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

Pal, A. K.; Chakrabarty, J.

doi:10.1109/eait.2014.53

Cited by 3 publications

(5 citation statements)

References 6 publications

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“…To overcome the shortcomings of the PD control scheme and also to maintain the oxygen level within the acceptable range, fuzzy logic is incorporated along with the PD control (FLPDC) scheme because it deals with qualitative aspects of a system rather than the quantitative features and also takes care of any sort of vagueness and ambiguity in the information [18,19].…”

Section: The Proposed Control Schemementioning

confidence: 99%

“…The PD-type FLC has been shown in the feedback path in Fig. 9 which works on two inputs, "error" (e) and "change of error" (∆e) [11,15,18,19]. Error is measured by comparing the output response of a healthy system with the output response of a pretentious system.…”

Section: Development Of the Fuzzy Logic Pd Controllermentioning

confidence: 99%

“…In comparison, the conventional PI, PD or PID control schemes require only two or three parameters to be tuned but their response while dealing with highly complex, non-linear systems is not satisfactory [16,17]. On the other hand, FLC thrives on the notions of imprecision, partial truth, ambiguity, inaccuracy, and vagueness to come to any sort of control conclusion [18,19], so, the use of FLC while dealing with the respiratory system is justified. This manuscript presents an Adaptive Fuzzy Logic PD Control technique that can be put to good use to control the adverse effect of breathlessness in an individual.…”

Section: Introductionmentioning

confidence: 99%

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Self Adaptive Fuzzy Controller for Supplementary Oxygen Supply to the Respiratory Distress Patients

Naskar¹,

Pal²

2022

J. Sci. Res.

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In this paper, respiratory irregularities of humans are investigated, and a self Adaptive Fuzzy Logic-based Proportional plus Derivative Controller (AFLPDC) is designed to provide a controlled supply of oxygen to the patient, suffering from breathlessness. The Fuzzy Logic-based Proportional plus Derivative Controller (FLPDC) is tuned, by implementing an adaptive scheme, which provides desired control action according to the process parameter variations. The electrical equivalent model of the respiratory system is developed, and the model is validated with the help of a Spirometer and MATLAB System Identification Toolbox. To build up this model, the respiratory tract is well thought out into four sections: the nasal cavity, trachea, bronchi, and finally, the alveolar sacs. Each of these sections is represented as a series of combinations of Resistance, Inductance, and Compliance. The control capability of the proposed AFLPDC is investigated on the derived respiratory model. The supply of the desired level of supplemental oxygen by AFLPDC to the patients suffering from the diseases like Bronchitis and Emphysema during respiratory dysfunction is observed in the MATLAB SIMULINK environment without human intervention. The response revealed by AFLPDC is compared with the responses of the conventional PD controller and the Fuzzy Logic-based PD controller (FLPDC).

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Section: The Proposed Control Schemementioning

confidence: 99%

Section: Development Of the Fuzzy Logic Pd Controllermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Self Adaptive Fuzzy Controller for Supplementary Oxygen Supply to the Respiratory Distress Patients

Naskar¹,

Pal²

2022

J. Sci. Res.

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“…More recent efforts have been made to take into account the states of the cart in this kind of scheme. In [6] an adaptive PD-like fuzzy controller is used with a good performance but only simulation results are presented and the stability of the algorithm is not proven. In [7], the authors present the study of dynamic response of the inverted pendulum in terms of classical control theory.…”

Section: Introductionmentioning

confidence: 99%

Heuristic Global Optimization of an Adaptive Fuzzy Controller for the Inverted Pendulum System: Experimental Comparison

Llama

Flores²,

Garcia-Hernandez

et al. 2020

Applied Sciences

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In this paper an adaptive fuzzy controller is proposed to solve the trajectory tracking problem of the inverted pendulum on a cart system. The designed algorithm is featured by not using any knowledge of the dynamic model and incorporating a full-state feedback. The stability of the closed-loop system is proven via the Lyapunov theory, and boundedness of the solutions is guaranteed. The proposed controller is heuristically tuned and its performance is tested via simulation and real-time experimentation. For this reason, a tuning method is investigated via evolutionary algorithms: particle swarm optimization, firefly algorithm and differential evolution in order to optimize the performance and verify which technique produces better results. First, a model-based simulation is carried out to improve the parameter tuning of the fuzzy systems, and then the results are transferred to real-time experiments. The optimization procedure is presented as well as the experimental results, which are also discussed.

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“…Inthispaper,thedifferentsimulationmodelsarestudiedalongwitharealtimeoverheadcrane. Theoverheadcranesaremainlyusedforthetransportofgoodsinports,coreindustriesandalso inrealestatesectors (Pal,Mudi,&DeMaity,2013).Themainissuewiththecranesystemisits pendulationsduringmovementduetoitsunderdampedandoscillatorynature (Pal,&Chakraborty, 2014;Pal&Chakraborty,2015;Sorensen,Singhose,&Dickerson,2007;Park,Chwa,&Hong,2008). Theperformanceoftheproposedset-pointmodulatedfuzzybasedMRAC(SFMRAC)iscompared withtheperformanceoffuzzyPDcontroller(FPDC),modelreferenceadaptivecontroller(MRAC) andconventionalPDcontrollerfortheswinganglecontrolofoverheadcrane.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy-based Gain Adaptive Scheme for Set-Point Modulated Model Reference Adaptive Controller

Pal¹,

Naskar²,

Paul³

2018

International Journal of Natural Computing Research

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In model reference adaptive controller (MRACs), the adaptive gain of the controller is varied according to the process dynamic variation as it is directly related with the system stability. In MRAC, there is no provision of an automatic selection of adaptive gain and adaptation rate. To get rid of this problem and for the automatic selection of adaptive gain, a fuzzy-based scheme is presented in this article. In the proposed fuzzy-based technique, the controller output gain is illustrated as the function of input process parameters, which is continuously amended for any process parameter variations. A set-point modulation scheme is also incorporated to tackle the undesired process parameter variations and to improve performance indices of the process under control. The performance of the proposed set-point modulated fuzzy-based model reference adaptive controller (SFMRAC) is demonstrated on different second order linear, marginally stable and nonlinear models. The scheme is also explored on a real-time twin arm overhead crane for transport of material without pendulation.

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

Cited by 3 publications

References 6 publications

Self Adaptive Fuzzy Controller for Supplementary Oxygen Supply to the Respiratory Distress Patients

Self Adaptive Fuzzy Controller for Supplementary Oxygen Supply to the Respiratory Distress Patients

Heuristic Global Optimization of an Adaptive Fuzzy Controller for the Inverted Pendulum System: Experimental Comparison

Fuzzy-based Gain Adaptive Scheme for Set-Point Modulated Model Reference Adaptive Controller

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