## 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 |

Case Studies of Indirect Adaptive Fuzzy | 254 |

Neural Network Approximation Capability | 282 |

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 adaptive controller algebraic product algorithm associative memory B-spline basis functions BSNN Cartesian product centre closed loop response CMAC compact support composition computational Consider constraints control action control rule base controller design convergence defined defuzzification derivative desired closed loop dimensional discretisation dynamics ensure equation error evaluated example Figure fuzzy controller fuzzy implication fuzzy logic controller fuzzy model fuzzy relation fuzzy rule fuzzy sets generalisation given inference input space input/output intelligent control inverse knots layer learning linear linguistic qualifiers mapping measure membership function metric space multivariable NB NB Neural Networks neurocontrol nonlinear norm normalised output parameters path curvature PB PB PB performance phase plane PID controller piecewise PM PB polynomial predictor priori product operator region relational matrix second order self-organising self-organising fuzzy logic set point SOFLIC Splines Stone-Weierstrass theorem theorem trajectory universe of discourse utilised variables vehicle weight vector whilst zero

### References to this book

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