Enhanced Model Reference Adaptive Control Scheme for Tracking Control of Magnetic Levitation System

Rahul, S G; Srinivasan, S.; Panneerselvam, Kavitha; Teekaraman, Yuvaraja; Kuppusamy, Ramya; Urooj, Shabana

doi:10.3390/en14051455

Cited by 10 publications

(4 citation statements)

References 17 publications

(21 reference statements)

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“…This shows how the error is dependent on the adjustable parameter; there are many alternatives to choose the loss function F. For example, it can also be taken as a mode of error. Similarly, d /dt can also have different relations for different applications (Gopi et al 2021).…”

Section: Adaptation Mechanismmentioning

confidence: 99%

Modeling and control of boiler in thermal power plant using model reference adaptive control

Sowparnika,

Vijula

2024

The Scientific Temper

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The boiler is a multi-variable system, which is very difficult to control due to its nonlinear behavior, uncertainties, interactions between variables, and unmeasured and frequent disturbances. Instead of conventional control techniques, modern control techniques are being implemented in most boilers by industries. Mathematical modeling is a useful tool to analyze a complex system’s performance and design a controller for the same. The mathematical model is derived from the open-loop data obtained from the process station. The mathematical equation is then derived using the decoupling technique in terms of transfer function. An adaptive controller is designed and implemented for the model and the simulation study for the same is carried out using MATLAB. The proposed method discussed in the paper can adjust the controller parameters in response to changes in plant and disturbance in real-time by referring to the reference model that specifies the properties of the desired control system.

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Section: Adaptation Mechanismmentioning

confidence: 99%

Modeling and control of boiler in thermal power plant using model reference adaptive control

Sowparnika,

Vijula

2024

The Scientific Temper

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“…Initializing the optimization procedure in the ABC algorithm is also committed to the creation of several solutions or food sources at random by dispatching scout bees from colonies [29]. Similarly, because of its capacity to automatically respond to unknown disturbances and model uncertainties that occur in the system, adaptive controllers have gotten a lot of attention [30].…”

Section: Artificial Bee Colony Optimizationmentioning

confidence: 99%

Fuzzy Logic-Based Load Frequency Control in an Island Hybrid Power System Model Using Artificial Bee Colony Optimization

Kumar

Rahul

Kuppusamy³

et al. 2022

Energies

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This study presents the implementation of Artificial Bee Colony (ABC) optimization in an island hybrid power system model using fuzzy logic-based load frequency control. The Island Hybrid Power System considered in this study consisted of various generation units and an energy storage system. The optimized control parameters of PID using ABC were used in an intelligent fuzzy logic controller. The profiles (power & Frequency) of isolated hybrid power system were improved using a Super Conducting Magnetic Energy Storage (SMES) System. Individual controllers were used for wind turbine and diesel generators to control the power output for balancing the demand (frequency change control). Comparative analysis of power and frequency with the help of various classical and intelligent control configurations is presented. The outcome of the study shows that a minimum deviation in frequency and power is obtained through the proposed Intelligent Fuzzy Control approach for the considered isolated power system model.

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“…Therefore, it is logical to modify the adjustable parameter

in the direction of the negative gradient of

; that is [ 61 ],

…”

Section: Mrac Strategymentioning

confidence: 99%

Robust Adaptive HCS MPPT Algorithm-Based Wind Generation System Using Model Reference Adaptive Control

Alrowaili

Ali

Youssef

et al. 2021

Sensors

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To treat the stochastic wind nature, it is required to attain all available power from the wind energy conversion system (WECS). Therefore, several maximum power point tracking (MPPT) techniques are utilized. Among them, hill-climbing search (HCS) techniques are widely implemented owing to their various features. Regarding current HCS techniques, the rotor speed is mainly perturbed using predefined constants or objective functions, which makes the selection of step sizes a multifaceted task. These limitations are directly reflected in the overall dynamic WECS performance such as tracking speed, power fluctuations, and system efficiency. To deal with the challenges of the existing HCS techniques, this paper proposes a new adaptive HCS (AD-HCS) technique with self-adjustable step size using model reference adaptive control (MRAC) based on the PID controller. Firstly, the mechanical power fluctuations are detected, then the MRAC continuously optimizes the PID gains so as to generate an appropriate dynamic step size until harvesting the maximum power point (MPP) under the optimal tracking conditions. Looking specifically at the simulation results, the proposed AD-HCS technique exhibits low oscillations around the MPP and a small settling time. Moreover, WECS efficiency is increased by 5% and 2% compared to the conventional and recent HCS techniques, respectively. Finally, the studied system is confirmed over a 1.5 MW, gird-tied, double-fed induction generator (DFIG) WECS using MATLAB/Simulink.

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Enhanced Model Reference Adaptive Control Scheme for Tracking Control of Magnetic Levitation System

Cited by 10 publications

References 17 publications

Modeling and control of boiler in thermal power plant using model reference adaptive control

Modeling and control of boiler in thermal power plant using model reference adaptive control

Fuzzy Logic-Based Load Frequency Control in an Island Hybrid Power System Model Using Artificial Bee Colony Optimization

Robust Adaptive HCS MPPT Algorithm-Based Wind Generation System Using Model Reference Adaptive Control

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