Simple tuned fuzzy controller embedded into an FPGA

Montiel, Oscar; Maldonado, Yazmin; Sepúlveda, Roberto; Castillo, Oscar

doi:10.1109/nafips.2008.4531339

Cited by 17 publications

(11 citation statements)

References 9 publications

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“…The implementation of a Mamdani controller on an FPGA is described in [8], in this paper it is proposed the control of a multiple input system managing to obtain an error of 0, 8% at the output. It was demonstrated in [9] the implementation of a nonlinear controller on an FPGA, for this purpose it was used a recurrent fuzzy controller.…”

Section: Introductionmentioning

confidence: 99%

Efficient Design and Implementation of a Multivariate Takagi-Sugeno Fuzzy Controller on an FPGA

Aguilar

Pérez-Patricio

Camas

et al. 2014

2014 International Conference on Mechatronics, Electronics and Automotive Engineering

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Abstract-This article describes the design and efficient implementation of a Takagi Sugeno multivariable Fuzzy Logic Controller. The application selected is a temperature and humidity controller for a chicken incubator. This design was elaborated using VHDL applying intermediate simulations in order to check for functional verification of all modules integrating the controller. The created circuit was implemented on FPGA Cyclone II EP2C35F672C6 assembled in breadboard Altera DE2. Precision of the model on FPGA was calculated by contrasting the obtained results with the results of simulation using MATLAB & Fuzzy Logic Tool Box.

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Section: Introductionmentioning

confidence: 99%

Efficient Design and Implementation of a Multivariate Takagi-Sugeno Fuzzy Controller on an FPGA

Aguilar

Pérez-Patricio

Camas

et al. 2014

2014 International Conference on Mechatronics, Electronics and Automotive Engineering

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“…T optimal solu final winner cycle. The T1-FLC fuzzification of membersh [9], [11] GAs, one must nformation of elated to the rms. Figure 5 related to the n; the design sides and one Fs parameters g to Table 1.…”

Section: Preliminariesmentioning

confidence: 99%

Genetic Design of an Interval Type-2 Fuzzy Controller for Velocity Regulation in a DC Motor

Maldonado

Castillo

2012

International Journal of Advanced Robotic Systems

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This paper proposes the design of a Type-2 Fuzzy Logic Controller (T2-FLC) using Genetic Algorithms (GAs). The T2-FLC was tested with different levels of uncertainty to regulate velocity in a Direct Current (DC) motor. The T2-FLC was synthesized in Very High Description Language (VHDL) code for a Fieldprogrammable Gate Array (FPGA), using the Xilinx System Generator (XSG) of Xilinx ISE and MatlabSimulink. Comparisons were made between the Type-1 Fuzzy Logic Controller and the T2-FLC in VHDL code and a Proportional Integral Differential (PID) Controller so as to regulate the velocity of a DC motor and evaluate the difference in performance of the three types of controllers, using the t-student test statistic.

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“…The output has five singletons values called Big Decrease (BD), Decrease (D), Hold (H), Increase (I), and Big Increase (BI); hence, BD is associated with the binary value "001", etc. [19,18]. Additionally, it is shown that each input produces two fuzzified values; i.e., in input 1 (x 1 ) the dashed line indicates a crisp value that is being fuzzified, so it will produce the variables e 1 and e 2 , associated with their corresponding degree of membership stored in variables ge 1 and ge 2 .…”

Section: Experiments Design and Validationmentioning

confidence: 99%

“…There are other works related to this one; for example, for type-1 fuzzy systems [13,15,18,19,22,24,25], in the book [17] the authors give an academic example of the VHDL coding of a type-1 fuzzy controller. With respect to type-2 fuzzy systems there are some works like [14,15,16].…”

Section: Introductionmentioning

confidence: 99%

Design and Simulation of the Type-2 Fuzzification Stage: Using Active Membership Functions

Montiel

Sepúlveda

Maldonado³

et al. 2009

Evolutionary Design of Intelligent Systems in Modeling, Simulation and Control

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Abstract. This paper describes the design and implementation of the fuzzification stage for type-1 and type-2 fuzzy inference systems (FIS). A versatile method to calculate the membership values was used, it handles real numbers using decimal floating point binary encoding to calculate the slopes of triangular and trapezoidal membership functions. The designs were developed using VHDL code for FPGA implementation. The type-1 implementation is shown to give the basis of the type-2 implementation, which is based on the average method that consists in substituting an interval type-2 FIS by two type-1 FISs to cope with uncertainty. The functionality of the designs were evaluated by the analysis of the control surface plots of a speed controller for a DC motor. The plots were obtained from Simulink models that includes the VHDL designs developed in the Xilinx ISE. They were imported to the Simulink environment through the Xilinx System Generator.

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Simple tuned fuzzy controller embedded into an FPGA

Cited by 17 publications

References 9 publications

Efficient Design and Implementation of a Multivariate Takagi-Sugeno Fuzzy Controller on an FPGA

Efficient Design and Implementation of a Multivariate Takagi-Sugeno Fuzzy Controller on an FPGA

Genetic Design of an Interval Type-2 Fuzzy Controller for Velocity Regulation in a DC Motor

Design and Simulation of the Type-2 Fuzzification Stage: Using Active Membership Functions

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