Dynamic rule based approach to reduce power consumption of the fuzzy logic controller for embedded applications

Baldania, M. D.; Sawant, D. A.; Patki, A. B.

doi:10.1109/cnsc.2014.6906688

2014 First International Conference on Networks &Amp; Soft Computing (ICNSC2014) 2014

DOI: 10.1109/cnsc.2014.6906688

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Dynamic rule based approach to reduce power consumption of the fuzzy logic controller for embedded applications

M. D. Baldania

D. A. Sawant

A. B. Patki

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2015

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“…Baldania et al [11], focuses on rule base division technique to reduce the power consumption of the fuzzy logic controller based on different conditions and priorities. A new methodology is developed and implemented successfully to reduce the power consumption of the fuzzy logic controller, i.e., dynamic rule base method.…”

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confidence: 99%

Literature Review for Digital Implementations of Fuzzy Logic Type-1 and Type-2

Ponce-Cruz

Molina

MacCleery

2015

Fuzzy Logic Type 1 and Type 2 Based on LabVIEW™ FPGA

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Some works are described below where optimization Type-1 and Type-2 FLS have had relative success according to different areas, illustrating the advantages of using methods to automate process with fuzzy controllers.The work of Sudha and Vijai Santhi [1], deals with the load frequency control (LFC) problem, which has been a major subject in electrical power system such as design and operation. In practice, LFC systems use simple proportional integral (PI) controllers. However, it shows that fuzzy logic controller is adaptive in nature and is applied successfully for power system stabilization control. Authors propose a type-2 fuzzy approach for load frequency control of two-area interconnected reheat thermal power system with the consideration of Generation Rate Constraint (GRC). The performance of the type-2 controller is compared with conventional controller and Type-1 fuzzy controller with regard to Generation Rate Constraint (GRC). The system parametric uncertainties are verified by changing parameters by 40% simultaneously from their typical values.The authors Jammeth and Fleury in their work [2] use an interval Type-2 FLC that gets better video quality compared with traditional controllers. They also use Type-1 FLC to show the response in different network scenarios, such like Internet cross-traffic, video streams occupy on an Internet protocol (IP) network. Type-2 FLC is designed for Internet conditions so they can react successfully to the network conditions of an IP-network. Type-2 FLC resulted in an order of magnitude performance improvement in comparison with Type-1 FLC.The work of Lee and Lin [3], proposes a new control scheme using type-2 fuzzy neural network (type-2 FNN) and adaptive filter for controlling nonlinear uncertain systems. This type-2 FNN model combines the advantages of type-2 fuzzy logic systems and neural networks. The type-2 FNN system has the ability of universal approximation, which identifies nonlinear dynamic systems. The control scheme consists of a PD-type adaptive FNN controller and a pre-filter. The adaptive filter is

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mentioning

confidence: 99%

Literature Review for Digital Implementations of Fuzzy Logic Type-1 and Type-2

Ponce-Cruz

Molina

MacCleery

2015

Fuzzy Logic Type 1 and Type 2 Based on LabVIEW™ FPGA

View full text Add to dashboard Cite

show abstract

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Dynamic rule based approach to reduce power consumption of the fuzzy logic controller for embedded applications

Cited by 1 publication

References 4 publications

Literature Review for Digital Implementations of Fuzzy Logic Type-1 and Type-2

Literature Review for Digital Implementations of Fuzzy Logic Type-1 and Type-2

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