A systematic method of adaptive fuzzy logic modeling, using an improved fuzzy c-means clustering algorithm for rule generation

Ƶeinali, Meysar; Notash, Leila

doi:10.1109/cca.2005.1507105

Cited by 5 publications

(9 citation statements)

References 18 publications

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“…When all weights are equal to "one", the parameterized reasoning mechanism i.e., equation (13), is identical COA method. In this paper, equation (13) is used to approximate the scanning speed of the process from input-output data of the process as: …”

Section: Review Of Systematic Fuzzy Modellingmentioning

confidence: 99%

“…The knowledge-base of the fuzzy model is built by system identification using the input-output data of the process and based on the systematic method proposed by the first author in [12] and [13].…”

Section: Review Of Systematic Fuzzy Modellingmentioning

confidence: 99%

“…One component is constructed based on the tracking error and desired height which both are available, and the other component is constructed using an adaptive fuzzylogic-based inverse dynamic model of the LMD process. The fuzzy inverse dynamic model of the process is constructed using improved systematic fuzzy modelling method proposed in Zeinali and Notash [12], [13]. The remainder of this paper is organized as follows.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive fuzzy sliding mode control design for laser metal deposition

Ƶeinali

Khajepour

2010

Proceedings of the 2010 American Control Conference

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This paper presents design and implementation of a novel adaptive fuzzy sliding mode control (AFSMC) scheme and its application to the height control of laser metal deposition process (LMD). Because of the uncertainties in modelling, and imperfections in switching devices or delays, the standard sliding mode control (SMC) cannot be used due to chattering. In order to eliminate the chattering, the control scheme proposed in this paper uses an adaptive fuzzy inverse dynamic model of the LMD process constructed from the inputoutput data, and an auxiliary continuous proportional-integral type control law constructed using the algebraic distance of trajectories from the switching surface. The simulation and experimental studies are presented to verify the performance and robustness of the controller and its ability in eliminating the chattering, and tracking the different trajectories. The overall performance, on-line learning capability and the disturbance attenuation property of the proposed control scheme makes it suitable for industrial applications. Manuscript received Sept. 15, 2009. M. Zeinali is with the Mechanical and Mechatronics Eng.

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Section: Review Of Systematic Fuzzy Modellingmentioning

confidence: 99%

Section: Review Of Systematic Fuzzy Modellingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Adaptive fuzzy sliding mode control design for laser metal deposition

Ƶeinali

Khajepour

2010

Proceedings of the 2010 American Control Conference

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“…whereˆ( , , , ) F q q q t is the known part (the approximated part) of the manipulator inverse dynamic model and can be approximated using fuzzy modeling method [3,4]. Fuzzy logic is capable of modeling vagueness, which cannot be described by precise mathematical models; of handling uncertainty; and of supporting human-type reasoning.…”

Section: General Inverse Dynamics Model Of Robot Manipulatorsmentioning

confidence: 99%

“…The controller proposed in this paper aims at building such a control system using a novel approach to combine adaptive fuzzy modeling algorithm, fuzzy c-means clustering algorithm [3,4], adaptive sliding mode control [5] and PID controller. The controller combines all the salient features of fuzzy modeling (FM), neural network modeling, sliding mode control (SMC), proportional integral derivative (PID), and repetitive control (the adaptive fuzzy model makes the controller a model-based learning or internal model learning control).…”

Section: Introductionmentioning

confidence: 99%

Learning Controller Design using Fuzzy Modeling, Sliding Mode Control, and PID Controller for Robots

Ƶeinali

2013

Power and Energy / 807: Intelligent Systems and Control / 808: Technology for Education and Learning

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This paper presents a novel approach to combine three powerful methods of control theory, namely, chattering free adaptive sliding mode control, fuzzy logic-based modeling method, PID control and fuzzy c-means clustering algorithm to build a robust learning control for nonlinear uncertain systems such as advance robot manipulators. The combination not only encompasses the features and capabilities of its components but also the limitations attributed to these techniques may be remedied by each other. To date different combinations of the above mentioned methods presented in the literature each having its own merits and limitations. But, for advance robotics applications, there is a pressing need for the control systems that are able to learn systematically and efficiently during the course of operation, from its own experience, from the demonstration and through supervised fashion as well. The controller proposed in this paper aims at building such a control system. The global stability and robustness of the proposed controller are established using Lyapunov's approach and fundamentals of sliding mode control theory. Based on the simulations and experimental results, the proposed controller performs remarkably well in terms of the tracking error convergence and robustness against uncertainties.

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An ACO-based Approach to Improve C-means Clustering Algorithm

Huang

Gou

2006

2006 International Conference on Computational Inteligence for Modelling Control and Automation and International Conference On

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A systematic method of adaptive fuzzy logic modeling, using an improved fuzzy c-means clustering algorithm for rule generation

Cited by 5 publications

References 18 publications

Adaptive fuzzy sliding mode control design for laser metal deposition

Adaptive fuzzy sliding mode control design for laser metal deposition

Learning Controller Design using Fuzzy Modeling, Sliding Mode Control, and PID Controller for Robots

An ACO-based Approach to Improve C-means Clustering Algorithm

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