Analyzing large dynamic set-point change tracking of MRAC by exploiting fuzzy logic based automatic gain tuning

Karthikeyan, R.; Yadav, Rahul Kumar; Tripathi, Shikha; G, Hemanth kumar.

doi:10.1109/icsgrc.2012.6287139

Cited by 5 publications

(6 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It applies the MRAC scheme [20][21][22] to real-world systems. For the stability analysis of the system by the MIT rule, we needed a loss function J, often known as the cost function, which may be illustrated using [23][24][25][26][27],…”

Section: Mit Rulementioning

confidence: 99%

Investigations on MIT and Lyapunov rule-based modified MRAC for noninteracting and interacting two-tank coupled systems

Gupta,

Kumar,

Kumar

2024

FME Transactions

View full text Add to dashboard Cite

The adaptive control method is a technique that measures the dynamic characteristics of the plant automatically and continuously to make a comparison with its required output. It utilizes the difference between plant output and reference output to compute adaptable system parameters to maintain optimal performance regardless of the system variations. The behavior of the adaptation rule is significantly affected by the adaptation gain value. This paper describes the design of MIT (Massachusetts Institute of Technology) and Lyapunov rule-based modified Model Reference Adaptative Controller (MRAC) to stabilize a non-interacting and interacting two-tank process system. Investigation of variation in adaptation gain has also been done. Initially, a traditional MIT and Lyapunov rule-based MRAC is designed to stabilize the non-interacting and interacting tow-tank coupled system and it is found that both the systems are stable only for a few values of adaptation gain. To overcome this problem the modified MRAC is planned to stabilize and improve the response of the system. The modified MRAC scheme is just the PD (Proportional Derivative) controller superimposed on the MRAC control method. Now with the modified MRAC, the systems have been stabilized and their response has been improved for the wide range of adaptation gains. The comparative analysis of traditional and modified MRAC has also been presented. The performance analysis in terms of rise time, settling time, and peak overshoot has been carried out by comparing the results obtained for all the mentioned rules with the variations in the adaptation gain, on the MATLAB/Simulink platform. The obtained results present encouraging outcomes.

show abstract

Section: Mit Rulementioning

confidence: 99%

Investigations on MIT and Lyapunov rule-based modified MRAC for noninteracting and interacting two-tank coupled systems

Gupta,

Kumar,

Kumar

2024

FME Transactions

View full text Add to dashboard Cite

show abstract

“…It applies the MRAC scheme (Mukherjee et al, 2018b;Sethi et al, 2017) to real-world systems. For the stability analysis of the system by MIT rule, we needed a loss function J, often known as the cost function, which may be illustrated using (Fan and Kobayashi, 1998;Karthikeyan et al, 2012;Mfoumboulou 2021;Rothe et al, 2020;Zareh and Soheili 2011) where e is the output error, which may be considered as the difference between the output of the plant and the output of the reference model, and u (i.e. u 1 and u 2 ) is the regulating parameter that is generally recognized as the control parameter.…”

Section: Mit Rulementioning

confidence: 99%

Section: Mit Rulementioning

confidence: 99%

Effect of adaptation gain and reference model in MIT and Lyapunov rule–based model reference adaptive control for first- and second-order systems

Gupta,

Kumar,

Giri

2023

Transactions of the Institute of Measurement and Control

View full text Add to dashboard Cite

An adaptive control law encompasses a regulating control rule compensating for system dynamics variations by adjusting the controller characteristics to maintain the overall system performance. Recently, some techniques have been developed based on fundamental aspects of the adaptability of living organisms. The adaptive control method is a technique that measures the dynamic characteristics of the plant automatically and continuously to make a comparison with its required output. It utilizes the difference between these two to commute adaptable system parameters to maintain optimal performance regardless of the system variations. The behavior of the adaptation rule is significantly affected by the adaptation gain value. Here, it has also been investigated that the adaptation gain range is wide for the systems with the lower order. The appropriate range of adaptation gain decreases as the order of the system increases. In the present work, the model reference adaptive control (MRAC) for first- and second-order systems has been designed and investigated using a wide range of values for the adaptation gain and variations in the reference model parameters. The MIT (Massachusetts Institute of Technology) and Lyapunov rules are applied for the analyses of systems. On the MATLAB/Simulink platform, all the adaptation process comparisons, variations, and investigations have been carried out by altering the adaptation gain and the reference model parameters. The obtained results present encouraging outcomes.

show abstract

“…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%

“…When the details of the step response are examined in Figure 8, it is observed that the overshoot is very high despite the fast response of the PID; the response is slow with a constant adaptation speed and less overshoot and fast response in the fuzzy MRAC approach. Obtained overshoot and settling time information as well as ISE in (27) and ICSC in (28) values are given in Table 2. The Lyapunov fuzzy MRAC approach shows a fast response 140 s settling time and smaller ISE and ICSC values.…”

mentioning

confidence: 99%

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

2022

View full text Add to dashboard Cite

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.

show abstract

Analyzing large dynamic set-point change tracking of MRAC by exploiting fuzzy logic based automatic gain tuning

Cited by 5 publications

References 5 publications

Investigations on MIT and Lyapunov rule-based modified MRAC for noninteracting and interacting two-tank coupled systems

Investigations on MIT and Lyapunov rule-based modified MRAC for noninteracting and interacting two-tank coupled systems

Effect of adaptation gain and reference model in MIT and Lyapunov rule–based model reference adaptive control for first- and second-order systems

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

Contact Info

Product

Resources

About