Development, experimental validation, and comparison of interval type-2 Mamdani fuzzy PID controllers with different footprints of uncertainty

Praharaj, Manoranjan; Sain, Debdoot; Mohan, B.M.

doi:10.1016/j.ins.2022.03.095

Cited by 16 publications

(4 citation statements)

References 41 publications

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“…The QRESCSO‐MFPIC model is utilized to improve the dynamic response of the DC‐DC converter through the objective function. In the QRESCSO‐MFPIC approach, the parameter of the fuzzy PI controller is considered as input 31 . The fitness function is determined, and quantile regression analysis is performed.…”

Section: Proposed Methodologymentioning

confidence: 99%

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A novel cat swarm optimization‐based fuzzy PI controller for improving dynamic response of converter

Priya,

Balakumaran

2023

Asia-Pacific J Chem Eng

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Nowadays, the use of fuel cells in various applications has become very widespread. Some of their most important applications are in electric vehicles (EVs) and local off‐grid power systems. An EV works as an electric motor that burns a mixture of fuel and gases to generate electricity. One of the challenges with fuel cells is the slow dynamic response to load power changes. In the past few decades, the modeling and control of DC‐DC converters have undergone extensive research and development. However, the error, settling time, and peak overshoot performance are not reduced by the robust nonlinear controller and current mode controller. To overcome these problems, quantile regressive extreme seeking cat swarm optimized Mamdani fuzzy PI controller (QRESCSO‐MFPIC) approach is developed. The goal of the QRESCSO‐MFPIC approach is to reduce the integral time absolute error (ITAE) for tuning the fuzzy PI controller. Initializing the fuzzy PI controller parameter is an input in the QRESCSO‐MFPIC approach. For every parameter value, the fitness function is determined and quantile regression analysis is carried out. The efficiency of the QRESCSO‐MFPIC approach is measured by evaluating the settling time and peak overshoot. Tuning of the PI controller is carried out with cat swarm optimization (CSO) and particle swarm optimization (PSO) based on the objective function of ITAE. The result analysis shows that the QRESCSO‐MFPIC approach improves the efficiency of an optimized Fuzzy PI controller compared to the existing methods. Peak overshoot reduction and settling time are improved through the QRESCSO‐MFPIC approach than the proportional integral particle swarm optimization controller (PI‐PSO).

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Section: Proposed Methodologymentioning

confidence: 99%

“…In the QRESCSO-MFPIC approach, the parameter of the fuzzy PI controller is considered as input. 31 The fitness function is determined, and quantile regression analysis is performed. In the QRESCSO-MFPIC model, the Mamdani fuzzy PI controller is used to manage the voltage regulation in the output of an EV.…”

Section: Mamdani Fuzzy Pi Controllermentioning

confidence: 99%

A novel cat swarm optimization‐based fuzzy PI controller for improving dynamic response of converter

Priya,

Balakumaran

2023

Asia-Pacific J Chem Eng

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“…It is possible to integrate attitude control with the trajectory controller once it is designed and optimized. The control architecture to be implemented on the quadcopter system was as follows [15][16][17][18][19]:…”

Section: Control Design For Quadcopter System Stabilization Using Fuz...mentioning

confidence: 99%

Modeling of Unmanned Aerial Vehicles for Smart Agriculture Systems Using Hybrid Fuzzy PID Controllers

Amertet,

Gebresenbet,

Alwan

2024

Applied Sciences

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Unmanned aerial vehicles have a wide range of uses in the military field, non-combat situations, and civil works. Due to their ease of operation, unmanned aerial vehicles (UAVs) are highly sought after by farmers and are considered the best agricultural technologies, since different types of controller algorithms are being integrated into drone systems, making drones the most affordable option for smart agriculture sectors. PID controllers are among the controllers frequently incorporated into drone systems. Although PID controllers are frequently used in drones, they have some limitations, such as sensitivity to noise and measurement errors, which can lead to instability or oscillations in the system. On the other hand, PID controllers provide improved accuracy in drone system responses. When using PID controllers to achieve the best performance in a drone system, it is better to share the advantages of PID controllers with other intelligence controllers. One promising option is the fuzzy PID controller. The aim of this study was to control quadcopter states (rolling, altitude, and airspeed) by leveraging quadcopter technology and adding hybrid fuzzy PID controls into the system. The quadcopter system and its controllers were mathematically modeled using the Simulink/MATLAB platform, and the system was controlled by fuzzy PID controllers. For validation purposes, the fuzzy PID controller was compared with a classically tuned PID controller. For roll, height, and airspeed, the fuzzy PID controller provided an improvement of 41.5%, 11%, and 44%, respectively, over the classically tuned PID controller. Therefore, the fuzzy PID controller best suits the needs of farmers and is compatible with smart agriculture systems.

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“…In the condition of the five-area AGC system with the addition of BESS, the undershoot value is greater than the overshoot value [63]. Therefore, after adding the P parameter, the undershoot value is greater than the overshoot value [64]. The second stage is to decrease the undershoot value by parameter I, causing the overshoot value to increase [65].…”

Section: B Pid-based Bessmentioning

confidence: 99%

Novel PID Controller on Battery Energy Storage Systems for Frequency Dynamics Enhancement

Abdillah,

Jayadiharja,

Arjadi

et al. 2023

Journal of Robotics and Control

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Frequency dynamics is one of the important aspects of power system stability. From the frequency dynamics, the operator could plan how is the reliability of the electricity. The frequency can be maintained by controlling the balance between load demand and generation. To maintain the balance of the generation, the governor is playing an important role to increase the speed of the turbine and enhance the generating capacity of the generator (ramp-up). However, as the speed of the governor is slower than the increasing load demand, in the sub-transient area, the frequency may experience higher overshoot. Hence, it is important to add additional devices such as battery energy storage systems to enhance the frequency dynamics response in the sub-transient area. One of the important parts of storage is the controller. The controller must make sure the storage charges and discharge energy are in the sub-transient area. Hence PID controller can be the solution to make the storage operate optimally This paper proposed a novel PID controller on battery energy storage systems (BESS) to enhance the dynamics performance of frequencies. The five-area power system is used as the test system to investigate the efficacy of the proposed novel idea. Time domain simulation is investigated to see the improvement of the frequency dynamics response. From the simulation results, it is found that adding a PID controller on BESS could enhance the BESS response and result in frequency dynamics response improvement.

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Development, experimental validation, and comparison of interval type-2 Mamdani fuzzy PID controllers with different footprints of uncertainty

Cited by 16 publications

References 41 publications

A novel cat swarm optimization‐based fuzzy PI controller for improving dynamic response of converter

A novel cat swarm optimization‐based fuzzy PI controller for improving dynamic response of converter

Modeling of Unmanned Aerial Vehicles for Smart Agriculture Systems Using Hybrid Fuzzy PID Controllers

Novel PID Controller on Battery Energy Storage Systems for Frequency Dynamics Enhancement

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