FPGA for fuzzy controllers

Manzoul, Mahmoud A.; Jayabharathi, D.

doi:10.1109/21.362948

Cited by 41 publications

(10 citation statements)

References 10 publications

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“…High density, flexible architecture, and availability of devices with good programmability feature such as FPGA, offer even greater versatility for system implementation. A potential inference speed of up to 60 M FLIPS was reported in [30] by employing the Xilinx XC3000-family FPGA. Besides improvement in inference speed, higher resolution for fuzzy chips has also been achieved.…”

Section: Fuzzy Inference Chips Reviewmentioning

confidence: 99%

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A context switchable fuzzy inference chip

Cao

Lim

et al. 2006

IEEE Trans. Fuzzy Syst.

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This paper describes a novel design of a fuzzy inference chip that allows for real-time online context switching. A context refers to a situation or scenario of an application requiring specific domain knowledge. In particular, our focus is on the class of applications involving embedded fuzzy control. The domain knowledge therefore refers to fuzzy rules and memberships. The kind of applications being considered is real-time in nature, which necessitates the implementation of hardware for fuzzy inferencing. The chip architecture is described and details on the design of the chip is presented.Index Terms-Context switching, embedded fuzzy control, evolvable systems, fuzzy inference, reconfigurable fuzzy chip.

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Section: Fuzzy Inference Chips Reviewmentioning

confidence: 99%

“…However, such an approach is able to support only a single context since the switching of context requires the complete reconfiguration of the whole memory map. Some other examples of implicit implementation approaches can be found in [22], [27], and [30].…”

Section: Fuzzy Inference Chips Reviewmentioning

confidence: 99%

A context switchable fuzzy inference chip

Cao

Lim

et al. 2006

IEEE Trans. Fuzzy Syst.

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“…The FPGA-based system is useful because it can reduce development time and overall cost greatly, reprogrammed on the fly, operate faster than microprocessor, and enables device densities over 1 million gates. Therefore, some fuzzy system design and implementation method based on the FPGA technology are proposed (Manzoul and Jayabharathi 1995;Kim and Hyung 1997;Liu and Huang 1997;Salcic 2001;Li et al 2003;Wong et al 2005). Due to these useful features, a pipeline sequential fuzzy system design method by using VHDL is proposed so that the fuzzy system hardware implemented on an FPGA chip can achieve a higher clock frequency to support a shorter computation time.…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of Ga-Based Fuzzy System on Fpga Chip

Wong

Lin

2007

Cybernetics and Systems

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In this article, a design and implementation methodology of a GA-based fuzzy system on a Field Programmable Gate Array (FPGA) chip is proposed. First, a self-generating method based on a genetic algorithm (GA) is proposed to automatically construct a high performance fuzzy system. Some simulation results of an inverted pendulum control system are presented to illustrate the efficiency of the proposed GA-based method in the fuzzy system design. Then, a fuzzy system design method by using VHSIC Hardware Description Language (VHDL) is proposed so that the fuzzy system hardware implemented on an FPGA chip has a great flexibility and a high process speed. Moreover, a friendly-used software tool is developed to automatically generate VHDL codes for the users to easily design their fuzzy system hardware based on the proposed methodology. Finally, some experimental results are presented to illustrate the validity and the applicability of the design methodology.

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“…Publisher Item Identifier S 1063-6706(99)01367- 3. of different kind of digital FLC's [1], [5], [6], [12], evidence the expansion of digital techniques in fuzzy control. Therefore, design aspects of digital control [4] have to be studied beyond the scope of fuzzy control [8]- [10].…”

Section: Introductionmentioning

confidence: 99%

Consequences of the digitization on the performance of a fuzzy logic controller

Campo

Tarela²

1999

IEEE Trans. Fuzzy Syst.

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The consequences of the finite wordlength on the performance of a digital fuzzy logic controller (FLC) based on the Mamdani's inference algorithm are investigated. Digital implementations of FLC's involve three main types of errors due to the finiteness of the wordlength: the analog-to-digital errors, the membership function errors, and the arithmetic errors. First, a statistical evaluation of the consequences of these errors is performed. The effects of the digital resolution on the controller response are discussed. Then, the dynamic behavior of a closed-loop fuzzy system composed of a digitized FLC and a second-order plant is evaluated; a qualitative evaluation of timedomain parameters as a function of the universe discretization is carried out. The results show that, as in the case of conventional control strategies, bias effects and limit cycles are generated as a consequence of the digitization. Although these distortions diminish when the discretization is sufficiently refined, they are very difficult to predict because of the nonlinear nature of both quantization and fuzzy operation. In this sense, the availability of computer-aided design (CAD) tools that permit the specification of the wordlength is essential to validate the design.

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FPGA for fuzzy controllers

Cited by 41 publications

References 10 publications

A context switchable fuzzy inference chip

A context switchable fuzzy inference chip

Design and Implementation of Ga-Based Fuzzy System on Fpga Chip

Consequences of the digitization on the performance of a fuzzy logic controller

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