A Lyapunov stable type-2 fuzzy wavelet network controller design for a bilateral teleoperation system

Ganjefar, Soheil; Solgi, Younes

doi:10.1016/j.ins.2015.03.036

Cited by 22 publications

(13 citation statements)

References 48 publications

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“…A new algorithm based on gradient descent from [12] is used to update the recurrent type 2 fuzzy wavelet neural network parameters. O represents the number of the output signals, and the error E should be calculated for the recurrent type 2 fuzzy wavelet neural network to update the parameters (w = (ω j , θ j , a ij , b ij , c1 ij , c2 ij )).…”

Section: Updating the Parameters Algorithmmentioning

confidence: 99%

“…Altin and Sefa designed a dSPACE based adaptive neuro-fuzzy controller of grid interactive inverter, but unlike previous designs is based on type-I fuzzy sets [9]. Ganjefar and Solgi designed a Lyapunov stable type-2 fuzzy wavelet network controller design for a bilateral teleoperation system [12]. 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].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Updating the Parameters Algorithmmentioning

confidence: 99%

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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“…In [57], a bilateral teleoperation system was controlled through a T2 fuzzy wavelet neural network (T2FWNN). This teleoperation system allows a human operator to send commands to a local master manipulator where it then drives a slave manipulator in a remote location.…”

Section: Controller Systems Using It2 Flc and Neural Networkmentioning

confidence: 99%

Review of Recent Type-2 Fuzzy Controller Applications

et al. 2016

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Type-2 fuzzy logic controllers (T2 FLC) can be viewed as an emerging class of intelligent controllers because of their abilities in handling uncertainties; in many cases, they have been shown to outperform their Type-1 counterparts. This paper presents a literature review on recent applications of T2 FLCs. To follow the developments in this field, we first review general T2 FLCs and the most well-known interval T2 FLS algorithms that have been used for control design. Certain applications of these controllers include robotic control, bandwidth control, industrial systems control, electrical control and aircraft control. The most promising applications are found in the robotics and automotive areas, where T2 FLCs have been demonstrated and proven to perform better than traditional controllers. With the development of enhanced algorithms, along with the advancement in both hardware and software, we shall witness increasing applications of these frontier controllers.

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“…Moreover, assuming perfect gravity compensation is the main shortage of the controller design in [21] that is difficult to realize in real applications, especially for large-scale and complex robotic systems. To the best of author's knowledge, only one academic article [22] in this area using Type-2 fuzzy logic system is available that presenting a bilateral teleoperation controller design based on a Type-2 fuzzy wavelet neural network. The Type-2 fuzzy system used in [22] has increased fuzziness in antecedents but remaining crisp coefficients in consequents, which may not be able to F properly describe the uncertainties since only antecedent part is Type-2.…”

Section: Introductionmentioning

confidence: 99%

“…To the best of author's knowledge, only one academic article [22] in this area using Type-2 fuzzy logic system is available that presenting a bilateral teleoperation controller design based on a Type-2 fuzzy wavelet neural network. The Type-2 fuzzy system used in [22] has increased fuzziness in antecedents but remaining crisp coefficients in consequents, which may not be able to F properly describe the uncertainties since only antecedent part is Type-2. Also, only simulation studies are provided in [22].…”

Section: Introductionmentioning

confidence: 99%

Type-2 Fuzzy Modeling and Control for Bilateral Teleoperation System With Dynamic Uncertainties and Time-Varying Delays

Sun

Liao

Ren

2018

IEEE Trans. Ind. Electron.

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A Lyapunov stable type-2 fuzzy wavelet network controller design for a bilateral teleoperation system

Cited by 22 publications

References 48 publications

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

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

Review of Recent Type-2 Fuzzy Controller Applications

Type-2 Fuzzy Modeling and Control for Bilateral Teleoperation System With Dynamic Uncertainties and Time-Varying Delays

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