scholarly journals Hysteresis Modeling of a PAM System Using ANFIS

Actuators ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 280
Author(s):  
Saad Abu Mohareb ◽  
Adham Alsharkawi ◽  
Moudar Zgoul
Keyword(s):  
Fuzzy Inference ◽  
Pi Controller ◽  
Rate Of Change ◽  
Hysteretic Behavior ◽  
Artificial Muscles ◽  
Tracking Errors ◽  
Inference System ◽  
Control Schemes ◽  
Hysteresis Modeling ◽  
Pneumatic Artificial Muscles

Pneumatic artificial muscles (PAMs) are excellent environmentally friendly actuators and springs that remain somewhat underutilized in the industry due to their hysteretic behavior, which makes predicting their behavior difficult. This paper presents a novel black-box approach that employs an adaptive-network-based fuzzy inference system (ANFIS) to create pressure-contraction hysteresis models. The resulting models are simulated in a control system toolbox to test their controllability using a simple proportional-integral (PI) controller. The data showed that the models created based on fixed inputs had an average normalized root mean square error (RMSE) of 0.0327, and their generalized counterparts achieved an average normalized RMSE of 0.04087. The simulation results showed that the PI controller was able to achieve mean tracking errors of 8.1 µm and 18.3 µm when attempting to track a sinusoidal and step references, respectively. This work concludes that modeling using the ANFIS is limited to being able to know the derivative of the input pressure or its rate of change, but competently models hysteresis in PAMs across multiple operating ranges. This is the highlight of this work. Additionally, these ANFIS-created models lend themselves well to controller, but exploring more refined control schemes is necessary to fully utilize them.

Adaptive Neural Fuzzy Inference System Optimization in Converter

2017 ◽  
Vol 14 (1) ◽  
pp. 640-646
Author(s):  
R Premalatha ◽  
P Murugesan
Keyword(s):  
Fuzzy Inference ◽  
Good Control ◽  
Speed Control ◽  
System Optimization ◽  
Optimization Method ◽  
Pi Controller ◽  
Process Industries ◽  
Inference System ◽  
Inference Systems ◽  
Neural Fuzzy

A new technique of AFIS speed control of DC drives with Asymmetrical Half-Bridge converter is proposed. The PI controller is the most common feedback controller used in the process industries. PI is easily understood algorithm, which give good control action of varied dynamic characteristics. However, PI controller have drawback of not giving the optimum response for non-linear systems. By the introduction of novel intelligent techniques, the PI controller and FLC are optimized by Adaptive Neural Fuzzy Inference Systems. In this paper the ANFIS optimization method is applied for speed control of DC to DC converter fed drive. The main motive of this work is to attain minimum transient and switching losses to decrease the energy loss by which the efficiency gets increased.

A new congruency-based hysteresis modeling and compensating of a piezoactuator incorporating an adaptive neuron fuzzy inference system

2015 ◽  
Vol 231 (9) ◽  
pp. 1712-1724 ◽  
Author(s):  
Phuong-Bac Nguyen ◽  
Seung-Bok Choi ◽  
Byung-Keun Song
Keyword(s):  
Fuzzy Inference ◽  
Open Loop ◽  
Inference System ◽  
New Approach ◽  
Loop Control ◽  
First Order ◽  
Minor Loop ◽  
Hysteresis Modeling ◽  
Adaptive Neuron ◽  
Relationship Of

This paper proposes a new approach to modeling and compensating for a rate-independent hysteresis of a piezoactuator. The model—namely, congruency-based hysteresis—is developed based on two very important characteristics of the hysteresis. These are congruency and wipe-out. The proposed approach consists of two branches for cases of monotonic increase and monotonic decrease of input excitation. In order to realize this model, datasets of first-order minor-loop values should be determined in advance. This can be done using the adaptive neuron fuzzy system (ANFIS) technique and experimental data. With this technique, an input-output relationship of first-order minor-loop values is estimated effectively. In addition, the ANFIS technique is also used in constructing datasets of inverse first-order minor-loop values, which are essential parts of a congruency-based hysteresis compensator. Several experiments in modeling and open-loop control are conducted to show the effectiveness of the proposed approach. In addition, a comparative work between the proposed approach and one of previous works is undertaken to demonstrate the benefit of the proposed method.

The hysteresis modeling of pneumatic artificial muscles using least squares support vector machine approach

2020 ◽  
Vol 31 (11) ◽  
pp. 1358-1370
Author(s):  
Shenglong Xie ◽  
Guoying Ren
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Hysteresis Loops ◽  
Support Vector ◽  
Artificial Muscles ◽  
Machine Model ◽  
Modeling Errors ◽  
Hysteresis Modeling ◽  
Pneumatic Artificial Muscles ◽  
Control Accuracy

Reducing the modeling errors of hysteresis model is of great significance for improving the control accuracy of pneumatic artificial muscles. However, the asymmetry and complexity of its hysteresis loops limit the effect of existing modeling approach, especially for the irregular loops. This article extends the least squares support vector machine approach to the domain of asymmetric and irregular hysteresis characterization for pneumatic artificial muscles. Compared with the established hysteresis models, the significance of this approach is that it does not depend on the shapes of hysteresis loops and possess the advantages of both few identification parameters and high accuracy. The effectiveness and advantage of the presented model is compared with modified symmetric generalized Prandtl–Ishlinskii model. In addition, the length–pressure hysteresis experiment and modeling comparison on both of Festo commercial and self-made pneumatic artificial muscles are presented. The modeling errors of least squares support vector machine model for both loops are suppressed to a negligible level, and are not affected by the shape and complexity of hysteresis loops, which validates the effectiveness of this approach.

scholarly journals Performance Enhancement of a Single-Stage CUK Based Three Phase Photovoltaic Inverter using Anfis Controller

2019 ◽  
Vol 8 (11) ◽  
pp. 2682-2688
Keyword(s):  
Performance Enhancement ◽  
Fuzzy Inference ◽  
Fuzzy Controller ◽  
Pi Controller ◽  
Single Stage ◽  
Pv System ◽  
Inference System ◽  
Pv Inverter ◽  
Three Phase ◽  
High Efficient

A modular structured and high efficient photovoltaic (PV) system is essential in today’s scenario. The single stage Cuk based inverter has continuous input and output current, and hence, makes it suitable for applying MPPT techniques when used for PV applications. The PI, PID, and fuzzy controllers could be applied for PV inverter. The PI controller decreases the error in steady state, and at the same time, it also decreases the stability of the system. The PID controller involves large time delay process. The random nature in fuzzy controller may not lead to optimum results. Hence, this paper proposes a controller based on Adaptive Neuro-Fuzzy Inference System (ANFIS) for a three phase PV inverter based on Cuk converter. The effectiveness of proposed system is verified using MATLAB/SIMULINK, and the results are presented. The performance of proposed ANFIS controller for Cuk based three phase inverter is compared with conventional PI controller. The proposed system has several merits like increased performance, accuracy, and efficiency.

scholarly journals Implementation of ANFIS based controller on IG wind farm for improved performance

2019 ◽  
Vol 8 (2) ◽  
pp. 94
Author(s):  
Pragya Sharma ◽  
Satbir Singh ◽  
Vivek Pahwa
Keyword(s):  
Time Domain ◽  
Wind Farm ◽  
Fuzzy Inference ◽  
Pi Controller ◽  
Active Power ◽  
Induction Generator ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Improved Performance ◽  
Phasor Analysis

In this paper, the transient performance of a 9-MW Induction Generator (IG) wind farm is improved by implementing an Adaptive Neuro Fuzzy Inference System (ANFIS) based controller on the turbine in MATLAB/SIMULINK environment using phasor analysis. Initially, the parameters of PI controller is developed using conventional method. Then, with the help of PI controller, the ANFIS based controller is trained. This developed controller reduces peak overshoot and settling time of active power and torque-speed characteristics in contrast to PI controller. Further, the system is linearized and the obtained results in time domain have been validated for stability by using Pole-Zero plot.

Hysteresis Modeling with Adaptive Neuro-Fuzzy Inference System

2008 ◽  
Vol 372 (1) ◽  
pp. 54-65 ◽  
Author(s):  
M. Mordjaoui ◽  
M. Chabane ◽  
B. Boudjema ◽  
R. Daira
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Hysteresis Modeling
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