Essay Example on To derive the conclusions from the past conclusions fuzzy logic controllers FLC

CHAPTER 1 INTRODUCTION 1 1 Significance of the Topic To derive the conclusions from the past conclusions fuzzy logic controllers FLC can be modeled to outvie the human-like thinking It will bring more ease to solve the control problems which are hard to depict by mathematical models It is also applicable to plants of higher-order systems The prime motive is their simplicity of operation ease of designs inexpensive maintenance low cost and effectiveness for most linear systems Zadeh has given the framework of all such systems where the fuzzy control logic was illustrated by IF THEN statements Owing to their knowledge-based nonlinear characteristics in order to control systems that contain nonlinearities control strategies must deal with the effect of all these fuzzy controllers are thrivingly applied Since most control strategies based on mathematical model are constrained in their ability to improve transient response as they have been mainly targeting on stability robustness against nonlinearity uncertainties 

We need to have a controller which can efficiently inculcate nonlinear properties and unmodeled effects into its basic design and simultaneously work upon to improve transient responses in all these cases and therefore we opt for fuzzy PI and PD controllers With its ability to simulate a human decision-making process the technology seems to be quiet transparent and natural to the humans Our objective is to find the mathematical models of fuzzy PI PD controllers and to use these models to control the behavior of plants These Mathematical models depend on factors like membership functions triangular norms triangular co norms and defuzzification methods So in this context fuzzy controller does not have a single fixed model In short fuzzy logic is a car with an engineer and driver s seat 

With proper design of component along with set of rules fuzzy control all set to evade the detrimental and complex control problem Once mathematical modeling is done we need not have to bother about the components fuzzification defuzzification control rules inference method of the controller 1 2 Objectives and Scope i Modeling and rigorous analysis of general PI and PD controllers which use triangular type of membership functions for each of the two input variables and trapezoidal type membership function for the output variable ii The output of the fuzzy controller depends on the UoD of the input variables Hence in this work distinct UoDs are considered for different input variables Development of general fuzzy PI PD controller is accomplished with multiple fuzzy sets iii Centre of Sums CoS method is the widely used defuzzification method Controller output is u in the case of PI and u in the case of PD The inference methods implied here are Mamdani minimum and Larsen product iv The two t co norms that are considered in this work are maximum 

Max and bounded sum BS In the literature algebraic product AP and minimum Min are widely used for control purpose and hence they are considered in this work v Illustration of improved system response using multiple fuzzy sets for PI and PD controllers through simulation 1 3 The Approach In the fuzzy control design methodology a set of rules which basically describes how to control the process is written down and then it is incorporated into fuzzy controllers which emulate the decision making process of the human The most significant part of a fuzzy logic controller is a set of linguistic control rules related by the dual concepts of the fuzzy implication and compositional rules of inference Apart from being a heavily used technology these days fuzzy logic control is simple effective and efficient The fundamental objective of using fuzzy control is to provide a user friendly formalism for describing and implementing the ideas we have about how to attain highly efficient performance control 

To use mathematical models of fuzzy controller models for control the way the conventional controllers are used is the prime objective of deriving it One does not have to coordinate with the constituents fuzzification defuzzification control rules inference method etc of fuzzy controllers once mathematical models of fuzzy controllers are made accessible To continue to reveal mathematical models of the general fuzzy PI and PD controllers is the primary aim of this thesis As few of the models developed herein are qualitatively efficient and completely distinct from the models already reported in the literature therefore the results illustrated in this thesis are significantly useful to control community 1 4 Organization of thesis in Chapter 1 gives a brief discussion about Fuzzy Logic Control FLC and its applications ii Chapter 2 deals with Literature Review iii Chapter3 discusses about Configuration of Fuzzy PI and PD controllers It also explains about each of its components iv Chapter 4 gives brief about basics of DC Motor v Chapter 5 explains Single Link Manipulator vi Chapter 6 discusses mathematical modeling of the following general fuzzy PI and PD controllers Class 1 Algebraic Product t norm Bounded sum t co norm Larsen Product inference and Center of Sums COS defuzzification method Class 2 Algebraic Product t norm Maximum t co norm Larsen Product inference and Center of Sums COS defuzzification method Class 3 Minimum t norm Bounded sumt co norm Larsen Product inference and Center of Sums COS defuzzification method vii Chapter 7 contains simulation results obtained using conventional and fuzzy PI and PD controllers on two different types of non linear systems viii Chapter 8 gives conclusion of the results