Neuro-fuzzy Control of a Position-Position Teleoperation System Using FPGA

Khati, Hocine; Mellah, Rabah; Talem, Hand

doi:10.1109/mmar.2019.8864681

Cited by 12 publications

(7 citation statements)

References 17 publications

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“…where A d 1 is the fuzzy set, ȳd is a constant value, and d = 1, … , M is the number of fuzzy rules, x(1, 2, … , n) and y are inputs and outputs of the fuzzy system, respectively. Therefore, the output of the fuzzy system can be defined as (20):…”

Section: Recommended Control Methodsmentioning

confidence: 99%

“…In teleoperation systems, if the commands (including force or movement) are communicated in a one‐way manner from the master to the slave, the resulting system is called one‐way or unilateral. Furthermore, if the response for the environmental force is communicated from the slave to the master as well, the teleoperation system is called two‐way or symmetric [20].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Design of robust adaptive fuzzy control for uncertain bilateral teleoperation systems based on backstepping approach

Bouteraa

Alattas

Peng

et al. 2023

IET Control Theory & Appl

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In this study, a novel method based on a robust adaptive fuzzy control approach is developed for nonlinear teleoperation systems. Its main objectives are to ensure system stability and properly mitigating parametric uncertainties stemming from external disturbances and un‐modelled dynamics. For the communication channel, instead of the direct transmission of environmental torque signals, the approximated environmental parameters by the fuzzy system are transmitted to the master side for the prediction of environmental torque, thus successfully avoiding the transmission of the power signals in the delayed communication channel and solving the passivity problem in the teleoperation system. Besides, a trajectory generator is employed in the master side, whereas a trajectory smoothing is provided in the slave side. Theoretically, it was proven that both position tracking and force feedback problems are attained. Using Lyapunov stability analysis, this work illustrates that the robust adaptive fuzzy controller based on the backstepping approach guarantees the system's asymptotic stability. Simulation results confirm the efficiency of the suggested control technique in achieving the stability and tracking objectives of the uncertain nonlinear teleoperation system.

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Section: Recommended Control Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Design of robust adaptive fuzzy control for uncertain bilateral teleoperation systems based on backstepping approach

Bouteraa

Alattas

Peng

et al. 2023

IET Control Theory & Appl

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“…We aim to determine vector Φ 2i through the extended Kalman filter which consists in linearizing the output around the control input at each sampling period. Thisisequivalenttowriting [16,19,20]:…”

Section: Learning Algorithmmentioning

confidence: 99%

Compensatory Adaptive Neural Fuzzy Inference System

Mellah¹,

Khati²,

Talem³

et al. 2022

Fuzzy Systems - Theory and Applications

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The traditional approach to fuzzy design is based on knowledge acquired by expert operators formulated into rules. However, operators may not be able to translate their knowledge and experience into a fuzzy logic controller. In addition, most adaptive fuzzy controllers present difficulties in determining appropriate fuzzy rules and appropriate membership functions. This chapter presents adaptive neural-fuzzy controller equipped with compensatory fuzzy control in order to adjust membership functions, and as well to optimize the adaptive reasoning by using a compensatory learning algorithm. An analysis of stability and transparency based on a passivity framework is carried out. The resulting controllers are implemented on a two degree of freedom robotic system. The simulation results obtained show a fairly high accuracy in terms of position and velocity tracking, what highlights the effectiveness of the proposed controllers.

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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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Neuro-fuzzy Control of a Position-Position Teleoperation System Using FPGA

Cited by 12 publications

References 17 publications

Design of robust adaptive fuzzy control for uncertain bilateral teleoperation systems based on backstepping approach

Design of robust adaptive fuzzy control for uncertain bilateral teleoperation systems based on backstepping approach

Compensatory Adaptive Neural Fuzzy Inference System

Proposal of Takagi–Sugeno Fuzzy-PI Controller Hardware

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