Design a Fuzzy Logic Controller with a Non-fuzzy Tuning Scheme for Swing up and Stabilization of Inverted Pendulum

Pal, A. K.; Chakrabarty, J.

doi:10.1007/978-81-322-2012-1_23

Cited by 2 publications

(2 citation statements)

References 8 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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“…Adaptation methods (Fallahi, et al, 2009); 2. Artificial methods such as fuzzy logic control (Pal, et al, 2015), neural network (Figueredo & Gomez, 2009), Genetic Algorithm (Metni, 2009) and PSO (Solihin, et al, 2009); 3. Sliding mode control (Park, et al, 2009); 4.…”

Section: Control Strategymentioning

confidence: 99%

Stabilizing and Swinging-Up the Inverted Pendulum Using PI and PID Controllers Based on Reduced Linear Quadratic Regulator Tuned by PSO

Mousa¹,

Ebrahim

Hassan

2015

International Journal of System Dynamics Applications

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The inherited instabilities in the Inverted Pendulum (IP) system make it one of the most difficult nonlinear problems in the control theory. In this research work, Proportional –Integral and Derivative (PID) Controller with a feed forward gain is used with Reduced Linear Quadratic Regulator (RLQR) for stabilizing the Cart Position and Swinging-up the Pendulum angle. Tuning the Controllers' gains is achieved by using Particle Swarm Optimization (PSO) Technique. Obtaining the combined PID controllers' gains with a feed forward gain and RLQR is a multi-dimensions control problem. The Proposed Controllers give minimum Settling Time, Rise Time, Undershoot and Over shoot for both the Cart Position and the Pendulum angle. A disturbance with different amplitudes is applied to the system, and the results showed the robustness of the systems based on the tuned controllers. The overall results are promising.

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Design a Fuzzy Logic Controller with a Non-fuzzy Tuning Scheme for Swing up and Stabilization of Inverted Pendulum

Cited by 2 publications

References 8 publications

System Modeling and Prediction of Respiratory Diseases using Machine Learning

System Modeling and Prediction of Respiratory Diseases using Machine Learning

Stabilizing and Swinging-Up the Inverted Pendulum Using PI and PID Controllers Based on Reduced Linear Quadratic Regulator Tuned by PSO

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