Neuro-Fuzzy Control Synthesis for Electro-Hydraulic Follow-up System

Ormandzhiev, Krasimir; Yordanov, Stanimir

doi:10.1109/elma.2019.8771671

Cited by 4 publications

(1 citation statement)

References 8 publications

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“…Based on fuzzy techniques and finite impulse response, Choi et al, developed a fuzzy finite memory state estimation for electro-hydraulic active suspension systems, which was more robust than the existing infinite impulse response nonlinear estimator in terms of external disturbances and modeling uncertainties [28]. The similar approaches were showed in [29,30]. To achieve higher performance, many researchers leveraged a hybrid of fuzzy and other techniques, such as PID and particle swarm optimization (PSO).…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Feedback Control for Electro-Hydraulic Actuators

Van¹,

Tran²,

Ha³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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Electro-hydraulic actuators (EHA) have recently played a significant role in modern industrial applications, especially in systems requiring extremely high precision. This can be explained by EHA's ability to precisely control the position and force through advanced sensors and innovative control algorithms. One of the promising approaches to improve control accuracy for EHA systems is applying classical to modern control algorithms, in which the proportional-integral-derivative (PID) algorithm, fuzzy logic controller, and a hybrid of these methods are popular options. In this paper, we developed a novel version of the fuzzy control algorithm and linear feedback control method, namely fuzzy linear feedback control, to improve the control performance. To achieve the highest performance, we first designed a mathematical EHA model based on the Matlab/ Simulink software packages thanks to the selected parameters, which are similar to a real EHA system. Then, we respectively applied PID, fuzzy PID (FPID), and fuzzy linear feedback control (FLFC) before comparing them to have a full view of the outstanding advantages of the proposed algorithm. The simulation results showed that the proposed FLFC algorithm is approximately 99% and 77% superior in performance to the PID and feedback control algorithms, respectively.

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Section: Introductionmentioning

confidence: 99%