The Logic-Based Supervisor Control for Sun-Tracking System of 1 MW HCPV Demo Plant: Study Case

Yeh, Hong-Yih; Lee, Cheng-Dar

doi:10.3390/app2010100

Cited by 8 publications

(2 citation statements)

References 27 publications

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“…In the literature, we can observe the use of FRBS to implement solar tracker using fuzzy control [61][62][63][64]. In [65], the authors explain that fuzzy control could be a good option when an accurate model of the tracking system is absent. Therefore, if we use this type of control for a solar tracker, it is necessary to learn a good knowledge base (KB) for this control system, and to achieve this task it is necessary to have a good model of the HCPV module.…”

Section: Utility Of the Frbs For Modeling An Hcpv Modulementioning

confidence: 99%

Modeling a High Concentrator Photovoltaic Module Using Fuzzy Rule-Based Systems

et al. 2019

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Currently, there is growing interest in the modeling of high concentrator photovoltaic modules, due to the importance of achieving an accurate model, to improve the knowledge and understanding of this technology and to promote its expansion. In recent years, some techniques of artificial intelligence, such as the Artificial Neural Network, have been used with the goal of obtaining an electrical model of these modules. However, little attention has been paid to applying Fuzzy Rule-Based Systems for this purpose. This work presents two new models of high concentrator photovoltaics that use two types of Fuzzy Systems: the Takagi-Sugeno-Kang, characterized by the achievement of high accuracy in the model, and the Mamdani, characterized by high accuracy and the ease of interpreting the linguistic rules that control the behavior of the fuzzy system. To obtain a good knowledge base, two learning methods have been proposed: the “Adaptive neuro-fuzzy inference system” and the “Ad Hoc data-driven generation”. These combinations of fuzzy systems and learning methods have allowed us to obtain two models of high concentrator photovoltaic modules, which include two improvements over previous models: an increase in the model accuracy and the possibility of deducing the relationship between the main meteorological parameters and the maximum power output of a module.

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Section: Utility Of the Frbs For Modeling An Hcpv Modulementioning

confidence: 99%

Modeling a High Concentrator Photovoltaic Module Using Fuzzy Rule-Based Systems

et al. 2019

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“…An alternative solution is to include a PID gain scheduling algorithm inside the controller, such as the cooperation between the PID controller and fuzzy logic algorithm [30], or logic-based supervisor strategy [31].…”

Section: Control Goalmentioning

confidence: 99%

Adaptive Control of Active Magnetic Bearing against Milling Dynamics

Lee

Chen

2016

Applied Sciences

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For improving the defects in milling processes caused by traditional spindle bearings, e.g., the dimensional discrepancy of a finished workpiece due to bearing wear or oil pollution by lubricant, a novel embedded cylindrical-array magnetic actuator (ECAMA) is designed for milling applications. Since ECAMA is a non-contact type actuator, a control strategy named fuzzy model-reference adaptive control (FMRAC) is synthesized to account for the nonlinearities of milling dynamics and magnetic force. In order to ensure the superior performance of spindle position regulation, the employed models in FMRAC are all constructed by experiments. Based on the experimental results, the magnetic force by ECAMA is much stronger than that by the traditional active magnetic bearing (AMB) design under the same test conditions and identical overall size. The efficacy of ECAMA to suppress the spindle position deviation with the aid of FMRAC has been verified as well via numerical simulations and practical metal cutting.

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