Applications Of Fuzzy Logic

Water (or liquid) tank systems are being extensively used these days for domestic purposes as well as in industries like refineries, reservoirs, nuclear power plants etc., Most of the time these systems perform well, but sometimes they face problems regarding the flow of water which leads to the conditions like overflow or underflow. This is caused because the systems will be unable to detect whether the water level has reached the desired level or not and also when the inflow rate of water will not be proportional to the outflow rate of water. To overcome these problems, water level controllers are used. Here, the final control element is a valve whose input is given from the controller whose aim is to maintain the desired set point value

Computers can interpret only true or false values, but humans can interpret the degrees of truth and degrees of false. So, Fuzzy systems were developed so that they can interpret these human actions and are also called as intelligent systems. A fuzzy system was first introduced by an American professor Lofti. A. Zadeh in 1965 by presenting a paper on fuzzy set theory in a seminar. Zadeh showed that the fuzzy logic unlike classical logic can realize values between false (0) and true (1). Basically, he transformed the crisp set into the continuous set. Later, he extended these concepts to mathematics. But this concept was practically applied to commercial goods like automobiles, washing machines by Japanese. Fuzzy logic is mostly applied in the field of control systems as they can interpret human actions. This controller’s use decision making rules like if-then on critical variables to interpolate the output between crisp boundaries. Fuzzy logic is a set of mathematical principles for knowledge representation based on degrees of membership. Fuzzy logic is an approach where human reasoning is imparted in the control

The multiplexer is used to overlap the input signals and pass them onto the next block. The output from the mux is given to the fuzzy logic controller with rule viewer block. The rules which are written using Fuzzy Inference System (FIS) are loaded to the fuzzy logic controller. The fuzzy logic controller with rule viewer displays the complete fuzzy inference process during simulation. The first input to the valve subsystem is received from the fuzzy controller block and the second input consists of a constant block containing the value 0.5 which is the maximum inflow of the tank. The valve performs the control action by multiplying these two input signals. The manipulated variable (the inflow of the tank) enters the tank block and leaves the controlled level, the outflow and the overflow signal. The controlled level output is divided into two signals, one signal is fed to the comparison block and the other signal is fed back to the level input of mux. The signal that is fed back acts as a second input (rate) to the mux. If the level is exceeded, then the overflow signal goes to the