Rule base modification scheme for improved performance of FOPDT process using PI like fuzzy controller

2013 ◽  
Author(s):  
N. J. Patil ◽  
R. P. Borse
Keyword(s):  
Fuzzy Controller ◽  
Rule Base ◽  
Base Modification ◽  
Improved Performance

A Fuzzy Logic Controlled Single Axis Solar Tracking System

2015 ◽  
Vol 787 ◽  
pp. 893-898
Author(s):  
Suneetha Racharla ◽  
K. Rajan ◽  
K.R. Senthil Kumar
Keyword(s):  
Fuzzy Controller ◽  
Tracking System ◽  
Renewable Energy Sources ◽  
Energy Resource ◽  
Clean Energy ◽  
Energy Sources ◽  
Pv Panel ◽  
Solar Tracking ◽  
Simulation Results ◽  
Improved Performance

Recently renewable energy sources have gained much attention as a clean energy. But the main problem occurs with the varying nature with the day and season. Aim of this paper is to conserve the energy, of the natural resources. For solar energy resource, the output induced in the photovoltaic (PV) modules depends on solar radiation and temperature of the solar cells. To maximize the efficiency of the system it is necessary to track the path of sun in order to keep the panel perpendicular to the sun. This paper proposes the design and construction of a microcontroller-based solar panel tracking system. The fuzzy controller aims at maximizing the efficiency of PV panel by focusing the sunlight to incident perpendicularly to the panel. The system consists of a PV panel which can be operated with the help of DC motor, four LED sensors placed in different positions and a fuzzy controller which takes the input from sensors and gives output speed to motor. A prototype is fabricated to test the results and compared with the simulation results. The results show the improved performance by using a tracking system

scholarly journals Design and Lyapunov Stability Analysis of a Fuzzy Logic Controller for Autonomous Road Following

2010 ◽  
Vol 2010 ◽  
pp. 1-20 ◽  
Author(s):  
Yi Fu ◽  
Howard Li ◽  
Mary Kaye
Keyword(s):  
Fuzzy Logic ◽  
Stability Analysis ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Intelligent Vehicles ◽  
Intelligent Vehicle ◽  
Rule Base ◽  
Control Laws ◽  
Robotic Vehicle ◽  
Road Following

Autonomous road following is one of the major goals in intelligent vehicle applications. The development of an autonomous road following embedded system for intelligent vehicles is the focus of this paper. A fuzzy logic controller (FLC) is designed for vision-based autonomous road following. The stability analysis of this control system is addressed. Lyapunov's direct method is utilized to formulate a class of control laws that guarantee the convergence of the steering error. Certain requirements for the control laws are presented for designers to choose a suitable rule base for the fuzzy controller in order to make the system stable. Stability of the proposed fuzzy controller is guaranteed theoretically and also demonstrated by simulation studies and experiments. Simulations using the model of the four degree of freedom nonholonomic robotic vehicle are conducted to investigate the performance of the fuzzy controller. The proposed fuzzy controller can achieve the desired steering angle and make the robotic vehicle follow the road successfully. Experiments show that the developed intelligent vehicle is able to follow a mocked road autonomously.

scholarly journals Fuzzy rule: Based model reference adaptive control for PMSM drives

2007 ◽  
Vol 4 (1) ◽  
pp. 13-22 ◽  
Author(s):  
Mohamed Kadjoudj ◽  
Noureddine Golea ◽  
Hachemi Benbouzid
Keyword(s):  
Knowledge Base ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Reference Model ◽  
Adaptive Fuzzy Control ◽  
Rule Base ◽  
Motor Speed ◽  
Model Reference ◽  
Adaptive Fuzzy ◽  
Model Reference Adaptive

The objective of the model reference adaptive fuzzy control (MRAFC) is to change the rules definition in the direct fuzzy logic controller (FLC) and rule base table according to the comparison between the reference model output signal and system output. The MRAFC is composed by the fuzzy inverse model and a knowledge base modifier. Because of its improved algorithm, the MRAFC has fast learning features and good tracking characteristics even under severe variations of system parameters. The learning mechanism observes the plant outputs and adjusts the rules in a direct fuzzy controller, so that the overall system behaves like a reference model, which characterizes the desired behavior. In the proposed scheme, the error and error change measured between the motor speed and output of the reference model are applied to the MRAFC. The latter will force the system to behave like the signal reference by modifying the knowledge base of the FLC or by adding an adaptation signal to the fuzzy controller output. In this paper, the MRAFC is applied to a permanent magnet synchronous motor drive (PMSM). High performances and robustness have been achieved by using the MRAFC. This will be illustrated by simulation results and comparisons with other controllers such as PI classical and adaptive fuzzy controller based on gradient method controllers.

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.

Navigational Analysis of Multiple Humanoids Using a Hybridized Rule Base-Sugeno Fuzzy Controller

2020 ◽  
Vol 17 (04) ◽  
pp. 2050017
Author(s):  
Manoj Kumar Muni ◽  
Dayal R. Parhi ◽  
Priyadarshi Biplab Kumar ◽  
Asita Kumar Rath
Keyword(s):  
Performance Enhancement ◽  
Fuzzy Controller ◽  
Fuzzy Model ◽  
Humanoid Robots ◽  
Rule Base ◽  
Turning Angle ◽  
Cluttered Environments ◽  
Hybrid Controller ◽  
Final Output ◽  
Set Up

This paper describes a rule base-Sugeno fuzzy hybrid controller for path planning of single as well as multiple humanoid robots in cluttered environments. Initially, sensor outputs regarding the obstacle distances are used as inputs to the rule base model, and turning angle is obtained as the output. The rule-based analysis is used for training the fuzzy controller with membership functions. The output from the rule base model along with other regular inputs is supplied to a Sugeno fuzzy model, and effective turning angle is obtained as the final output to avoid the obstacles present in the environment and navigate the humanoids safely to their target points. The proposed hybrid controller is tested on a V-REP simulation platform, and the simulation results are validated in an experimental set-up. To avoid the possibility of any inter-collision during navigation of multiple humanoids on a common platform, a Petri-net scheme is integrated along with the proposed hybrid model. Finally, the results obtained from simulation and experimental platforms are compared against each other with proper agreement and minimal percentage of deviations. To validate the proposed controller, it has also been tested against another existing navigational approach, and satisfactory performance enhancement has been observed.

BACKPROPAGATION THROUGH TIME TRAINING OF A NEURO-FUZZY CONTROLLER

2010 ◽  
Vol 20 (05) ◽  
pp. 421-428 ◽  
Author(s):  
PETIA KOPRINKOVA-HRISTOVA
Keyword(s):  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Fuzzy Rule ◽  
Linguistic Meaning ◽  
Rule Base ◽  
Linguistic Variables ◽  
Backpropagation Through Time ◽  
Linear Plant ◽  
Neuro Fuzzy ◽  
Neural Network Structure

The paper considers gradient training of fuzzy logic controller (FLC) presented in the form of neural network structure. The proposed neuro-fuzzy structure allows keeping linguistic meaning of fuzzy rule base. Its main adjustable parameters are shape determining parameters of the linguistic variables fuzzy values as well as that of the used as intersection operator parameterized T-norm. The backpropagation through time method was applied to train neuro-FLC for a highly non-linear plant (a biotechnological process). The obtained results are discussed with respect to adjustable parameters rationality. Conclusions are made with respect to the appropriate intersection operations too.

Fuzzy Quality Measures for Use in a Hierarchical Fuzzy Controller

2000 ◽  
Author(s):  
Alan Carlson ◽  
Dean B. Edwards ◽  
Michael J. Anderson
Keyword(s):  
Fuzzy Controller ◽  
Structure Optimization ◽  
Quality Measures ◽  
Quality Measure ◽  
Approximate Determination ◽  
Rule Base ◽  
System Parameters ◽  
The Individual ◽  
Monolithic Structure

Abstract This paper presents a control strategy that uses a hierarchical structure to arbitrate between recommendations from lower level modules. The lower level modules represent lower level tasks or behaviors. Each lower level module provides its own control recommendation based on its limited perception of the environment. We introduce the concept of a fuzzy quality measure that may be used by the hierarchical controller to determine how best to fuse the individual recommendations. The Quality Measure provides an approximate determination of each control recommendations potential value. The hierarchical partitioning reduces the cardinality of the rule base and decreases the number of system parameters, as compared to a monolithic structure. Optimization of the reduced parameter set is simpler and requires less time.

Lyapunov approach based design of a gain adaptive interval type-2 fuzzy controller for servo systems

2020 ◽  
pp. 1-19
Author(s):  
Ritu Rani De (Maity) ◽  
Rajani K. Mudi ◽  
Chanchal Dey
Keyword(s):  
Fuzzy Logic ◽  
Stability Analysis ◽  
Fuzzy Controller ◽  
Rule Base ◽  
Performance Study ◽  
Servo Systems ◽  
Lyapunov Approach ◽  
The Stability ◽  
Interval Type

This paper focuses on the development of a stable Mamdani type-2 fuzzy logic based controller for automatic control of servo systems. The stability analysis of the fuzzy controller has been done by employing the concept of Lyapunov. The Lyapunov approach results in the derivation of an original stability analysis that can be used for designing the rule base of our proposed online gain adaptive Interval Type-2 Fuzzy Proportional Derivative controller (IT2-GFPD) for servo systems with assured stability. In this approach a Quadratic positive definite Lyapunov function is used and sufficient stability conditions are satisfied by the adaptive type-2 fuzzy logic control system. Illustrative simulation studies with linear and nonlinear models as well as experimental results on a real-time servo system validate the stability and robustness of the developed intelligent IT2-GFPD. A comparative performance study of IT2-GFPD with other controllers in presence of noise and disturbance also proves the superiority of the proposed controller.

scholarly journals Dynamic Residential Energy Management for Real-Time Pricing

Energies ◽  
2020 ◽  
Vol 13 (10) ◽  
pp. 2562
Author(s):  
Leehter Yao ◽  
Fazida Hanim Hashim ◽  
Chien-Chi Lai
Keyword(s):  
Real Time ◽  
Energy Management ◽  
Smart Home ◽  
Solar Power ◽  
Fuzzy Controller ◽  
Fuzzy Rule ◽  
Rule Base ◽  
Learning Scheme ◽  
Home Energy Management ◽  
Self Learning

A home energy management system (HEMS) was designed in this paper for a smart home that uses integrated energy resources such as power from the grid, solar power generated from photovoltaic (PV) panels, and power from an energy storage system (ESS). A fuzzy controller is proposed for the HEMS to optimally manage the integrated power of the smart home. The fuzzy controller is designed to control the power rectifier for regulating the AC power in response to the variations in the residential electric load, solar power from PV panels, power of the ESS, and the real-time electricity prices. A self-learning scheme is designed for the proposed fuzzy controller to adapt with short-term and seasonal climatic changes and residential load variations. A parsimonious parameterization scheme for both the antecedent and consequent parts of the fuzzy rule base is utilized so that the self-learning scheme of the fuzzy controller is computationally efficient.

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