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.

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.

Fuzzy Controller Approach for DC Motor Control on Fishing Rod

2015 ◽  
Vol 2 (1) ◽  
pp. 20-28
Author(s):  
Emmanuel Ade Crisna Putra ◽  
Houtman P. Siregar
Keyword(s):  
Fuzzy Logic ◽  
Transfer Function ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Fuzzy Rule ◽  
Dc Motor ◽  
Step Response ◽  
Rule Base ◽  
Dc Motor Control ◽  
Automation Control

In this paper described the usable and effectiveness of automation control by using fuzzy logic controller forcontrolling the speed of DC motor that will be used on string roller of fishing rod. The transfer function of DCmotor has been obtained. For transfer function, the load of DC motor will be acted as input, and the output is thevelocity of DC motor. The fuzzy rule base then created by trial and error. The step response between fuzzy logiccontroller and without using fuzzy logic controller then obtained and compared. As a result, the fuzzy logic hassuccessfully reduced the overshoot of step response.

Adaptive Neuro-Fuzzy Control Approach Based on Particle Swarm Optimization

2010 ◽  
Vol 1 (4) ◽  
pp. 1-16
Author(s):  
Gomaa Zaki El-Far
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Particle Swarm ◽  
Rule Base ◽  
Swarm Optimization ◽  
Linear Dynamical Systems ◽  
Neuro Fuzzy ◽  
Non Linear

This paper proposes a modified particle swarm optimization algorithm (MPSO) to design adaptive neuro-fuzzy controller parameters for controlling the behavior of non-linear dynamical systems. The modification of the proposed algorithm includes adding adaptive weights to the swarm optimization algorithm, which introduces a new update. The proposed MPSO algorithm uses a minimum velocity threshold to control the velocity of the particles, avoids clustering of the particles, and maintains the diversity of the population in the search space. The mechanism of MPSO has better potential to explore good solutions in new search spaces. The proposed MPSO algorithm is also used to tune and optimize the controller parameters like the scaling factors, the membership functions, and the rule base. To illustrate the adaptation process, the proposed neuro-fuzzy controller based on MPSO algorithm is applied successfully to control the behavior of both non-linear single machine power systems and non-linear inverted pendulum systems. Simulation results demonstrate that the adaptive neuro-fuzzy logic controller application based on MPSO can effectively and robustly enhance the damping of oscillations.

Neuro Fuzzy Controller for DTC of Induction Motor Using Multilevel Inverter with SVM

2021 ◽  
pp. 2150250
Author(s):  
B. Kiran Kumar ◽  
Y. V. Siva Reddy ◽  
M. Vijaya Kumar
Keyword(s):  
Induction Motor ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Reference Voltage ◽  
Training Dataset ◽  
Testing Time ◽  
Hybrid Technique ◽  
Nonlinear Loads ◽  
Neuro Fuzzy ◽  
Voltage Signal

In this paper, an effective neuro-fuzzy controller (NFC) technique has been proposed to control the induction motor torque and flux. The NFC hybrid technique is the grouping of the neural network (NN) and fuzzy logic controller (FLC), which generated the target voltages with the corresponding input flux and torque. The novelty of the proposed hybrid technique is highly flexible in nonlinear loads, convenient user interface and logical intellectual and permitting for integrated controlling schemes. Here, the FLC generates the training dataset of the NN technique based on the logical rules. The generated dataset contains the information about the flux and torque deviation parameters and the corresponding reference voltage parameters. The NN has been trained based on training dataset and the testing time which produces the optimal reference voltage parameters depends on the variation of the torque and flux parameters. By using the output of the NFC technique, the space vector modulation (SVM) develops the appropriate control pulses to the five-level inverter and the inverter generates the output voltage signal to the induction motor. The proposed method is designed in the MATLAB/Simulink platform and the outputs are verified through the comparison analysis with the existing techniques.

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.

Design and Implementation of Neuro-Fuzzy Controller Using FPGA for Sun Tracking System

2016 ◽  
Vol 12 (2) ◽  
pp. 123-136 ◽  
Author(s):  
Ammar Aldair ◽  
Adel Obed ◽  
Ali Halihal
Keyword(s):  
Optical Sensors ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Tracking System ◽  
Maximum Power ◽  
Experimental Results ◽  
The Sun ◽  
Fixed Angle ◽  
Pv Panel ◽  
Neuro Fuzzy

Nowadays, renewable energy is being used increasingly because of the global warming and destruction of the environment. Therefore, the studies are concentrating on gain of maximum power from this energy such as the solar energy. A sun tracker is device which rotates a photovoltaic (PV) panel to the sun to get the maximum power. Disturbances which are originated by passing the clouds are one of great challenges in design of the controller in addition to the losses power due to energy consumption in the motors and lifetime limitation of the sun tracker. In this paper, the neuro-fuzzy controller has been designed and implemented using Field Programmable Gate Array (FPGA) board for dual axis sun tracker based on optical sensors to orient the PV panel by two linear actuators. The experimental results reveal that proposed controller is more robust than fuzzy logic controller and proportional-integral (PI) controller since it has been trained offline using Matlab tool box to overcome those disturbances. The proposed controller can track the sun trajectory effectively, where the experimental results reveal that dual axis sun tracker power can collect 50.6% more daily power than fixed angle panel. Whilst one axis sun tracker power can collect 39.4 % more daily power than fixed angle panel. Hence, dual axis sun tracker can collect 8 % more daily power than one axis sun tracker.

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.

Neural Network Based Intelligent Learning of Fuzzy Logic Controller Parameters

2004 ◽  
Author(s):  
Manish Kumar ◽  
Devendra P. Garg
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Network Architecture ◽  
Fuzzy Logic Controller ◽  
Rule Base ◽  
Neural Network Architecture ◽  
The Neural Network ◽  
Neuro Fuzzy ◽  
Learning Capabilities ◽  
Optimal Signal

Design of an efficient fuzzy logic controller involves the optimization of parameters of fuzzy sets and proper choice of rule base. There are several techniques reported in recent literature that use neural network architecture and genetic algorithms to learn and optimize a fuzzy logic controller. This paper presents methodologies to learn and optimize fuzzy logic controller parameters that use learning capabilities of neural network. Concepts of model predictive control (MPC) have been used to obtain optimal signal to train the neural network via backpropagation. The strategies developed have been applied to control an inverted pendulum and results have been compared for two different fuzzy logic controllers developed with the help of neural networks. The first neural network emulates a PD controller, while the second controller is developed based on MPC. The proposed approach can be applied to learn fuzzy logic controller parameter online via the use of dynamic backpropagation. The results show that the Neuro-Fuzzy approaches were able to learn rule base and identify membership function parameters accurately.

Fuzzy and neuro-fuzzy approaches to control a flexible single-link manipulator

2003 ◽  
Vol 217 (5) ◽  
pp. 387-399 ◽  
Author(s):  
B Subudhi ◽  
A S Morris
Keyword(s):  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Fuzzy Model ◽  
Scale Parameters ◽  
Neuro Fuzzy ◽  
On Line ◽  
Position Regulation ◽  
Tip Position ◽  
Typical Model ◽  
Better Than

In this paper, new fuzzy and neuro-fuzzy approaches to tip position regulation of a flexible-link manipulator are presented. Firstly, a non-collocated, proportional-dervative (PD) type, fuzzy logic controller (FLC) is developed. This is shown to perform better than typical model-based controllers (LQR and PD). Following this, an adaptive neuro-fuzzy controller (NFC) is described that has been developed for situations where there is payload variability. The proposed NFC tunes the input and output scale parameters of the fuzzy controller on-line. The efficacy of the NFC has been evaluated by comparing it with a fuzzy model reference adptive controller (FMRC).

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