Design and implementation of a fuzzy supervisor for on-line compensation of nonlinearities: An instability avoidance module

Sanjuán, Marco; Kandel, Abraham; Smith, Carlos A.

doi:10.1016/j.engappai.2005.09.003

Cited by 8 publications

(4 citation statements)

References 23 publications

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“…6. This controlled system has transfer function (6). All tried controlled systems are simulated in PLC by mathematical functions and function blocks.…”

Section: Visualizationmentioning

confidence: 99%

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Implementation of fuzzy logic control based on PLC

2011

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A substantial portion of the literature on fuzzy control deals with the usage of fuzzy rules to implement PID type control and also fuzzy supervisory control.Fuzzy models have received significant attention from various fields of interest. Especially so called the Takagi-Sugeno-type fuzzy model which superbly describes a nonlinear system.In this paper we present implementation of universal fuzzy PS/PD function block implemented to the PLC Simatic S7 300/400. We also present implementation of universal function block of fuzzy expert system and PID regulator for PLC Simatic S7 300/400.Control system with implemented fuzzy expert system and PID regulator and control system with implemented PS/PD regulator with visualizations are designed.

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“…6. This controlled system has transfer function (6). All tried controlled systems are simulated in PLC by mathematical functions and function blocks.…”

Section: Visualizationmentioning

confidence: 99%

“…This approach is discussed in [2], [3]. [4], [6], [7]. Practical case studies of fuzzy gain scheduling as a supervisory control function implementation are in [5], [8], [9].…”

Section: Introductionmentioning

confidence: 99%

Implementation of fuzzy logic control based on PLC

2011

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“…It also for easy integration of human logic into various systems—concepts such as mostly , partially , or a little can be integrated. Some excellent examples of fuzzy control applications include an unmaned landing system and an instability avoidance module within a control loop 74,75. While techniques such as interval‐valued fuzzy sets and fuzzy sets of type 2 have great expressive power and utility, they are also extremely computationally complex, and therefore not the best choice given these goals.…”

Section: Fuzzy Controlmentioning

confidence: 99%

On accurate localization and uncertain sensors

Kramer¹,

Kandel²

2012

Int. J. Intell. Syst.

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The necessity of accurate localization in mobile robotics is obvious—if a robot does not know where it is, it cannot navigate accurately and reach goal locations. Robots learn about their environment via sensors. Small robots require small, efficient, and, if they are to be deployed in large numbers, inexpensive sensors. The sensors used by robots to perceive the world are inherently inaccurate, providing noisy, erroneous data, or even no data at all. Combined with estimation error due to imperfect modeling of the robot, there are many obstacles to successfully localizing in the world. Sensor fusion is used to overcome these difficulties—combining the available sensor data to derive a more accurate pose estimation for the robot. A feeling of “ready‐fire‐aim'' pervades the discipline—filters are chosen on little to no information, and new filters are simply tested against a few peers and claimed as superior to all others. This is folly—the most appropriate filter is seldom the newest. This article provides an overview and in‐depth tutorial of all modern robot localization methods and thoroughly discusses their strengths and weaknesses to assist a robot researcher in the task of choosing the most appropriate filter for their task. © 2012 Wiley Periodicals, Inc.

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“…The formulation and solution of such "fuzzy-parameterized" models is a subdomain of fuzzy set theory that has received relatively little attention, with much more work having been focused on the use of fuzzy logic and reasoning methods. For example, just within the field of chemical engineering, there have been many applications of fuzzy logic and reasoning, in process control (e.g., Gromov et al, 1995;Chen et al, 2001;Andujar & Bravo, 2005;Chen & Chang, 2006;Sanjuan et al, 2006;Zhang et al, 2006;Kaucsár et al, 2007), safety and reliability analysis (e.g., Yu & Lee, 1991;Takeda et al, 1994;Guimarães & Lapa, 2004;Meel & Seider, 2006;Yong et al, 2007;Hassana et al, 2009), knowledge processing (e.g., Arva & Csukas, 1987;Vrba, 1991;Dohnal et al, 1994;Gromov et al, 1996;Johansen & Foss, 1997;Schmitz & Aldrich, 1998;Tsekouras et al, 2002;Claudel et al, 2003;Stephane & Marc, 2008), and other areas.…”

Section: Introductionmentioning

confidence: 99%

Computing fuzzy trajectories for nonlinear dynamic systems

Măceş

Stadtherr

2013

Computers & Chemical Engineering

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One approach for representing uncertainty is the use of fuzzy sets or fuzzy numbers. A new approach is described for the solution of nonlinear dynamic systems with parameters and/or initial states that are uncertain and represented by fuzzy sets or fuzzy numbers. Unlike current methods, which address this problem through the use of sampling techniques and do not account rigorously for the effect of the uncertain quantities, the new approach is not based on sampling and provides mathematically and computationally rigorous results. This is achieved through the use of explicit analytic representations (Taylor models) of state variable bounds in terms of the uncertain quantities. Examples are given that demonstrate the use of this new approach and its computational performance.

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Design and implementation of a fuzzy supervisor for on-line compensation of nonlinearities: An instability avoidance module

Cited by 8 publications

References 23 publications

Implementation of fuzzy logic control based on PLC

Implementation of fuzzy logic control based on PLC

On accurate localization and uncertain sensors

Computing fuzzy trajectories for nonlinear dynamic systems

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