An Introduction to Fuzzy ControlFuzzy controllers are a class of knowledge based controllers using artificial intelligence techniques with origins in fuzzy logic to compute an appropriate control action. These fuzzy knowledge based controllers can be found either as stand-alone control elements or as integral parts of distributed control systems including conventional controllers in a wide range of industrial process control systems and consumer products. Applications of fuzzy controllers have become a well established practice for Japanese manufacturers of control equipment and systems, and are becoming more and more common for their European and American counterparts. The main aim of this book is to show that fuzzy control is not totally ad hoc, that there exist formal techniques for the analysis of a fuzzy controller, and that fuzzy control can be implemented even when no expert knowledge is available. Thus the book is mainly oriented toward control engineers and theorists rather than fuzzy and non-fuzzy AI people. However, parts can be read without any knowledge of control theory and may be of interest to AI people. The book has six chapters. Chapter 1 introduces two major classes of knowledge based systems for closedloop control. Chapter 2 introduces relevant parts of fuzzy set theory and fuzzy logic. Chapter 3 introduces the principal design parameters of a fuzzy knowledge based controller (FKBC) and discusses their relevance with respect to its performance. Chapter 4 considers an FKBC as a particular type of nonlinear controller. Chapter 5 considers tuning and adaptation of FKBCs, which are nonlinear and so can be designed to cope with a certain amount of nonlinearity. Chapter 6 considers several approaches for stability analysis of FKBCs in the context of classical nonlinear dynamic systems theory. |
From inside the book
Results 1-3 of 54
Page 32
... set - point , since e ( k ) = Ysp − y ( k ) > 0 , and increasing , since Ae ( k ) = − ( y ( k ) − y ( k − 1 ) ) < 0. Thus , the current process output is approaching the set - point from below . - - The combination ( negative e ( k ) ...
... set - point , since e ( k ) = Ysp − y ( k ) > 0 , and increasing , since Ae ( k ) = − ( y ( k ) − y ( k − 1 ) ) < 0. Thus , the current process output is approaching the set - point from below . - - The combination ( negative e ( k ) ...
Page 113
... set - point . At the same time , since Ae ( k ) is positive , this means that y is moving towards the set - point . The amount of change Au which the rules of this group introduce to the previous control output u ( k - 1 ) is intended ...
... set - point . At the same time , since Ae ( k ) is positive , this means that y is moving towards the set - point . The amount of change Au which the rules of this group introduce to the previous control output u ( k - 1 ) is intended ...
Page 219
... set - point control performance at a slower rate . Adapting for set - point errors too aggressively could lead to instability . The adaptation is done by modifying the shapes of the membership func- tions in proportion to the undesired ...
... set - point control performance at a slower rate . Adapting for set - point errors too aggressively could lead to instability . The adaptation is done by modifying the shapes of the membership func- tions in proportion to the undesired ...
Contents
Foreword | 1 |
The Mathematics of Fuzzy Control | 37 |
FKBC Design Parameters | 103 |
Copyright | |
6 other sections not shown
Other editions - View all
An Introduction to Fuzzy Control Dimiter Driankov,Hans Hellendoorn,Michael Reinfrank Limited preview - 2013 |
An Introduction to Fuzzy Control Dimiter Driankov,Hans Hellendoorn,Michael Reinfrank Limited preview - 2013 |
An Introduction to Fuzzy Control Dimiter Driankov,Hans Hellendoorn,Michael Reinfrank Snippet view - 1993 |
Common terms and phrases
adaptive controller adaptive FKBC algorithm applications based inference change-of-error computed consider control output variable control system conventional control crisp input defined Definition defuzzification method degree of membership denormalization denoted described domain dynamic elements equation error example fuzzy control Fuzzy Control Systems fuzzy logic fuzzy model fuzzy process model fuzzy propositions fuzzy relation fuzzy set theory given Hellendoorn if-then rules inference engine intersection knowledge representation linear linguistic values linguistic variable Mamdani membership degree membership functions NB NB normalized obtain on-line operation parameters phase plane PI-like FKBC PID-controller process control process output process state variables relation matrix representing the meaning robot rule base rule-antecedent scaling factors Section set of rules set-point Sets and Systems shown in Fig sliding mode control stability analysis step response Sugeno T-norm transient step response u₁ universe of discourse vector Zadeh zero