Design of a Mathematical Unit in FPGA for the Implementation of the Control of a Magnetic Levitation System

Raygoza-Panduro, Juan José; Ortega-Cisneros, Susana; Rivera, Jorge; Mora, Ana

doi:10.1155/2008/634306

Cited by 3 publications

(3 citation statements)

References 18 publications

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“…In 2008, Raygoza-Panduro et al [22] presented an automatically generated mathematical unit. The hardware description is automatically generated by a JAVA program and can be synthesized.…”

Section: Related Workmentioning

confidence: 99%

A Vector-Like Reconfigurable Floating-Point Unit for the Logarithm

Alachiotis

Stamatakis

2011

International Journal of Reconfigurable Computing

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The use of reconfigurable computing for accelerating floating-point intensive codes is becoming common due to the availability of DSPs in new-generation FPGAs. We present the design of an efficient, pipelined floating-point datapath for calculating the logarithm function on reconfigurable devices. We integrate the datapath into a stand-alone LUT-based (Lookup Table) component, the LAU (Logarithm Approximation Unit). We extended the LAU, by integrating two architecturally independent, LAU-based datapaths into a larger component, the VLAU (vector-like LAU). The VLAU produces 2 results/cycle, while occupying the same amount of memory as the LAU. Under single precision, one LAU is 12 and 1.7 times faster than the GNU and Intel Math Kernel Library (MKL) implementations, respectively. The LAU is also 1.6 times faster than the FloPoCo reconfigurable logarithm architecture. Under double precision, one LAU is 20 and 2.6 times faster than the respective GNU and MKL functions and 1.4 times faster than the FloPoCo logarithm. The VLAU is approximately twice as fast as the LAU, both under single and double precision.

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“…In 2008, Raygoza-Panduro et al [22] presented an automatically generated mathematical unit. The hardware description is automatically generated by a JAVA program and can be synthesized.…”

Section: Related Workmentioning

confidence: 99%

A Vector-Like Reconfigurable Floating-Point Unit for the Logarithm

Alachiotis

Stamatakis

2011

International Journal of Reconfigurable Computing

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“…A FPGA implementation of the Runge–Kutta model predictive control mechanism was proposed by Iplikci and Bahtiyar (2016). Raygoza-Panduro et al (2008) have designed a mathematical unit in a FPGA for the implementation of the control of a maglev system. Hamed and Elreesh (2013) presented a FPGA optimized fuzzy controller design for maglev using genetic algorithms.…”

Section: Introductionmentioning

confidence: 99%

Hardware-in-the-loop simulation and implementation of a fuzzy logic controller with FPGA: case study of a magnetic levitation system

Akbati

Üzgün

Akkaya

2018

Transactions of the Institute of Measurement and Control

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This paper presents the design and implementation of a fuzzy logic controller using Very High Speed Integrated Circuit Hardware Description Language (VHDL) on a field programmable gate array (FPGA). First, a Sugeno-type fuzzy logic controller with five triangular and trapezoidal membership functions for two inputs and with nine singleton membership functions for one output is examined. The proposed structure is tested with second- and third-order system model using FPGA-in-the-loop simulation via a MATLAB/Simulink environment. Then, for different kinds of fuzzy logic controllers, a new look-up table (LUT) and interpolation-based controller implementation is proposed to eliminate the computational complexity of the primarily designed structure. As a case study, a magnetic levitation system is controlled with an adaptive neuro-fuzzy inference system (ANFIS) trained fuzzy logic controller, then it is simulated and implemented using a LUT-based controller. Finally, we provide a comparison of results.

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“…Experimental results indicate effectiveness of this method for controlling magnetic levitation system. Panduro et al has used sliding mode to control magnetic levitation system [31]. They used a combination of Output Regulation Theory (ORT) and sliding mode controllers however this method imposes a non-zero steady state error to the position of the levitated object.…”

Section: Introductionmentioning

confidence: 99%