Observability of speed DC motor with self-tuning fuzzy-fractional-order controller

2022 ◽  
pp. 157-179
Author(s):  
Arezki Fekik ◽  
Mohamed Lamine Hamida ◽  
Hamza Houassine ◽  
Hakim Denoun ◽  
Sundarapandian Vaidyanathan ◽  
...  
Mechatronics ◽  
2013 ◽  
Vol 23 (7) ◽  
pp. 798-804 ◽  
Author(s):  
Cristina I. Muresan ◽  
Silviu Folea ◽  
George Mois ◽  
Eva H. Dulf

2013 ◽  
Vol 62 (3) ◽  
pp. 505-516 ◽  
Author(s):  
Andrzej Ruszewski ◽  
Andrzej Sobolewski

Abstract The paper presents the problem of position control of DC motor with rated voltage 24 V loaded by flywheel. The fractional order PD controller implemented in National Instruments NI ELVIS II programmed in LabView is used for controlling. The simple method for determining stability regions in the controller parameters space is given. Knowledge of these regions permits tuning of the controller and ensures required the phase margin of the system


2021 ◽  
Author(s):  
Amirreza Naderipour ◽  
Zulkurnain Abdul-Malek ◽  
Iraj Faraji Davoodkhani ◽  
Hesam Kamyab ◽  
Roshafima Rasit Ali

Abstract The variations in the consumption load and generation power in microgrid systems such as photovoltaic, wind-turbine fuel cell and energy storage systems (PV/WT/FC/ESSs) has challenged the load-frequency control due to the increased complexity and nonlinear nature of these systems. This paper employs a self-tuning controller based on the fuzzy logic to overcome parameter uncertainties of classic controllers, such as operation conditions, the change in the operating point of the microgrid and the uncertainty of microgrid modeling. Further, a combined fuzzy logic and fractional-order controller is used for load-frequency control of the off-grid microgrid with the influence of renewable resources because the latter controller benefits robust performance and enjoys a flexible structure. To reach a better operation for the proposed controller, a novel meta-heuristic whale algorithm has been used to optimally determine the input and output scale coefficients of the fuzzy controller and fractional orders of the fractional-order controller. The suggested approach is applied to a microgrid with a diesel generator, wind turbine, photovoltaic systems, and energy storage devices. The comparison made between the results of the proposed controller and those of the classic PID controller proves the superiority of the optimized fractional-order self-tuning fuzzy controller in terms of operation characteristics, response speed, and the reduction in frequency deviations against load variations.


2021 ◽  
Vol 4 (3) ◽  
pp. 50
Author(s):  
Preeti Warrier ◽  
Pritesh Shah

The control of power converters is difficult due to their non-linear nature and, hence, the quest for smart and efficient controllers is continuous and ongoing. Fractional-order controllers have demonstrated superior performance in power electronic systems in recent years. However, it is a challenge to attain optimal parameters of the fractional-order controller for such types of systems. This article describes the optimal design of a fractional order PID (FOPID) controller for a buck converter using the cohort intelligence (CI) optimization approach. The CI is an artificial intelligence-based socio-inspired meta-heuristic algorithm, which has been inspired by the behavior of a group of candidates called a cohort. The FOPID controller parameters are designed for the minimization of various performance indices, with more emphasis on the integral squared error (ISE) performance index. The FOPID controller shows faster transient and dynamic response characteristics in comparison to the conventional PID controller. Comparison of the proposed method with different optimization techniques like the GA, PSO, ABC, and SA shows good results in lesser computational time. Hence the CI method can be effectively used for the optimal tuning of FOPID controllers, as it gives comparable results to other optimization algorithms at a much faster rate. Such controllers can be optimized for multiple objectives and used in the control of various power converters giving rise to more efficient systems catering to the Industry 4.0 standards.


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