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).

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.

Application of on-line neuro-fuzzy controller to AUVs

2002 ◽  
Vol 145 (1-2) ◽  
pp. 169-182 ◽  
Author(s):  
T.W. Kim ◽  
J. Yuh
Keyword(s):  
Fuzzy Controller ◽  
Neuro Fuzzy ◽  
On Line

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.

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.

scholarly journals Design and Implementation of a Fuzzy Controller for Small Rotation Angles

2011 ◽  
Vol 7 (1) ◽  
pp. 14-18
Author(s):  
Mohammed Hussein
Keyword(s):  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Position Error ◽  
Error Performance ◽  
Unknown Parameters ◽  
Adaptation Mechanism ◽  
Design And Implementation ◽  
On Line ◽  
Input Variables ◽  
Small Rotation

This paper present an adaptation mechanism for fuzzy logic controller FLC in order to perfect the response performance against small rotation angles of real D.C. motor with unknown parameters. A supervisor fuzzy controller SFC is designed to continuously adjust, on-line, the universe of discourse UOD of the basic fuzzy controller BFC input variables based on position error and change of position error. Performance of the proposed adaptive fuzzy controller is compared with corresponding conventional FLC in terms of several performance measures such rise time, settling time, peak overshoot, and steady state error. The system design and implementation are carried out using LabVIEW 2009 with NI PCI-6251 data acquisition DAQ card. The practical results demonstrate using self tuning FLC scheme grant a better performance as compared with conventional FLC which is incapable of rotating a motor if the rotation angle is being small.

scholarly journals Design of a hierarchical fuzzy model predictive controller

2015 ◽  
Vol 4 (2) ◽  
pp. 342 ◽  
Author(s):  
Zeinab Fallah ◽  
Mojtaba Ahmadieh Khanesar ◽  
Mohammad Teshnehlab
Keyword(s):  
Nonlinear Model ◽  
Fuzzy Systems ◽  
Gradient Descent ◽  
Fuzzy Controller ◽  
Fuzzy Model ◽  
Least Square ◽  
Continuous Stirred Tank Reactor ◽  
Descent Algorithm ◽  
Neuro Fuzzy ◽  
Gradient Descent Algorithm

In order to control a nonlinear system using Nonlinear Model Predictive Control (NMPC), a nonlinear model from system is required. In this paper, a hierarchical neuro-fuzzy model is used for nonlinear identification of the plant. The use of hierarchical neuro-fuzzy systems makes it possible to overcome the curse of dimensionality. In neuro-fuzzy systems, if the input number increases, then the number of rules increases exponentially. One solution to this problem is making use of Hierarchical Fuzzy System Mamdani (HFS) in which the number of the rules increases linearly. Gradient descent and recursive least square algorithm are used simultaneously to train the parameters of the HFS. Gradient Descent Algorithm is utilized to train the parameters, which appear nonlinearly in the output of HFS, and RLS is used to train the parameters of consequent the part, which appears linearly in the output of HFS. Finally, a model predictive fuzzy controller based on a predictive cost function is proposed. Using Gradient Descent Algorithm, the parameters of the controller are optimized. The proposed controller is simulated on the control of continuous stirred tank reactor. It is shown that the proposed method can control the system with high performance.

scholarly journals Comparison of self-tuned Neuro-Fuzzy controllers on 2 DOF helicopter: an application

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Muhammet Öztürk ◽  
İbrahim Özkol
Keyword(s):  
Fuzzy Inference ◽  
Fuzzy Controller ◽  
Strong Stability ◽  
Inference System ◽  
New Approach ◽  
Fuzzy Controllers ◽  
Advantages And Disadvantages ◽  
Neuro Fuzzy ◽  
On Line ◽  
The Difference

AbstractIn this paper, a new approach for Neuro-Fuzzy Controller (NFC) has been presented and compared to previously defined NFCs given in open literature. The proposed controller is based on an on-line Adaptive Neuro-Fuzzy Inference System (ANFIS) and meticulous analysis through simulations is performed to show its robustness. The performance of Neuro-Fuzzy Controllers (NFC) depends on controller inputs. To show the difference and superiority of the proposed controller, many studies in the open literature are examined and compared. Therefore, the advantages and disadvantages of the Neuro-Fuzzy controller are outlined and an optimum Neuro-Fuzzy controller is structured and presented. To test our developed controller for a nonlinear problem, having coupling effects, a 2 DOF helicopter model is chosen. Also to show the robustness, the controller performance which is applied to a 2 DOF helicopter is investigated and compared with other Neuro-Fuzzy controller structures. To better show NFC performance, NFC control results were compared with LQR+I. It is observed that besides being on-line adaptive for all systems, the controller developed has many priorities such as noiseless, strong stability, and better response time.

scholarly journals Online Epileptic Seizure Prediction Using Phase Synchronization and Two Time Characteristics: SOP and SPH

2019 ◽  
Vol 7 (1) ◽  
pp. 16-25
Author(s):  
Hesam Shokouh Alaei ◽  
Mohammad Ali Khalilzadeh ◽  
Ali Gorji
Keyword(s):  
Epileptic Seizure ◽  
False Positive ◽  
Evaluation Method ◽  
Fuzzy Model ◽  
Seizure Prediction ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Prediction Time ◽  
On Line ◽  
Labeling Method

Background: The successful prediction of epileptic seizures will significantly improve the living conditions of patients with refractory epilepsy. A proper warning impending seizure system should be resulted not only in high accuracy and low false-positive alarms but also in suitable prediction time. Methods: In this research, the mean phase coherence index used as a reliable indicator for identifying the preictal period of the 14-patient Freiburg EEG dataset. In order to predict the seizures on-line, an adaptive Neuro-fuzzy model named ENFM (evolving neuro-fuzzy model) was used to classify the extracted features. The ENFM trained by a new class labeling method based on the temporal properties of a prediction characterized by two time intervals, seizure prediction horizon (SPH), and seizure occurrence period (SOP), which subsequently applied in the evaluation method. It is evident that an increase in the duration of the SPH can be more useful for the subject in preventing the irreparable consequences of the seizure, and provides adequate time to deal with the seizure. Also, a reduction in duration of the SOP can reduce the patient’s stress in the SOP interval. In this study, the optimal SOP and SPH obtained for each patient using Mamdani fuzzy inference system considering sensitivity, false-positive rate (FPR), and the two mentioned points, which generally ignored in most studies. Results: The results showed that last seizure, as well as 14-hour interictal period of each patient, were predicted on-line without false negative alarms: the average yielding of sensitivity by 100%, the average FPR by 0.13 per hour and the average prediction time by 30 minutes. Conclusion: Based on the obtained results, such a data-labeling method for ENFM showed promising seizure prediction for online machine learning using epileptic seizure data. Apart from that, the proposed fuzzy system can consider as an evaluation method for comparing the results of studies.

scholarly journals Investigation of the Possibility of Building a Neural Fuzzy Logic Controller with Discrete Terms for Controlling and Automating Oil and Gas Engineering Facilities

2021 ◽  
Vol 19 (3) ◽  
pp. 105-110
Author(s):  
A. M. Sagdatullin ◽  
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Control System ◽  
Fuzzy Control ◽  
Control Systems ◽  
Fuzzy Logic Controller ◽  
Oil And Gas ◽  
Fuzzy Controller ◽  
Neuro Fuzzy ◽  
Classical Control

The issue of increasing the efficiency of functioning of classical control systems for technological processes and objects of oil and gas engineering is investigated. The relevance of this topic lies in the need to improve the quality of the control systems for the production and transportation of oil and gas. The purpose of the scientific work is to develop a neuro-fuzzy logic controller with discrete terms for the control and automation of pumping units and pumping stations. It is noted that fuzzy logic, neural network algorithms, together with control methods based on adaptation and synthesis of control objects, make it possible to learn the automation system and work under conditions of uncertainty. Methods for constructing classical control systems are studied, the advantages and disadvantages of fuzzy controllers, as the main control system, are analyzed. A method for constructing a control system based on a neuro-fuzzy controller with discrete terms in conditions of uncertainty and dynamic parameters of the process is proposed. The positive features of the proposed regulator include a combination of fuzzy reasoning about a technological object and mathematical predictive models, a fuzzy control system gains the possibility of subjective description based on neural network structures, as well as adaptation to the characteristics of the object. The graph of dependence for the term-set of the controlled parameter on the degree of membership is presented. A possible implementation of tracking the triggering of one of the rules of the neuro-fuzzy system in the format of functional block diagrams is presented. The process of forming an expert knowledge base in a neuro-fuzzy control system is considered. For analysis, a graph of the dependence of the output parameter values is shown. According to the results obtained, the deviation of the values for the model and the real process does not exceed 18%, which allows us to speak of a fairly stable operation of the neuro-fuzzy controller in automatic control systems.

Design of a Hybrid Adaptive Neuro Fuzzy Inference System (ANFIS) Controller for Position and Angle Control of Inverted Pendulum (IP) Systems

Fuzzy Systems ◽  
2017 ◽  
pp. 308-320
Author(s):  
Ashwani Kharola
Keyword(s):  
Fuzzy Logic ◽  
Inverted Pendulum ◽  
Fuzzy Logic Controller ◽  
Fuzzy Inference ◽  
Fuzzy Controller ◽  
Data Sets ◽  
Inference System ◽  
Simulink Model ◽  
Neuro Fuzzy ◽  
Proposed Model

This paper illustrates a comparison study of Fuzzy and ANFIS Controller for Inverted Pendulum systems. IP belongs to a class of highly non-linear, unstable and multi-variable systems which act as a testing bed for many complex systems. Initially, a Matlab-Simulink model of IP system was proposed. Secondly, a Fuzzy logic controller was designed using Mamdani inference system for control of proposed model. The data sets from fuzzy controller was used for development of a Hybrid Sugeno ANFIS controller. The results shows that ANFIS controller provides better results in terms of Performance parameters including Settling time(sec), maximum overshoot(degree) and steady state error.

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