A Non-Fuzzy Self-Tuning Scheme of PD-Type FLC for Overhead Crane Control

Pal, A. K.; Mudi, Rajani K.; Maity, Ritu Rani De

doi:10.1007/978-3-642-35314-7_5

Cited by 9 publications

(3 citation statements)

References 12 publications

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“…The derived model is validated in real time by spirometry test in the laboratory and the model transfer function (TFR) is derived using MATLAB System Identification Toolbox [14][15][16].…”

Section: Validation Of the Respiratory Modelmentioning

confidence: 99%

System Modeling and Prediction of Respiratory Diseases using Machine Learning

Paul¹,

Naskar²,

Pal³

2022

YMER

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In the first part of this paper a human respiratory model and their responses are presented and in the second part machine learning algorithm is used to predict the respiratory diseases from the datasets. This endeavor proposes a simple representation of the mathematical model of the human respiratory system comprises of nasal cavity, trachea, bronchi and alveolar sacs. Ranging from a very common disease called bronchitis to highly perilous diseases like emphysema are considered in this venture and the variation of responses are observed with input pressure. In the conventional computation and control the system modeling is very much essential for analysis, whereas, machine learning algorithms use computational methods to learn information directly from data without relying on a predetermined equation as a model. Logistic regression algorithm is applied to find natural patterns in respiratory data that generate insight and help to make better decision and prediction of respiratory diseases. The Logistic regression model makes a clear decision boundary between the respiratory diseases. Keywords: Bronchitis, Emphysema, Logistic regression, Mathematical model of the human respiratory system.

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“…The derived model is validated in real time by spirometry test in the laboratory and the model transfer function (TFR) is derived using MATLAB System Identification Toolbox [14][15][16].…”

Section: Validation Of the Respiratory Modelmentioning

confidence: 99%

System Modeling and Prediction of Respiratory Diseases using Machine Learning

Paul¹,

Naskar²,

Pal³

2022

YMER

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“…As is demonstrated in the diagram (Fig.8), an online gain modifier (İ) is determined from the relation, Here, İ is the on-line adjustable parameter for the output SF G u , and į is a positive constant for appropriate modification in İ. Thus, in the proposed scheme, the output SF is continuously modified by the combined effect of a single deterministic rule defined on normalized change of error [8] of the system and the corresponding set-point weighed factor. This is expected to improve the close-loop performance of the system in addition to setpoint following, since it incorporates the dynamics of the process.…”

Section: Controller Designmentioning

confidence: 99%

Adaptive Fuzzy Control of Inverted Pendulum with a Fuzzy-Based Set-Point Weighting Scheme

Pal¹,

Chakrabarty²

2014

2014 Fourth International Conference of Emerging Applications of Information Technology

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This paper presents a solution to the control problem of inverted pendulum, in presence of model uncertainties due to its varying pendulum mass and length. Here, a fuzzy PD controller is proposed which is continuously tuned and updated by a non-fuzzy adaptive scheme. In addition to the proposed tuning scheme, a fuzzybased set-point weighing scheme is also incorporated for effective control in load variations. The application of the proposed fuzzy-based set-point weighting self-adaptive fuzzy PD controller (FSW-SAFPDC) stabilized the pendulum in an upright position in minimum time and the cart returned to its initial position.

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“…FLC is one the intelligent control that is suitable to be implemented in real-time system, and is commonly integrated with PID elements. As for example, PD-FLC is capable of rejecting the noise signal with minimum control tuning, such as reported in [6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Forkloader Position Control for A Mini Heavy Loaded Vehicle using Fuzzy Logic-Antiwindup Control

2017

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This paper presents a proposed integrated Takagi-Sugeno-Kang (TSK) type Fuzzy Logic control (TSK-FLC) with Antiwindup elements for a forkloader position control of a Mini Heavy Loaded Forklift Autonomous Guided Vehicle (MHeLFAGV). The study was carried out by modeling TSK-FLC as a closeloop control for the each axis of the fork-lift's movement. The degree of membership is designed with reference to the system response, in which ultrasonic sensor with 1cm resolution is used. Moreover, the rule base is determined and optimized to deal with microcontroller processing speed. In order to cater for the windup phenomenon, proportional and integrated antiwindup elements are integrated into the TSK-FLC model. This control strategy consumes less memory and is expected to increase the time response of the control system. The experiment and analysis is done on the actual forkloader unit of MHeLFAGV system. The experiment was done on the vertical axis motion since horizontal motion will have the same characteristic pattern of implementation and characteristic of tuning. The experiment shows that the proposed integrated TSK-FLC with antiwindup elements is able to speed up the time response of the system and eliminate the overshoot as well as oscillation on the forkloader movement.

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A Non-Fuzzy Self-Tuning Scheme of PD-Type FLC for Overhead Crane Control

Cited by 9 publications

References 12 publications

System Modeling and Prediction of Respiratory Diseases using Machine Learning

System Modeling and Prediction of Respiratory Diseases using Machine Learning

Adaptive Fuzzy Control of Inverted Pendulum with a Fuzzy-Based Set-Point Weighting Scheme

Forkloader Position Control for A Mini Heavy Loaded Vehicle using Fuzzy Logic-Antiwindup Control

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