Control of nonlinear systems by fuzzy observer-controller with unmeasurable premise variables

Liu, Chuang; Lam, Hak‐Keung; Tsai, Shun-Hung; Chen, Chin‐Sheng

doi:10.1109/aris.2015.7158232

Cited by 8 publications

(2 citation statements)

References 34 publications

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“…In this paper fuzzy logic theory has been applied to control ball and beam system as it provides a simple and realistic control based on human logic of reasoning (Moezi et al, 2014). Fuzzy controllers are providing a systematic and effective approach for control of many nonlinear systems (Liv et al, 2015). These controllers have replaced manual tuning of nonlinear systems resulting in better system response and accuracy.…”

Section: Design Of Fuzzy Controllersmentioning

confidence: 99%

Neural Fuzzy Control of Ball and Beam System

Kharola

Patil

2017

International Journal of Energy Optimization and Engineering

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This paper presents an offline control of ball and beam system using fuzzy logic. The objective is to control ball position and beam orientation using fuzzy controllers. A Matlab/Simulink model of the proposed system has been designed using Newton's equations of motion. The fuzzy controllers were built using seven gbell membership functions. The performance of proposed controllers was compared in terms of settling time, steady state error and overshoot. The simulation results are shown with the help of graphs and tables which illustrates the effectiveness and robustness of proposed technique.

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Section: Design Of Fuzzy Controllersmentioning

confidence: 99%

Neural Fuzzy Control of Ball and Beam System

Kharola

Patil

2017

International Journal of Energy Optimization and Engineering

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“…Authors in [17] considered a fuzzy-model-based observer-controller using an unmeasurable premise variable, they relax the stability conditions by considering the membership functions information's in stability conditions.…”

Section: Introductionmentioning

confidence: 99%

Premise Variables Cases for LMI based TS Fuzzy Observers

Hazerchi¹,

Illoul²

2018

IJCA

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This paper deal with the control of TS fuzzy system via fuzzy observer such as we consider the stability conditions of the augmented system ( TS fuzzy model + fuzzy observer) using different cases of the premise variables.We propose a new classification while using the premise variables for both TS fuzzy model and fuzzy observer. We show the existing differences when using premise variable with same or different nature (either measurable or unmeasurable) for TS fuzzy model and fuzzy observer. The LMI tools are used to represent the stability conditions and the design procedure.We present in this paper a comment about previous result of the augmented system stability (state and observer) in the unmeasurable case of the premise variable cited in [1] of Tanaka et al.

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