Force tracking control for electrohydraulic servo system based on adaptive neuro-fuzzy inference system (ANFIS) controller

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Lie Yu ◽  
Lei Ding ◽  
Fangli Yu ◽  
Jianbin Zheng ◽  
Yukang Tian
Keyword(s):  
Tracking Control ◽  
Fuzzy Inference ◽  
Sinusoidal Signal ◽  
Inference System ◽  
Content Type ◽  
Signal Tracking ◽  
Neuro Fuzzy ◽  
Actual Force ◽  
Force Tracking ◽  
Electrohydraulic Servo System

PurposeThe purpose of this paper is to apply a intelligent algorithm to conduct the force tracking control for electrohydraulic servo system (EHSS). Specifically, the adaptive neuro-fuzzy inference system (ANFIS) is selected to improve the control performance for EHSS.Design/methodology/approachTwo types of input–output data were chosen to train the ANFIS models. The inputs are the desired and actual forces, and the output is the current. The first type is to set a sinusoidal signal for the current to produce the actual driving force, and the desired force is chosen as same as the actual force. The other type is to give a sinusoidal signal for the desired force. Under the action of the PI controller, the actual force tracks the desired force, and the current is the output of the PI controller.FindingsThe models built based on the two types of data are separately named as the ANFIS I controller and the ANFIS II controller. The results reveal that the ANFIS I controller possesses the best performance in terms of overshoot, rise time and mean absolute error and show adaptivity to different tracking conditions, including sinusoidal signal tracking and sudden change signal tracking.Originality/valueThis paper is the first time to apply the ANFIS to optimize the force tracking control for EHSS.

Structure identification and IO space partitioning in a nonlinear fuzzy system for prediction of patient survival after surgery

2017 ◽  
Vol 10 (2) ◽  
pp. 166-182 ◽  
Author(s):  
Shabia Shabir Khan ◽  
S.M.K. Quadri
Keyword(s):  
Pancreatic Cancer ◽  
Fuzzy Inference System ◽  
Fuzzy System ◽  
Fuzzy Inference ◽  
Patient Survival ◽  
Data Partitioning ◽  
Structure Identification ◽  
Inference System ◽  
Content Type ◽  
Neuro Fuzzy

Purpose As far as the treatment of most complex issues in the design is concerned, approaches based on classical artificial intelligence are inferior compared to the ones based on computational intelligence, particularly this involves dealing with vagueness, multi-objectivity and good amount of possible solutions. In practical applications, computational techniques have given best results and the research in this field is continuously growing. The purpose of this paper is to search for a general and effective intelligent tool for prediction of patient survival after surgery. The present study involves the construction of such intelligent computational models using different configurations, including data partitioning techniques that have been experimentally evaluated by applying them over realistic medical data set for the prediction of survival in pancreatic cancer patients. Design/methodology/approach On the basis of the experiments and research performed over the data belonging to various fields using different intelligent tools, the authors infer that combining or integrating the qualification aspects of fuzzy inference system and quantification aspects of artificial neural network can prove an efficient and better model for prediction. The authors have constructed three soft computing-based adaptive neuro-fuzzy inference system (ANFIS) models with different configurations and data partitioning techniques with an aim to search capable predictive tools that could deal with nonlinear and complex data. After evaluating the models over three shuffles of data (training set, test set and full set), the performances were compared in order to find the best design for prediction of patient survival after surgery. The construction and implementation of models have been performed using MATLAB simulator. Findings On applying the hybrid intelligent neuro-fuzzy models with different configurations, the authors were able to find its advantage in predicting the survival of patients with pancreatic cancer. Experimental results and comparison between the constructed models conclude that ANFIS with Fuzzy C-means (FCM) partitioning model provides better accuracy in predicting the class with lowest mean square error (MSE) value. Apart from MSE value, other evaluation measure values for FCM partitioning prove to be better than the rest of the models. Therefore, the results demonstrate that the model can be applied to other biomedicine and engineering fields dealing with different complex issues related to imprecision and uncertainty. Originality/value The originality of paper includes framework showing two-way flow for fuzzy system construction which is further used by the authors in designing the three simulation models with different configurations, including the partitioning methods for prediction of patient survival after surgery. Several experiments were carried out using different shuffles of data to validate the parameters of the model. The performances of the models were compared using various evaluation measures such as MSE.

Analyzing of the sensitivity of chip formation during machining process

Sensor Review ◽  
2017 ◽  
Vol 37 (4) ◽  
pp. 448-450 ◽  
Author(s):  
Srdjan Jovic ◽  
Dragan Lazarevic ◽  
Aleksa Vulovic
Keyword(s):  
Chip Formation ◽  
Design Methodology ◽  
Fuzzy Inference ◽  
Machining Process ◽  
Machining Parameters ◽  
Important Indicator ◽  
Inference System ◽  
Content Type ◽  
Neuro Fuzzy

Purpose The paper aims to analyze chip formation during machining process since it can be a very important indicator for the quality of the machining process, as some chip forms can be undesirable. Design/methodology/approach It is essential to determine the sensitivity of the chip formation on the basis of different machining parameters. The main goal of the study was to analyze the sensitivity of the chip formation during the machining process by using adaptive neuro-fuzzy inference system (ANFIS). Findings According to the results, the chip formation is the most sensitive to feed rate. Originality/value Different cutting tests were performed to monitor the chip formation on the basis of the cutting forces and the cutting displacement. ANFIS was used to estimate the sensitivity of the chip formation during the cutting process on the basis of different parameters.

Socioeconomic analysis of infectious diseases based on different scenarios using uncertain SEIAR system dynamics with effective subsystems and ANFIS

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Zeinab Rahimi Rise ◽  
Mohammad Mahdi Ershadi
Keyword(s):  
Infectious Diseases ◽  
System Dynamics ◽  
Fuzzy Inference ◽  
Healthcare Systems ◽  
System Dynamics Model ◽  
Dynamics Model ◽  
Inference System ◽  
Content Type ◽  
Neuro Fuzzy

PurposeThis paper aims to analyze the socioeconomic impacts of infectious diseases based on uncertain behaviors of social and effective subsystems in the countries. The economic impacts of infectious diseases in comparison with predicted gross domestic product (GDP) in future years could be beneficial for this aim along with predicted social impacts of infectious diseases in countries.Design/methodology/approachThe proposed uncertain SEIAR (susceptible, exposed, infectious, asymptomatic and removed) model evaluates the impacts of variables on different trends using scenario base analysis. This model considers different subsystems including healthcare systems, transportation, contacts and capacities of food and pharmaceutical networks for sensitivity analysis. Besides, an adaptive neuro-fuzzy inference system (ANFIS) is designed to predict the GDP of countries and determine the economic impacts of infectious diseases. These proposed models can predict the future socioeconomic trends of infectious diseases in each country based on the available information to guide the decisions of government planners and policymakers.FindingsThe proposed uncertain SEIAR model predicts social impacts according to uncertain parameters and different coefficients appropriate to the scenarios. It analyzes the sensitivity and the effects of various parameters. A case study is designed in this paper about COVID-19 in a country. Its results show that the effect of transportation on COVID-19 is most sensitive and the contacts have a significant effect on infection. Besides, the future annual costs of COVID-19 are evaluated in different situations. Private transportation, contact behaviors and public transportation have significant impacts on infection, especially in the determined case study, due to its circumstance. Therefore, it is necessary to consider changes in society using flexible behaviors and laws based on the latest status in facing the COVID-19 epidemic.Practical implicationsThe proposed methods can be applied to conduct infectious diseases impacts analysis.Originality/valueIn this paper, a proposed uncertain SEIAR system dynamics model, related sensitivity analysis and ANFIS model are utilized to support different programs regarding policymaking and economic issues to face infectious diseases. The results could support the analysis of sensitivities, policies and economic activities.Highlights:A new system dynamics model is proposed in this paper based on an uncertain SEIAR model (Susceptible, Exposed, Infectious, Asymptomatic, and Removed) to model population behaviors;Different subsystems including healthcare systems, transportation, contacts, and capacities of food and pharmaceutical networks are defined in the proposed system dynamics model to find related sensitivities;Different scenarios are analyzed using the proposed system dynamics model to predict the effects of policies and related costs. The results guide lawmakers and governments' actions for future years;An adaptive neuro-fuzzy inference system (ANFIS) is designed to estimate the gross domestic product (GDP) in future years and analyze effects of COVID-19 based on them;A real case study is considered to evaluate the performances of the proposed models.

Making decision toward overseas construction projects

2019 ◽  
Vol 26 (2) ◽  
pp. 285-302 ◽  
Author(s):  
Wahyudi P. Utama ◽  
Albert P.C. Chan ◽  
Hafiz Zahoor ◽  
Ran Gao ◽  
Dwifitra Y. Jumas
Keyword(s):  
Decision Support ◽  
Construction Projects ◽  
Fuzzy Inference ◽  
Optimization Method ◽  
Preliminary Assessment ◽  
Anfis Model ◽  
Data Set ◽  
Inference System ◽  
Content Type ◽  
Neuro Fuzzy

Purpose The purpose of this paper is to introduce a decision support aid for deciding an overseas construction project (OCP) using an adaptive neuro fuzzy inference system (ANFIS). Design/methodology/approach This study presents an ANFIS approach as a decision support aid for assessment of OCPs. The processing data were derived from 110 simulation cases of OCPs. In total, 21 international factors observed from a Delphi survey were determined as assessment variables to examine the cases. The experts were involved to evaluate and judge whether the company should Go or Not Go for an OCP, based on the different parameter scenarios given. To measure the performance of the ANFIS model, root mean square error (RMSE) and coefficient of correlation (R) were employed. Findings The result shows that optimum ANFIS model indicating RMSE and R scores adequately near between 0 and 1, respectively, was obtained from parameter set of network algorithm with two input membership functions, Gaussian type of membership function and hybrid optimization method. When the model tested to nine real OCPs data, the result indicates 88.89 percent accurate. Research limitations/implications The use of simulation cases as data set in development the model has several advantages. This technique can be replicated to generate other case scenarios which are not available publicly or limited in terms of quantity. Originality/value This study evidences that the developed ANFIS model can predict the decision satisfactorily. Therefore, it can help companies’ management to make preliminary assessment of an OCP.

A mutated FastSLAM using soft computing

2017 ◽  
Vol 44 (4) ◽  
pp. 416-427 ◽  
Author(s):  
Ramazan Havangi
Keyword(s):  
Kalman Filter ◽  
Soft Computing ◽  
Pose Estimation ◽  
Design Methodology ◽  
Fuzzy Inference ◽  
Unscented Kalman Filter ◽  
Inference System ◽  
Content Type ◽  
Localization And Mapping ◽  
Neuro Fuzzy

Purpose Simultaneous localization and mapping (SLAM) is the problem of determining the pose (position and orientation) of an autonomous robot moving through an unknown environment. The classical FastSLAM is a well-known solution to SLAM. In FastSLAM, a particle filter is used for the robot pose estimation, and the Kalman filter (KF) is used for the feature location’s estimation. However, the performance of the conventional FastSLAM is inconsistent. To tackle this problem, this study aims to propose a mutated FastSLAM (MFastSLAM) using soft computing. Design/methodology/approach The proposed method uses soft computing. In this approach, particle swarm optimization (PSO) estimator is used for the robot’s pose estimation and an adaptive neuro-fuzzy unscented Kalman filter (ANFUKF) is used for the feature location’s estimation. In ANFUKF, a neuro-fuzzy inference system (ANFIS) supervises the performance of the unscented Kalman filter (UKF) with the aim of reducing the mismatch between the theoretical and actual covariance of the residual sequences to get better consistency. Findings The simulation and experimental results indicate that the consistency and estimated accuracy of the proposed algorithm are superior FastSLAM. Originality/value The main contribution of this paper is the introduction of MFastSLAM to solve the problems of FastSLAM.

Use of adaptive neuro-fuzzy inference system and gene expression programming methods for estimation of the bearing capacity of rock foundations

2018 ◽  
Vol 35 (5) ◽  
pp. 2078-2106 ◽  
Author(s):  
Ehsan Sadrossadat ◽  
Behnam Ghorbani ◽  
Rahimzadeh Oskooei ◽  
Mahdi Kaboutari
Keyword(s):  
Gene Expression ◽  
Bearing Capacity ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Gene Expression Programming ◽  
Inference System ◽  
Indirect Estimation ◽  
Content Type ◽  
Rock Foundations ◽  
Neuro Fuzzy

Purpose This study aims to examine the potential of two artificial intelligence (AI)-based algorithms, namely, adaptive neuro-fuzzy inference system (ANFIS) and gene expression programming (GEP), for indirect estimation of the ultimate bearing capacity (qult) of rock foundations, which is a considerable civil and geotechnical engineering problem. Design/methodology/approach The input-processing-output procedures taking place in ANFIS and GEP are represented for developing predictive models. The great importance of simultaneously considering both qualitative and quantitative parameters for indirect estimation of qult is taken into account and explained. This issue can be considered as a remarkable merit of using AI-based approaches. Furthermore, the evaluation procedure of various models from both engineering and accuracy viewpoints is also demonstrated in this study. Findings A new and explicit formula generated by GEP is proposed for the estimation of the qult of rock foundations, which can be used for further engineering aims. It is also presented that although the ANFIS approach can predict the output with a high degree of accuracy, the obtained model might be a black-box. The results of model performance analyses confirm that ANFIS and GEP can be used as alternative and useful approaches over previous methods for modeling and prediction problems. Originality/value The superiorities and weaknesses of GEP and ANFIS techniques for the numerical analysis of engineering problems are expressed and the performance of their obtained models is compared to those provided by other approaches in the literature. The findings of this research provide the researchers with a better insight to using AI techniques for resolving complicated problems.

Measuring maintenance activity effectiveness

2018 ◽  
Vol 24 (4) ◽  
pp. 437-448 ◽  
Author(s):  
Anil Rana ◽  
Emosi V.M. Koroitamana
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Multiple Attribute Decision Making ◽  
Performance Measure ◽  
Inference System ◽  
Content Type ◽  
Maintenance Activity ◽  
Expert Opinions ◽  
Neuro Fuzzy ◽  
Multiple Attribute

Purpose The purpose of this paper is to provide a framework for measuring the imprecise and subjective “effectiveness” of a major maintenance activity. Such a measure will not only bring objectivity in gauging the effectiveness of maintenance task carried out by the workforce without any intervention from an expert but also help in measuring the slow degradation of the performance of the concerned major equipment/system. Design/methodology/approach The paper follows a three-step approach. First, identify a set of parameters considered important for estimating the maintenance activity effectiveness. Second, generate a set of data using expert opinions on a fuzzy performance measure of maintenance activity effectiveness (output). Also, find an aggregated estimate of the effectiveness by analysing the consensus among experts. This requires using a part of the “fuzzy multiple attribute decision making” process. Finally, train a neuro-fuzzy inference system based on input parameters and generated output data. Findings The paper analysed major maintenance activity carried out on diesel engines of a power plant company. Expert opinions were used in selection of key parameters and generation of output (effectiveness measure). The result of a trained adaptive neuro-fuzzy inference system (ANFIS) matched acceptably well with that aggregated through the expert opinions. Research limitations/implications In view of unavailability of data, the method relies on training a neuro-fuzzy system on data generated through expert opinion. The data as such are vague and imprecise leading to lack of consensus between experts. This can lead to some amount of error in the output generated through ANFIS. Originality/value The originality of the paper lies in presentation of a method to estimate the effectiveness of a maintenance activity.

Contact positions estimation of sensing structure using adaptive neuro-fuzzy inference system

Kybernetes ◽  
2014 ◽  
Vol 43 (5) ◽  
pp. 783-796 ◽  
Author(s):  
Dalibor Petkovic ◽  
Mirna Issa ◽  
Nenad D. Pavlovic ◽  
Lena Zentner ◽  
Md Nor Ridzuan Daud ◽  
...  
Keyword(s):  
Carbon Black ◽  
Silicone Rubber ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Tactile Sensing ◽  
Inference System ◽  
Content Type ◽  
Sensing Material ◽  
Neuro Fuzzy ◽  
Current Contact

Purpose – Tactile sensing is the process of determining physical properties and events through contact with objects in the world. The purpose of this paper is to establish a novel design of an adaptive neuro-fuzzy inference system (ANFIS) for estimation of contact position of a new tactile sensing structure. Design/methodology/approach – The major task is to investigate implementations of carbon-black-filled silicone rubber for tactile sensation; the silicone rubber is electrically conductive and its resistance changes by loading or unloading strains. Findings – The sensor-elements for the tactile sensing structure were made by press-curing from carbon-black-filled silicone rubber. The experimental results can be used as training and checking data for the ANFIS network. Originality/value – This system is capable to find any change of contact positions and thus indicates state of the current contact location of the tactile sensing structure. The behavior of the use silicone rubber shows strong non-linearity, therefore, the sensor cannot be used for high accurate measurements. The greatest advantage of this sensing material lies in its high elasticity.

Modelling and validation of magneto-rheological fluid damper behaviour under impact loading using interpolated multiple adaptive neuro-fuzzy inference system

2020 ◽  
Vol 16 (6) ◽  
pp. 1395-1415
Author(s):  
Mohd Sabirin Rahmat ◽  
Khisbullah Hudha ◽  
Zulkiffli Abd Kadir ◽  
Noor Hafizah Amer ◽  
Muhammad Luqman Hakim Abd Rahman ◽  
...  
Keyword(s):  
Impact Loading ◽  
Fuzzy Inference ◽  
Control Strategies ◽  
Modelling Technique ◽  
Inference System ◽  
Content Type ◽  
Neuro Fuzzy ◽  
Fluid Damper ◽  
Magneto Rheological ◽  
Experimental Characterisation

PurposeThe objective of this paper is to develop a fast modelling technique for predicting magneto-rheological fluid damper behaviour under impact loading applications.Design/methodology/approachThe adaptive neuro-fuzzy inference system (ANFIS) technique was adopted to predict the behaviour of a magneto-rheological fluid (MRF) damper through experimental characterisation data. In this study, an MRF damper manufactured by Lord Corporation was used for characterisation using an impact pendulum test rig. The experimental characterisation was carried out with various impact energies and constant input currents applied to the MRF damper.FindingsThis research provided a fast modelling technique with relatively less error in predicting MRF damper behaviour for the development of control strategies. Accordingly, the ANFIS model was able to predict MRF damper behaviour under impact loading and showed better performance than the modified Bouc–Wen model.Research limitations/implicationsThis study only focused on modelling technique for a single type of MRF damper used for impact loading applications. It is possible for other applications, such as cyclic loading, random loadings and system identification, to be studied in future experiments.Original/ValueFuture researchers could apply the ANFIS model as an actuator model for the development of control strategies and analyse the control performance. The model also can be replicated in other industries with minor modifications to suit different needs.

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