Design of fractional model reference adaptive PID controller to magnetic levitation system with permagnet

Ananthababu, P.; Raja, Ch.; Latha, D. V. Pushpa; Sudha, K. Rama

doi:10.1504/ijscc.2016.075117

Cited by 3 publications

(2 citation statements)

References 16 publications

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“…Tepljakov et al (see [13,14]) described the problem of fractional-order PID controller design for a model of a magnetic levitation system. The latest research focuses on the digital implementation of non-integer controller for a real plant; this topic was considered by Chopade et al [15], Rojas et al [16] and Ananthababu et al [17]. Pandey et al (see [18,19]) proposed an anti-windup fractional PID controller for magnetic levitation.…”

Section: Introductionmentioning

confidence: 99%

Fractional PIλD Controller Design for a Magnetic Levitation System

Bauer

Baranowski

2020

Electronics

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Currently, there are no formalized methods for tuning non-integer order controllers. This is due to the fact that implementing these systems requires using an approximation of the non-integer order terms. The Oustaloup approximation method of the sα fractional derivative is intuitive and widely adopted in the design of fractional-order PIλD controllers. It requires special considerations for real-time implementations as it is prone to numerical instability. In this paper, for design and tuning of fractional regulators, we propose two methods.The first method relies on Nyquist stability criterion and stability margins. We base the second on parametric optimization via Simulated Annealing of multiple performance indicators. We illustrate our methods with a case study of the PIλD controller for the Magnetic Levitation System. We illustrate our methods’ efficiency with both simulations and experimental verification in both nominal and disturbed operation.

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Section: Introductionmentioning

confidence: 99%

Fractional PIλD Controller Design for a Magnetic Levitation System

Bauer

Baranowski

2020

Electronics

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“…Since both fuzzy logic and ANN had their relative benefits, a powerful processing with benefits of both was achieved by merging them together. The learning ability of Neural Network was used for regulating the parameters of fuzzy logic in various scenarios for achieving better performance (Chu and Tsai, 2008;Afzalian and Linkens, 2000;Kala et al, 2011;Ananthababu et al, 2016;Khan et al, 2016;Sardar et al, 2015). Nevertheless, application field was restricted to static problems as the feed forward network composition was a major shortcoming of the neuro-fuzzy system.…”

Section: Introductionmentioning

confidence: 99%

Recurrent Neuro-Fuzzy Control of Grid-Interfaced Solid Oxide Fuel Cell (SOFC) System

Qureshi

Qamar

Ali

et al. 2018

IJSCC

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The paper presents controller design, modelling, and simulations of the solid oxide fuel cell (SOFC) system. The SOFC model is used for the development of the fuzzy control scheme to improve the system performance. The SOFC is widely acknowledged for the clean distributed power generation. However, dire process problems occur frequently when the stand-alone fuel cell is directly interfaced with the electricity grid. Moreover, sustaining the optimal power quality and load following is the huge challenge, during the peak power demand schedule from the utility grid and large load perturbations. Consequently, a suitable and highly efficient control system is required for controlling and following the power load demands for the complex grid interfaced SOFC power systems. Therefore, a novel nonlinear hybrid adaptive recurrent fuzzy neural network (ARFNN) is developed for the control of the grid interfaced SOFC. The rapid power load following and safe SOFC operation requirement is also considered in the design of the closed loop control system. Simulation results proved that the proposed hybrid ARFNN enhances the optimal power quality and load-following than the conventional PI control scheme.

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