Type-2 Fuzzy Hybrid Controller Network for Robotic Systems

Chao, Fei; Zhou, Dajun; Lin, Chih‐Min; Yang, Longzhi; Zhou, Changle; Shang, Changjing

doi:10.1109/tcyb.2019.2919128

Cited by 55 publications

(34 citation statements)

References 61 publications

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“…For future works, more real-world applications, such as truck backer-upper control [5], navigation of autonomous mobile robot control [27], powered exoskeleton control [28], and robotic control [29], will be considered for more thorough evaluation of the approach. And then, it is worthwhile to compare the performance between the conventional Mamdani inference approach and the Mamdani-based fuzzy interpolation approaches.…”

Section: Resultsmentioning

confidence: 99%

A Study of TSK Inference Approaches for Control Problems

Chao

Yang

2019

Lecture Notes in Computer Science

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Fuzzy inference systems provide a simple yet powerful solution to complex non-linear problems, which have been widely and successfully applied in the control field. The TSK-based fuzzy inference approaches, such as the convention TSK, interval type 2 (IT2) TSK and their extensions TSK+ and IT2 TSK+ approaches, are more convenient to be employed in the control field, as they directly produce crisp outputs. This paper systematically reviews those four TSKbased inference approaches, and evaluates them empirically by applying them to a well-known cart centering control problem. The experimental results confirm the power of TSK+ and IT2 TSK+ approaches in enhancing the inference using either dense or sparse rule bases.

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Section: Resultsmentioning

confidence: 99%

A Study of TSK Inference Approaches for Control Problems

Chao

Yang

2019

Lecture Notes in Computer Science

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“…The parameters used in the proposed MAFC are initialized by using empirical rules so that it is of great theory and practical significance to present the online tuning mechanism of control parameters in an effort to improve the flexibility of the proposed MAFC. Moreover, it is worth investigating the Type-2 fuzzy control and designing Type-2 fuzzy controller for APF due to its better control performance than traditional fuzzy control [36]- [38].…”

Section: Discussionmentioning

confidence: 99%

Experimental Validation of Modified Adaptive Fuzzy Control for Power Quality Improvement

et al. 2020

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This study aims to present a modified adaptive fuzzy control (MAFC) for power quality improvement, whereby adaptive fuzzy control with parameter adaptive capability is employed to emulate optimal control. Different from conventional fuzzy control-based current control method without analytical tools to guarantee basic performance criteria, high-performance control system can be realized using Lyapunov-based adaptive fuzzy control. Also, in order to compensate the fuzzy approximation error (FAE) in conventional fuzzy control, a modified adaptive fuzzy control with adaptive compensator is developed. Unlike the existing approaches, adaptive compensator can estimate the optimal value of FAE with no need of upper bound of FAE. Simulation and experimental results verify that proposed control strategies reveal superior performance under different conditions.

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“…In intelligent robust control processes, Radial-basis function (RBF) networks or Fuzzy-hybrid-networks were activated by various information of control errors [46][47][48]. Since network convergences depended on the richness of the excitation signals, the controllers were difficult to yield outstanding transient control performances [45,49]. As a solution, learning laws of such the networks have been modified by using linear leakage terms [4,50].…”

Section: Introductionmentioning

confidence: 99%

A Precise Neural-Disturbance Learning Controller of Constrained Robotic Manipulators

Xuan

Bae

2021

IEEE Access

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An adaptive robust controller is introduced for high-precision tracking control problems of robotic manipulators with output constraints. A nonlinear function is employed to transform the constrained control objective to new free variables that are then synthesized using a sliding-mode-like function as an indirect control mission. A robust nonlinear control signal is derived to ensure the boundedness of the main control objective without violation of physical output constraints. The control performance is improved by adopting a neural-network model with conditioned nonlinear learning laws to deal with nonlinear uncertainties and disturbances inside the system dynamics. A disturbanceobserver-based control signal is additionally properly injected into the neural nonlinear system to eliminate the approximation error for achieving asymptotically tracking control accuracy. Performance of the overall control system is validated by intensive theoretical proofs and comparative simulation results.

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Type-2 Fuzzy Hybrid Controller Network for Robotic Systems

Abstract: This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination.

Cited by 55 publications

References 61 publications

A Study of TSK Inference Approaches for Control Problems

A Study of TSK Inference Approaches for Control Problems

Experimental Validation of Modified Adaptive Fuzzy Control for Power Quality Improvement

A Precise Neural-Disturbance Learning Controller of Constrained Robotic Manipulators

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