The size of the universes of diseourse will depend upon the expeeted range (usually up to the saturation level) of the input variables. Assume for the system about to be eonsidered that e has a range of 6 and ce a range of 1. [Pg.331]

The number and shape of fuzzy sets in a partieular universe of diseourse is a tradeoff between preeision of eontrol aetion and real-time eomputational eomplexity. In this example, seven triangular sets will be used. [Pg.331]

This style of fuzzy conditional statement is often called a Mamdani -type rule, after Mamdani (1976) who first used it in a fuzzy rulebase to control steam plant. [Pg.332]

If (c) above is used, then knowledge of the plant mathematical model is not required. [Pg.332]

Equation (10.23) is referred to as the max-min inference process or max-min fuzzy reasoning. [Pg.333]

Fuzzy inference is therefore the process of mapping membership values from the input windows, through the rulebase, to the output window(s). [Pg.335]

for given error of 2.5, and a rate of change of error of —0.2, the control signal from the fuzzy controller is 3.83. [Pg.336]

The inverted pendulum problem is a elassie example of produeing a stable elosed-loop eontrol system from an unstable plant. [Pg.337]

The elements of K ean be obtained by seleeting a set of desired elosed-loop poles as deseribed in seetion 8.4.2, and applying one of the three teehniques diseussed. [Pg.339]

The rulebase ean be extended up to 22 rules by a further set of 11 rules replaeing 6 with X and 6 with x. [Pg.340]

a similar inferenee proeess oeeurs with x and x. Following defuzzifieation, a erisp eontrol foree F(t) is obtained. [Pg.340]

Fuzzy Logic Control The apphcation of fuzzy logic to process control requires the concepts of Fuzzy rules and fuzzy inference. A fuzzy rule, also known as a fuzzy IF-THEN statement, has the form [Pg.735]

Sometimes fuzzy logic controllers are combined with pattern recognition software such as artificial neural networks (Kosko, Neural Networks and Fuzzy Systems, Prentice Hall, Englewood Cliffs, New Jersey, 1992). [Pg.735]

Fuzzy logic control systems 10.2.1 Fuzzy set theory [Pg.326]

MATLAB Fuzzy Inference System (FIS) editor can be found in Appendix 1. Figure 10.16 shows the control surface for the 11 set rulebase fuzzy logic controller. [Pg.344]

Self-Organizing Fuzzy Logic Control (SOFLC) is an optimization strategy to create and modify the control rulebase for a FLC as a result of observed system performance. The SOFLC is particularly useful when the plant is subject to time-varying parameter changes and unknown disturbances. [Pg.344]

Fig. 10.16 Control surface for 11 set rulebase fuzzy logic controller. |

Fig. 10.17 Self-Organizing Fuzzy Logic Control system. |

Fig. A1.8 Simulink implementation of inverted pendulum fuzzy logic control problem. |

Predictive self-organizing fuzzy logic control (PSOFLC) 364 Pressure 27 difference 8 [Pg.445]

In addition to single-loop process controllers, products that have benefited from the implementation of fuzzy logic are [Pg.735]

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