This paper deals with the simplest fuzzy PID controllers which employ two fuzzy sets for each of the three input variables and four fuzzy sets for the output variable. Mathematical model for a fuzzy PID controller is derived by using asymmetric Γ-function type and L-function type membership functions for each input, asymmetric trapezoidal membership functions for output, algebraic product trian...

This work presents a supervisory control strategy for Networked Control Systems. This shows the identification and control of a plant using fuzzy theory. The identification is perform through input-output data. The fuzzy model incorporates the delay dynamics within of fuzzy rules. The controller is composed by a nominal control and a proposed supervisory control from the fuzzy model. A system o...

This paper explores the learning fuzzy inference systems implemented as adaptive fuzzy-neural networks. The research into application of learning techniques to fuzzy inference systems (FIS) has matured into a family of adaptive fuzzy inference systems (AFIS). In most cases, the learning FIS and AFIS families can be interpreted as a partially connected multilayer feedforward neural network with ...

The identification of a model is one of the key issues in the field of fuzzy system modeling and function approximation theory. There are numerous approaches to the issue of parameter optimization within a fixed fuzzy system structure but no reliable method to obtain the optimal topology of the fuzzy system from a set of input–output data. This paper presents a reliable method to obtain the str...

Mamdani fuzzy models have always been used as black-box models. Their structures in relation to the conventional model structures are unknown. Moreover, there exist no theoretical methods for rigorously judging model stability and validity. I attempt to provide solutions to these issues for a general class of fuzzy models. They use arbitrary continuous input fuzzy sets, arbitrary fuzzy rules, a...

Air conditioning system is developed using mamdani fuzzy model and neuro fuzzy model. It is two input one output system where inputs being the temperature and humidity measured from their respective sensors and the output being the signal that controls the compressor speed. Both the models are simulated using MATLAB Fuzzy logic Toolbox and their results are compared. Keywords— air conditioning,...

The fuzzy rule based inference is known to be a useful tool to capture the behavior of an approximate system in transportation. One of the obstacles of implementing the fuzzy rule based inference, however, has been to calibrate the membership functions of the fuzzy sets used in the rules. This paper proposes a way to calibrate the membership function when a set of input and output data is given...

Development of Load sensor is done in this paper, the input output of the load sensor is taken from the optical fiber sensor and the inputs are load and displacement and output is voltage. Load sensor is implemented by using two models i.e. mamdani fuzzy model and neuro fuzzy model and both the models are simulated using MATLAB, Fuzzy logic Toolbox and the results of the two models are compared...

In this paper, we propose a constructive method to develop a fuzzy system having a monotonic input–output relationship and prove that the developed fuzzy system can approximate any continuously differentiable monotonic function with any desired degree of accuracy. The fuzzy system is constructed with complete and consistent input membership functions and imposes special parametric constraints o...

Fuzzy set theory and fuzzy logic are the convenient tools for handling uncertain, imprecise, or unmodeled data in intelligent decision-making systems. The utility of fuzzy logic in system controls domain is presented in the context of a mobile robot navigation control application. The Takagi-Sugeno controller is a fuzzy model capable of approximating a wide class of nonlinear systems by decompo...