Independent Joint Adaptive Fuzzy Control of Robot Manipulator

Kim, Y. T.

doi:10.1080/10798587.2005.10642890

Cited by 6 publications

(3 citation statements)

References 4 publications

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“…It can be seen that both show slight errors at the beginning, after which the tracking performance is good. The robust adaptive fuzzy control method can be used to quickly eliminate disturbances and achieve system stability and robustness [18][19][20][21][22][23][24]. By comparing Figs.…”

Section: Simulation Of a Multi-joint Manipulator Based On Fuzzy Adaptive Control Algorithmsmentioning

confidence: 99%

An Adaptive Fuzzy Control Model for Multi-Joint Manipulators

Han¹,

Zhang²,

Shi³

et al. 2022

Computer Systems Science and Engineering

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Multi-joint manipulator systems are subject to nonlinear influences such as frictional characteristics, random disturbances and load variations. To account for uncertain disturbances in the operation of manipulators, we propose an adaptive manipulator control method based on a multi-joint fuzzy system, in which the upper bound information of the fuzzy system is constant and the state variables of the manipulator control system are measurable. The control algorithm of the system is a MIMO (multi-input-multi-output) fuzzy system that can approximate system error by using a robust adaptive control law to eliminate the shadow caused by approximation error. It can ensure the stability of complex manipulator control systems and reduce the number of fuzzy rules required. Comparison of experimental and simulation data shows that the controller designed using this algorithm has highly-precise trajectory-tracking control and can control robotic systems with complex characteristics of non-linearity, coupling and uncertainty. Therefore, the proposed algorithm has good practical application prospects and promotes the development of complex control systems.

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Section: Simulation Of a Multi-joint Manipulator Based On Fuzzy Adaptive Control Algorithmsmentioning

confidence: 99%

An Adaptive Fuzzy Control Model for Multi-Joint Manipulators

Han¹,

Zhang²,

Shi³

et al. 2022

Computer Systems Science and Engineering

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“…The Adaptive Network-based Fuzzy Inference System (ANFIS) having mixed learning rules (Cao et al 1990) which was equipped to optimize parameters associated with the first Sugeno system (Kim 2003) was employed in this study. Figure 2 shows the configuration of the neuro-fuzzy system used in this study.…”

Section: Adaptive Neuro-fuzzy Inference Systemmentioning

confidence: 99%

Performance Evaluation of CFRP Reinforced Concrete Members Utilizing Fuzzy Technique

Chung

Hur

Park

2018

Int J Concr Struct Mater

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Aging and structural deterioration under severe environments are major causes of damage in reinforced concrete (RC) structures, such as buildings and bridges. Degradations such as concrete cracks, corrosion of steel, and deformation of structural members can significantly degrade the structural performance and safety. Therefore, effective and easy-to-use methods are desired for repairing and strengthening such concrete structures. Various methods for the strengthening and rehabilitation of RC structures have been developed over the past several decades. Recently, FRP composite materials have emerged as a cost-effective alternative to conventional materials for repairing, strengthening, and retrofitting deteriorating/deficient concrete structures, by externally bonding FRP laminates to concrete structural members. The main purpose of this study is to investigate the effectiveness of the FRP retrofit for circular type concrete columns under the framework of the adaptive neuro-fuzzy inference system (ANFIS). Retrofit ratio, strength of existing concrete, thickness, number of layer, stiffness, ultimate strength of fiber, and size of specimens are used as input parameters to predict strength, strain, and stiffness of the post-yielding modulus. These proposed ANFIS models show reliable increased accuracy in predicting the constitutive properties of concrete retrofitted by FRP, compared to the constitutive models suggested by other researchers. which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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“…The robust model of fuzzy combination, so called adaptive fuzzy sliding mode, was introduced to reject the chattering phenomenon and compensate unknown dynamics parameters in the systems by another fuzzy logic controller [8].Some researchers recommended some improvements applied to this method [9][10][11][12][13][14].The adaptive controller defines in two groups: traditional and indirect groups where the first one requires some main parameters of the system and the other, such as fuzzy adaptive method, adjusts the parameter variations by the expert's knowledge [15][16][17][18][19][20][21][22][23][24].…”

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confidence: 99%

Model-Free Adaptive Fuzzy Sliding Mode Controller Optimized by Particle Swarm for Robot Manipulator

Jalali¹,

Piltan²,

Gavahian³

et al. 2013

IJIEEB

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The main purpose of this paper is to design a suitable control scheme that confronts the uncertainties in a robot. Sliding mode controller (SMC) is one of the most important and powerful nonlinear robust controllers which has been applied to many non-linear systems. However, this controller has some intrinsic drawbacks, namely, the chattering phenomenon, equivalent dynamic formulation, and sensitivity to the noise. This paper focuses on applying artificial intelligence integrated with the sliding mode control theory. Proposed adaptive fuzzy sliding mode controller optimized by Particle swarm algorithm (AFSMC-PSO) is a Mamdani’s error based fuzzy logic controller (FLS) with 7 rules integrated with sliding mode framework to provide the adaptation in order to eliminate the high frequency oscillation (chattering) and adjust the linear sliding surface slope in presence of many different disturbances and the best coefficients for the sliding surface were found by offline tuning Particle Swarm Optimization (PSO). Utilizing another fuzzy logic controller as an impressive manner to replace it with the equivalent dynamic part is the main goal to make the model free controller which compensate the unknown system dynamics parameters and obtain the desired control performance without exact information about the mathematical formulation of model

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Independent Joint Adaptive Fuzzy Control of Robot Manipulator

Cited by 6 publications

References 4 publications

An Adaptive Fuzzy Control Model for Multi-Joint Manipulators

An Adaptive Fuzzy Control Model for Multi-Joint Manipulators

Performance Evaluation of CFRP Reinforced Concrete Members Utilizing Fuzzy Technique

Model-Free Adaptive Fuzzy Sliding Mode Controller Optimized by Particle Swarm for Robot Manipulator

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