## Intelligent Control: Aspects of Fuzzy Logic and Neural NetsWith increasing demands for high precision autonomous control over wide operating envelopes, conventional control engineering approaches are unable to adequately deal with system complexity, nonlinearities, spatial and temporal parameter variations, and with uncertainty. Intelligent Control or self-organising/learning control is a new emerging discipline that is designed to deal with problems. Rather than being model based, it is experiential based. Intelligent Control is the amalgam of the disciplines of Artificial Intelligence, Systems Theory and Operations Research. It uses most recent experiences or evidence to improve its performance through a variety of learning schemas, that for practical implementation must demonstrate rapid learning convergence, be temporally stable, be robust to parameter changes and internal and external disturbances. It is shown in this book that a wide class of fuzzy logic and neural net based learning algorithms satisfy these conditions. It is demonstrated that this class of intelligent controllers is based upon a fixed nonlinear mapping of the input (sensor) vector, followed by an output layer linear mapping with coefficients that are updated by various first order learning laws. Under these conditions self-organising fuzzy logic controllers and neural net controllers have common learning attributes.A theme example of the navigation and control of an autonomous guided vehicle is included throughout, together with a series of bench examples to demonstrate this new theory and its applicability. |

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### Contents

An Introduction to Intelligent Control | 1 |

Introductory Fuzzy Logic | 37 |

Fuzzy Logic Controller Structure and Design | 90 |

The Static Fuzzy Logic Controller | 135 |

SelfOrganising Fuzzy Logic Control | 170 |

Indirect SelfOrganising Fuzzy Logic | 215 |

Adaptive controller implementation issues | 238 |

Case Studies of Indirect Adaptive Fuzzy | 254 |

Neural Network Approximation Capability | 282 |

Polynomial and functional single layer perceptrons | 299 |

The BSpline Neural Network and Fuzzy | 314 |

Mathematical Prerequisites | 358 |

373 | |

### Other editions - View all

Intelligent Control: Aspects of Fuzzy Logic and Neural Nets C J Harris,C G Moore,M Brown Limited preview - 1993 |

### Common terms and phrases

achieved action adaptive algebraic algorithm applications approach approximation associated B-spline basis functions centre changes Chapter closed loop complex composition computational Consider constant constraints continuous convergence defined Definition derivative desired determined dimensional dynamics elements ensure equation error evaluated example exists Figure fixed fuzzy controller fuzzy logic controller fuzzy model fuzzy relation fuzzy sets gain generalisation given Hence illustrates implement implication initial input input/output intelligent control inverse knots knowledge lateral layer learning linear linguistic mapping matrix measure membership function memory method metric neural nonlinear norm operator output parameters performance phase PID controller plant polynomial problem properties region relation represented response rule confidences satisfy selected self-organising ship single SOFLIC space speed structure universe updating variables vector vehicle weight whilst

### References to this book

Data Mining and Computational Intelligence Abraham Kandel,Mark Last,Horst Bunke No preview available - 2001 |