaluate the performance of the controller. Simulation results show the performances of the navigation and obstacle avoidance fuzzy controller in terms of minimum travelled path for various cases.

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

Journal of Computer Science ◽

10.3844/jcssp.2008.799.806 ◽

2008 ◽

Vol 4 (10) ◽

pp. 799-806 ◽

Cited By ~ 20

Author(s):

Sheroz Khan ◽

Salami Femi Abdulazeez ◽

Lawal Wahab Adetunji ◽

AHM Zahirul Alam ◽

Momoh Jimoh E. Salami ◽

...

Keyword(s):

Genetic Algorithm ◽

Fuzzy Logic ◽

Fuzzy Logic Controller ◽

Design And Implementation

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Embedded Fuzzy Logic Controller and Wireless Communication for Home Energy Management Systems

Electronics ◽

10.3390/electronics7090189 ◽

2018 ◽

Vol 7 (9) ◽

pp. 189 ◽

Cited By ~ 2

Author(s):

Aryuanto Soetedjo ◽

Yusuf Nakhoda ◽

Choirul Saleh

Keyword(s):

Fuzzy Logic ◽

Energy Management ◽

Cost Reduction ◽

Fuzzy Logic Controller ◽

Management Systems ◽

Fuzzy Membership ◽

Membership Functions ◽

Energy Management Systems ◽

Fuzzy Membership Functions ◽

Home Energy Management

Energy management systems in residential areas have attracted the attention of many researchers along the deployment of smart grids, smart cities, and smart homes. This paper presents the implementation of a Home Energy Management System (HEMS) based on the fuzzy logic controller. The objective of the proposed HEMS is to minimize electricity cost by managing the energy from the photovoltaic (PV) to supply home appliances in the grid-connected PV-battery system. A fuzzy logic controller is implemented on a low-cost embedded system to achieve the objective. The fuzzy logic controller is developed by the distributed approach where each home appliance has its own fuzzy logic controller. An automatic tuning of the fuzzy membership functions using the Genetic Algorithm is developed to improve performance. To exchange data between the controllers, wireless communication based on WiFi technology is adopted. The proposed configuration provides a simple effective technology that can be implemented in residential homes. The experimental results show that the proposed system achieves a fast processing time on a ten-second basis, which is fast enough for HEMS implementation. When tested under four different scenarios, the proposed fuzzy logic controller yields an average cost reduction of 10.933% compared to the system without a fuzzy logic controller. Furthermore, by tuning the fuzzy membership functions using the genetic algorithm, the average cost reduction increases to 12.493%.

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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.

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