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.
AdıgüzelFDokumaclarEAkbatıOTürkerT (2018) Design and implementation of an adaptive backstepping controller for a magnetic levitation system. Transactions of the Institute of Measurement and Control40(8): 2466–2475.
2.
AguilarAPérezMCamasJLHernándezHRRosC (2014) Efficient design and implementation of a multivariate Takagi–Sugeno fuzzy controller on an FPGA. In: 2014 IEEE International Conference on Mechatronics, Electronics and Automotive Engineering (ICMEAE). IEEE, pp. 152–157.
3.
AielloGCacciatoMScarcellaGScelbaG (2017) Failure analysis of AC motor drives via fpga-based hardware-in-the-loop simulations. Electrical Engineering99(4): 1337–1347.
4.
AkkayaSUzgunHDAkbatiO (2017) FPGA icin bulanik mantik kontrolcusu tasarimi, ikinci ve ucuncu dereceden dogrusal sistemler ile donanm tabanli benzetim calismalari. In: 2017 Otomatik Kontrol Turk Milli Komitesi Ulusal Toplantisi (TOK), pp. 294–299.
5.
AnandMSTyagiB (2012) Design and implementation of fuzzy controller on FPGA. International Journal of Intelligent Systems and Applications4(10): 35–42.
6.
BaiYGuoNAgbeghaG (2012) Fuzzy interpolation and other interpolation methods used in robot calibrations. Journal of Robotics2012; 2012: 376293.
7.
BaranowskiJPiatekP (2012) Observer-based feedback for the magnetic levitation system. Transactions of the Institute of Measurement and Control34(4): 422–435.
8.
ChenWYuanHMWangY (2009) Design and implementation of digital fuzzy-PID controller based on FPGA. In: 4th IEEE Conference on Industrial Electronics and Applications (ICIEA). IEEE, pp. 393–397.
9.
EroğluYAblayG (2016) Cascade sliding mode–based robust tracking control of a magnetic levitation system. Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering230(8): 851–860.
10.
HamedBElreeshHA (2013) FPGA optimized fuzzy controller design for magnetic ball levitation using genetic algorithms. International Journal on Electrical and Power Engineering4(2): 58–65.
11.
IplikciSBahtiyarB (2016) A field-programmable gate array implementation of a real-time nonlinear Runge–Kutta model predictive control. Transactions of the Institute of Measurement and Control38(5): 555–564.
12.
IrwansyahAIbraheemOWHagemeyerJPorrmannMRueckertU (2017) FPGA-based multi-robot tracking. Journal of Parallel and Distributed Computing107: 146–161.
13.
KrimSGdaimSMtibaaAMimouniMF (2015) Fuzzy speed controller for an induction motor associated with the direct torque control: Implementation on the FPGA. In: 4th IEEE International Conference on Systems and Control (ICSC). IEEE, pp. 492–497.
14.
MeswaniMRCarringtonLUnatDSnavelyABadenSPooleS (2013) Modeling and predicting performance of high performance computing applications on hardware accelerators. The International Journal of High Performance Computing Applications27(2): 89–108.
15.
OliveiraDNde Souza BragaAPda Mota AlmeidaO (2010) Fuzzy logic controller implementation on a FPGA using VHDL. In: 2010 IEEE Annual Meeting of the North American: Fuzzy Information Processing Society (NAFIPS). IEEE, pp. 1–6.
16.
PlazaAChangCI (2008) Clusters versus FPGA for parallel processing of hyperspectral imagery. The International Journal of High Performance Computing Applications22(4): 366–385.
17.
Raygoza-PanduroJJOrtega-CisnerosSRiveraJde la MoraA (2008) Design of a mathematical unit in FPGA for the implementation of the control of a magnetic levitation system. International Journal of Reconfigurable Computing2008.
18.
SadekUSarjašAChowdhuryASvečkoR (2017) Improved adaptive fuzzy backstepping control of a magnetic levitation system based on symbiotic organism search. Applied Soft Computing56: 19–33.
19.
TalpurNSallehMNMHussainK (2017) An investigation of membership functions on performance of ANFIS for solving classification problems. In: IOP Conference Series: Materials Science and Engineering, vol. 226. Bristol: IOP Publishing.
20.
VuongPTMadniAMVuongJB (2006) VHDL implementation for a fuzzy logic controller. In: 2006 World Automation Congress (WAC’06). IEEE, pp. 1–8.
21.
YadavSVermaSKNagarSK (2017) Performance enhancement of magnetic levitation system using teaching learning based optimization. Alexandria Engineering Journal. DOI: 10.1016/j.aej.2017.08.016.
22.
ZhangJTaoTMeiXJiangGZhangD (2011) Non-linear robust control of a voltage-controlled magnetic levitation system with a feedback linearization approach. Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering225(1): 85–98.
23.
ZhangNKamdemROsosanyaEMahmoudWLiuW (2010) VHDL implementation of the hybrid fuzzy logic controllers with FPGA. In: 2010 IEEE International Conference on Intelligent Control and Information Processing (ICICIP). IEEE, pp. 5–10.
