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

Fuzzy logic control Longitudinal combustion instabili- 1. Only effective when many states can be sensored [Pg.356]

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]

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]

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

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

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

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]

Fusion power, noble gases and, 17 375 Fusion process, 9 278 10 361-364, 365 Fusion reactors, vanadium in, 25 526 FutureGen Program (Department of Energy), 13 845 Fuzzy logic control, 20 698-699 Fuzzy rules, 20 699 F values, 13 252 [Pg.388]

The common characteristic of fuzzy logic and neural networks is that one does not need to know anything about the mathematical model of the process in order to utilize them. In a way it is like the tennis player who can hit the ball without the in-depth knowledge of Newton s laws of motion and how these laws apply to the tennis process. A fuzzy logic controller just mimics the operator (the tennis player) in its responses. [Pg.206]

The angular positional control system shown by the block diagram in Figure 10.36 is to have the velocity feedback loop removed and controller K replaced by a fuzzy logic controller (FLC) as demonstrated by Barrett (1992). The inputs to the FLC [Pg.373]

While the single-loop PID controller is satisfactory in many process applications, it does not perform well for processes with slow dynamics, time delays, frequent disturbances, or multivariable interactions. We discuss several advanced control methods below that can be implemented via computer control, namely, feedforward control, cascade control, time-delay compensation, selective and override control, adaptive control, fuzzy logic control, and statistical process control. [Pg.21]

It may be useful to point out a few topics that go beyond a first course in control. With certain processes, we cannot take data continuously, but rather in certain selected slow intervals (c.f. titration in freshmen chemistry). These are called sampled-data systems. With computers, the analysis evolves into a new area of its own—discrete-time or digital control systems. Here, differential equations and Laplace transform do not work anymore. The mathematical techniques to handle discrete-time systems are difference equations and z-transform. Furthermore, there are multivariable and state space control, which we will encounter a brief introduction. Beyond the introductory level are optimal control, nonlinear control, adaptive control, stochastic control, and fuzzy logic control. Do not lose the perspective that control is an immense field. Classical control appears insignificant, but we have to start some where and onward we crawl. [Pg.8]

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