Fuzzy logic control on FPGA for two axes solar tracking

Cruz-Alejo, Jesus de la; Antonio-Mendez, Ricardo; Pereyra, Martín Salazar

doi:10.1007/s00521-017-3207-1

Cited by 6 publications

(6 citation statements)

References 23 publications

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“…The speedup in Msps for the TS-FIMM-OS, TS-FIMM-P, Fuzzy-PI-OS and Fuzzy-PI-P are equal to previously calculated values used in [5]. The speedup in Mflips are about 49 25 ≈ 1.96× and 49 42 ≈ 1.16× over the speedup in Msps for works [18] and [20], respectively. Finally, the hardware proposes in [7] achieved a throughput of about 1.56 Msps with three inputs, two outputs and 24 bits.…”

Section: Validation Results -Fuzzy-pi Controller Hardwaresupporting

confidence: 52%

“…With 8 bits, the speedup in Msps for the TS-FIMM-OS, TS-FIMM-P, Fuzzy-PI-OS and Fuzzy-PI-P are 11 [18,20] shows a hardware can achieve about 1 Msps. The work presented in [18] uses two inputs, 25 rules, one output and 8 bits and the designer presented in [20] was projected with three inputs, 42 rules and one output. The speedup in Msps for the TS-FIMM-OS, TS-FIMM-P, Fuzzy-PI-OS and Fuzzy-PI-P are equal to previously calculated values used in [5].…”

Section: Validation Results -Fuzzy-pi Controller Hardwarementioning

confidence: 99%

“…On the other hand, this manuscript uses a fully parallel approach and it achieves 1 Msps/MHz, in other words, it can execute more operations per clock cycle. In the same direction, the proposals presented in [18,20] shows a semi-parallel fuzzy control hardware with low-throughput, about 1 Msps. Thus, this manuscript proposes a hardware architecture for the Fuzzy-PI control system.…”

Section: Related Workmentioning

confidence: 91%

“…Intelligent systems based on production rules that use Fuzzy Logic in the inference process are called in the literature of Fuzzy Systems (FS) [4]. Among the existing inference strategies, the most used, the Mamdani and the Takagi-Sugeno, are differentiated by the final stage of the inference process [5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20].…”

Section: Introductionmentioning

confidence: 99%

“…The works presented in [30,31,32,33,34,35,36,37] propose implementations of FS on reconfigurable hardware (Field Programmable Gate Array -FPGA), showing the possibilities associated with the acceleration of fuzzy inference processes having a high degree of parallelization. Other works propose specific implementations of Fuzzy Control Systems (FCS) using the Fuzzy Mamdani Inference Machine (M-FIM) and the Takagi-Sugeno Fuzzy Inference Machine (TS-FIM) [5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20]. The works presented in [38,39,40] propose the Takagi-Sugeno hardware acceleration for other types of application fields.…”

Section: Introductionmentioning

confidence: 99%

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Proposal of Takagi–Sugeno Fuzzy-PI Controller Hardware

Silva

Lopes

Valderrama

et al. 2020

Sensors

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This work proposes dedicated hardware for an intelligent control system on Field Programmable Gate Array (FPGA). The intelligent system is represented as Takagi-Sugeno Fuzzy-PI controller. The implementation uses a fully parallel strategy associated with a hybrid bit format scheme (fixed-point and other floatingpoint). Two hardware designs are proposed; the first one uses a single clock cycle processing architecture, and the other uses a pipeline scheme. The bit accuracy was tested by simulation with a non linear control system of robotic manipulator. The area, throughput, and dynamic power consumption of the implemented hardware are used to validate and compare the results of this proposal. The results achieved allow that the proposal hardware can use in several applications with high-throughput, low-power and ultra-low-latency restrictions such as teleportation of robot manipulators, tactile internet, industrial automation in industry 4.0, and others. such as the tactile Internet [21,22], the Internet of Things (IoT) and Industry 4.0, where the problems associated with processing, power, latency and miniaturization are fundamental. Robotic manipulators used on tactile internet need a high-throughput and ultra-low-latency control system, and this can be achieved with dedicated hardware [21].The development of dedicated hardware, in addition to speeding up parallel processing, makes it possible to operate with clocks adapted to low-power consumption [23,24,25,26,27,28,29]. The works presented in [30,31,32,33,34,35,36,37] propose implementations of FS on reconfigurable hardware (Field Programmable Gate Array -FPGA), showing the possibilities associated with the acceleration of fuzzy inference processes having a high degree of parallelization. Other works propose specific implementations of Fuzzy Control Systems (FCS) using the Fuzzy Mamdani Inference Machine (M-FIM) and the Takagi-Sugeno Fuzzy Inference propose the Takagi-Sugeno hardware acceleration for other types of application fields. This work aims to develop a new hardware proposal for a Fuzzy-PI controller with TS-FIM. Unlike most of the works presented, this project offers a fully parallel scheme associated with a hybrid platform using fixed-point and floating-point representations. Two TS-FIM hardware modules have been proposed, the first (here called TS-FIM module one-shot) takes one sample time to execute the TS-FIM, and the second (here called as TS-FIM module pipeline) uses registers inside the TS-FIM. Two Fuzzy-PI controller hardware have been proposed, one for the TS-FIM one-shot module and another for the TS-FIM module pipeline. The proposed hardware have been implemented, tested and validated on a Xilinx Virtex 6 FPGA. The synthesis results, in terms of size, resources and throughput, are presented according to the number of bits and the type of numerical precision. Already, the physical area on the target FPGA reaches less than 7%. The implementation achieved a throughput between 10 and 18Msps (Mega samples per second), and between 490and 882 Mfl...

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