A Fuzzy-Based Modified Gain Adaptive Scheme for Model Reference Adaptive Control

Pal, A. K.; Naskar, Indrajit; Paul, Sampa

doi:10.1007/978-981-10-7563-6_33

Cited by 3 publications

(4 citation statements)

References 9 publications

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“…The mathematical model of a system whose system characteristics are constantly changing can be created continuously with the data collected from the process and system identification methods, and this model can be controlled by model predictive controllers [23]. MRACs, which are the subject of this study, can also be used where the classical PID controller is insufficient [30].…”

Section: Blast Furnace Gas Cleaning Tank Level Control Systemmentioning

confidence: 99%

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Level Control of Blast Furnace Gas Cleaning Tank System with Fuzzy Based Gain Regulation for Model Reference Adaptive Controller

2022

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Iron making processes and automation systems are mostly controlled by logical rules and PID controllers. The dynamic behavior of these processes varies due to factors such as raw materials, outdoor conditions, and equipment aging. Changes in system dynamics necessitate re-determination of PID controller parameters. Model reference adaptive controllers (MRACs) are used in many industrial application areas with their adaptability to variable conditions. In this study, an MRAC is applied in the gas cleaning tank system level control problem in the blast furnace facility, which is at the center of the iron making processes. In addition, fuzzy based gain regulation is proposed to improve MRAC performance. MRAC and PID controller system control results are observed and compared. The fast response and adaptation performance of the proposed fuzzy MRAC approach along with external disturbance effects are analyzed. Fuzzy based gain regulation MRAC performances show better performance especially in level change as well as disturbance effect.

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Section: Blast Furnace Gas Cleaning Tank Level Control Systemmentioning

confidence: 99%

“…The X-15 research aircraft performed 199 missions over 10 years [26]. Over the years, MRAC has started to be used in solving real-world adaptive control problems such as aerospace, robot arms, and industrial process control [27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Level Control of Blast Furnace Gas Cleaning Tank System with Fuzzy Based Gain Regulation for Model Reference Adaptive Controller

2022

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“…It is well known that there is no fixed methodology or neither any well-established criterion that can be followed while tuning an FLPDC. For the betterment of the controlling experience of FLPDC, a new adaptive tuning scheme is proposed [27][28][29]. This scheme generates an additional corrective signal (1+k|e|) at every instant, which coalesces with the FLPDC output "Y' and collectively controls the O 2 supply as shown in Fig.…”

Section: Development Of the Fuzzy Logic Pd Controllermentioning

confidence: 99%

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

Naskar¹,

Pal²

2022

J. Sci. Res.

Self Cite

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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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“…However, in practical application, due to the interference of various external factors and limitations of the modeling model and parameters of the system, the precision of the position control of the valve-controlled cylinder is often difficult to meet the requirements [1,2]. Based on this, to achieve higher position control accuracy, relevant scholars have applied control methods, such as adaptive control, fuzzy control, variable structure control, and intelligent control algorithms, to valve-controlled cylinder position servo systems [3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Research on High-Precision Position Control of Valve-Controlled Cylinders Based on Variable Structure Control

Gao,

Zhang,

Zheng

et al. 2023

Machines

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During the operation of valve-controlled cylinder position systems, there are some problems, such as uncertainty and time-varying of the system parameters, imprecise modeling, and external interference. These problems will affect the position loading accuracy and safety reliability of the electro-hydraulic position servo system. This paper used the position servo system of a valve-controlled cylinder as the research object, and a mathematical model was established. Then, a sliding mode variable structure controller and dynamic soft variable structure controller were designed, and the Grey Wolf algorithm was used to adjust the controller parameters. The control effect of the two controllers in the valve-controlled cylinder system was verified with a simulation and experiment. The results showed that compared with the sliding mode variable structure controller, the dynamic soft variable structure controller had higher control precision and better stability and avoided the chattering phenomenon.

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A Fuzzy-Based Modified Gain Adaptive Scheme for Model Reference Adaptive Control

Cited by 3 publications

References 9 publications

Level Control of Blast Furnace Gas Cleaning Tank System with Fuzzy Based Gain Regulation for Model Reference Adaptive Controller

Level Control of Blast Furnace Gas Cleaning Tank System with Fuzzy Based Gain Regulation for Model Reference Adaptive Controller

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

Research on High-Precision Position Control of Valve-Controlled Cylinders Based on Variable Structure Control

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