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

Akbati, Onur; Üzgün, Hatice Didem; Akkaya, Sirin

doi:10.1177/0142331218813425

Cited by 7 publications

(5 citation statements)

References 22 publications

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“…Another controller that needs the system to be modeled in a linear one is the widely known state feedback [39]- [42]. Meanwhile, a controller based on fuzzy logic control is not easy to design [43], needs practical data reference or additional knowledge from other controllers [44] and requires high computation to run the fuzzy [45]. Likewise, as proposed in [46], neural network control for MLS needs training data from other controllers [47] [48].…”

Section: Introductionmentioning

confidence: 99%

Sliding Mode Control Design for Magnetic Levitation System

Ma’arif

Márquez-Vera²,

Mahmoud³

et al. 2023

Journal of Robotics and Control

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This paper presents a control system design for a magnetic levitation system (Maglev) or MLS using sliding mode control (SMC). The MLS problem of levitating the object in the air will be solved using the controller. Inductors used in MLS make the system have nonlinear characteristics. Thus, a nonlinear controller is the most suitable control design for MLS. SMC is one of the nonlinear controllers with good robustness and can handle any model mismatch. Based on simulation results with a step as input reference, MLS provided good system performances: 0.0991s rise time, 0.1712s settling time, and 0.0159 overshoot. Moreover, a prominent tracking control for sine wave reference was also shown. Although the augmented system had a chattering effect on the control signal, the chattering control signal did not affect MLS performances.

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Section: Introductionmentioning

confidence: 99%

Sliding Mode Control Design for Magnetic Levitation System

Ma’arif

Márquez-Vera²,

Mahmoud³

et al. 2023

Journal of Robotics and Control

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“…Over the years, various control strategies have been successively developed to implement real-time position control of the magnetic levitation system. The control strategies mainly include feedback linearization control 8 , 9 , proportional-integral-derivative (PID) control 10 , 11 , model predictive control 12 , 13 , robust H-infinity control 14 , 15 , sliding mode control 16 , 17 , and adaptive fuzzy control 18 , 19 . Although these different control strategies can achieve good control results from different perspectives 7 , there is still room for improvement in the control performance of the magnetic levitation system to a certain extent.…”

Section: Introductionmentioning

confidence: 99%

Recurrent neural network based high-precision position compensation control of magnetic levitation system

Huang

Shao

et al. 2022

Sci Rep

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For improving the dynamic quality and steady-state performance, the hybrid controller based on recurrent neural network (RNN) is designed to implement the position control of the magnetic levitation ball system in this study. This hybrid controller consists of a baseline controller, an RNN identifier, and an RNN controller. In the hybrid controller, the baseline controller based on the control law of proportional-integral-derivative is firstly employed to provide the online learning sample and maintain the system stability at the early control phase. Then, the RNN identifier is trained online to learn the accurate inverse model of the controlled object. Next, the RNN controller shared the same structures and parameters with the RNN identifier is applied to add the precise compensation control quantity in real-time. Finally, the effectiveness and advancement of the proposed hybrid control strategy are comprehensively validated by the simulation and experimental tests of tracking step, square, sinusoidal, and trapezoidal signals. The results indicate that the RNN-based hybrid controller can obtain higher precision and faster adjustment than the comparison controllers and has strong anti-interference ability and robustness.

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“…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%

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

Cited by 7 publications

References 22 publications

Sliding Mode Control Design for Magnetic Levitation System

Sliding Mode Control Design for Magnetic Levitation System

Recurrent neural network based high-precision position compensation control of magnetic levitation system

Proposal of Takagi–Sugeno Fuzzy-PI Controller Hardware

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