Position tracking of a 3-PSP parallel robot using dynamic growing interval type-2 fuzzy neural control

Toloue, Shirin Fartash; Akbarzadeh, Marzieh; Akbarzadeh, Alireza; Jalaeian-F, Mohsen

doi:10.1016/j.asoc.2015.07.015

Cited by 26 publications

(5 citation statements)

References 30 publications

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“…For further comparison with alternative approaches, the AMSE values for three motors are presented in Table 2 when different levels of measurement noise with SNR = ½30, 50 dB are present. As seen, the AMSE of the proposed approach of three motors is lower than the AMSEs for Kayacan et al (2011), Toloue et al (2015, and Wang et al (2021). This demonstrates that the proposed approach handles noise powerfully.…”

Section: Discussionmentioning

confidence: 80%

“…where N ( t ) is a uniformly distributed random Gaussian white noise. Because white noise is random, the simulation is done five times, and the average mean squared error (AMSE) values for three motors are included in Table 2 when compared with self-organization-based fuzzy control (Toloue et al, 2015), type-2 fuzzy neural control (Kayacan et al, 2011), and WT-BiLSTM (Wang et al, 2021). The proposed approach shows better performance in comparison.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

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Introducing convex BiLSTM-based controller applied to 3-PSP spatial parallel robot manipulator

Sabahi

2023

Transactions of the Institute of Measurement and Control

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The idea of modeling first and then finding a control approach might have made sense when systems were changing on a vast scale, but not in today’s world where we are in danger of being run over on a highway called the information highway. The feedback phenomenon, which involves a system’s future being influenced by its past, presents a rational conflict in this context. However, we can move beyond these challenges by leveraging the system’s inherent ability to randomly repeat and re-create parts, allowing new features to emerge and the best repetition to become the standard. To address the mentioned challenges, this paper proposes a new structure based on bilateral long short-term memory (BiLSTM) to provide supervising feedback on the nonlinear components comprising friction, Coriolis, and Centrifugal forces in addition to gravity for adapting uncertain variations in the robotic system dynamics. A key characteristic of the proposed BiLSTM-based controller is the incorporation of memory. A new formulation is developed for determining when to recover old memories and to what extent, ensuring the controller remains both adaptive and efficient. Furthermore, given that for system identification, speed is vital; the proposed approach has dealt with the speed category by introducing a convex combination of BiLSTM layers. The stability of the whole structure has also been analyzed, and the updated learning rules have been derived. The simulation results show improved tracking performance and learning time compared to alternative approaches.

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Section: Discussionmentioning

confidence: 80%

Section: Simulation Results and Discussionmentioning

confidence: 99%

Introducing convex BiLSTM-based controller applied to 3-PSP spatial parallel robot manipulator

Sabahi

2023

Transactions of the Institute of Measurement and Control

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“…The work is used interface electronic devise "PCI 1712/1711" through a personal computer. This model will determine the error change and error which will be distinguished between those zero point of the needed position of the particular join with the real position about join, then the control action has been generated and go to driving circuit [17], [18].…”

Section: A Input /Output Robotic Manipulator Hardwarementioning

confidence: 99%

The Optimum Design of Interval Type-2 Fuzzy Controller for 5 DOF Robotic Manipulator

Oglah¹,

Al-Faiz²

2018

Iraqi Journal of ICT

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The robotic manipulator using intelligent controller, have become a research hotspot for most robot manufacturers all over the world. Robotic manipulator systems have disadvantages, such as high nonlinearity , slow response and uncertainty ;Type-2 FLC can be a solution to these drawbacks. A fuzzy controller for 5250 Robotic Manipulator (RM) Lab-Volt (LV) is designed to track the joints position with new axis. The RM joint is processed as single case. The Particle Swarm Optimization and Genetic Algorithm are used. New design was implemented of type-2 fuzzy logic based PSO as four controllers ( (T1FLC,T2FLC,PSOT1FLC, PSOT2FLC). The most important parameters of Type-2 Fuzzy are gains (GAINS, input /output) and foot of print uncertainty (FOU) . Four cases are implemented for each joint (fixed FOU fixed Gain, fixed FOU variable Gain, variable FOU fixed Gain, and variable FOU variable Gain). The experimental results for PSOT2FLC gave a good transient response with minimum integral absolute error IAE, and minimum over shoot .

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“…Sarhadi, Rezaie, and Rahmani implemented adaptive predictive control based on adaptive neuro-fuzzy inference system for a class of nonlinear industrial processes [8]. Mohammadzadeh and Ghaemi synchronizes chaotic systems and identifies nonlinear systems by using recurrent hierarchical type-2 fuzzy neural networks [10], Gaxiola, Melin, Valdez, Castro, and Castillo proposed position tracking of a 3-PSP parallel robot using dynamic growing interval type-2 fuzzy neural control [11]. Given higher control accuracy requirements, a type-II fuzzy neural network has been developed recently, which performs better than a type-I fuzzy neural network.…”

Section: Introductionmentioning

confidence: 99%

“…Given higher control accuracy requirements, a type‐II fuzzy neural network has been developed recently, which performs better than a type‐I fuzzy neural network. Mohammadzadeh and Ghaemi synchronizes chaotic systems and identifies nonlinear systems by using recurrent hierarchical type‐2 fuzzy neural networks , Gaxiola, Melin, Valdez, Castro, and Castillo proposed position tracking of a 3‐PSP parallel robot using dynamic growing interval type‐2 fuzzy neural control . Ganjefar and Solgi designed a Lyapunov stable type‐2 fuzzy wavelet network controller design for a bilateral teleoperation system .…”

Section: Introductionmentioning

confidence: 99%

Identification Recurrent Type 2 Fuzzy Wavelet Neural Network and L2‐Gain Adaptive Variable Sliding Mode Robust Control of Electro‐Hydraulic Servo System (EHSS)

Chen

Yang

et al. 2017

Asian Journal of Control

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An electro-hydraulic servo system (EHSS) is a kind of system with the characteristics of time-variant, serious nonlinearity, parameter and structural uncertainty, and uncertain load disturbance in most cases. These characteristics make it very difficult to realize highly accurate control by conventional methods. In order to solve the above problems, this paper introduces a recurrent type 2 fuzzy wavelet neural network to approximate the unknown nonlinear functions of the dynamic systems through tuning by the desired adaptive law. Based on the identification by recurrent type 2 fuzzy wavelet neural network, a L2 gain design method, combining gain adaptive variable sliding mode control with H infinity control, is proposed for load disturbance, thereby accommodating uncertainties that are the main factors affecting system stability and accuracy in EHSS. In this algorithm, a recurrent type 2 fuzzy wavelet neural network is employed to evaluate the unknown dynamic characteristics of the system and gain adaptive variable sliding mode control to compensate for evaluating errors, and H infinity control to suppress the effect on system by load disturbance. The experiment results show that the proposed system L2 gain design method can make the system exhibit strong robustness to parameter variation and load disturbance.Key Words: Electro-hydraulic servo system (EHSS) nonlinear system, recurrent type 2 fuzzy wavelet neural network, L2 gain adaptive variable sliding mode control, robust control.

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Position tracking of a 3-PSP parallel robot using dynamic growing interval type-2 fuzzy neural control

Cited by 26 publications

References 30 publications

Introducing convex BiLSTM-based controller applied to 3-PSP spatial parallel robot manipulator

Introducing convex BiLSTM-based controller applied to 3-PSP spatial parallel robot manipulator

The Optimum Design of Interval Type-2 Fuzzy Controller for 5 DOF Robotic Manipulator

Identification Recurrent Type 2 Fuzzy Wavelet Neural Network and L2‐Gain Adaptive Variable Sliding Mode Robust Control of Electro‐Hydraulic Servo System (EHSS)

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