Implementing a Fuzzy Logic Control of a Shower

Transcription

1 Implementing a Fuzzy Logic Control of a Shower ABSTRACT Krishankumar Assistant Professor, Department of Electrical Engineering, Guru Jambheshwar University of Science & Technology, Hissar, Haryana, India International Journal of Research in Electrical & Electronics Engineering Volume 4, Issue 1, January-March, 2016, pp Online: Print: , DOA: IASTER 2016, This system provides an automatic control of temperature and water flow of a shower. The shower system consists of a hot water tap and a cold water tap, each of which supplies water at a particular temperature and at a certain rate. Keywords: Fuzzy Logic, Shower System, Defuzzification, Prefuzzified Fuzzy. INTRODUCTION The initial settings for the both cold tap and hot tap of the shower are randomly selected and the target temperature is between -20 and +20 degrees Celsius and the target flow rate and temperature of the current combination, and must recommended adjustments to the taps in order to achieve the optimum flow and temperature in as few iterations as possible. The shower system uses the Mamdani s fuzzy inference method. Mamdani-type inference expects the output membership functions to be fuzzy sets. After aggregation process, there is a fuzzy set for each output variable that needs de-fuzzification. In many cases, it is much more efficient to use a single spike as the output membership function rather than a distributed fuzzy set. This can be thought of as a prefuzzified fuzzy set. It enhances the efficiency of the defuzzification process because it greatly simplifies the computation required by the more general Mamdani method. Figure.1 SIMULINK Model

2 II. PROCESS FLOW The fuzzy controller for shower system switches between two input controllers (namely, temperature and flow controllers) and produces two required outputs (cold and hot). This output will control the valve opening. The fuzzy inference systems is shown in figure.7. The membership functions for all the input and output controllers are shown in figure.8 to 11. There are nine rules defined in the fuzzy logic controller. The rules used are: If the temp is cold and flow rate is soft then cold is open slow, hot is open fast. If temp is cold and flow is good then cold is close slow, hot is open slow. If temp is cold and flow is hard then cold is close fast, hot is close slow. If temp is good and flow is soft then cold is open slow, hot is open slow. If temp is good and flow is good then cold is steady, hot is steady. If temp is good and flow is hard then cold is close slow, hot is close fast. If temp is hot and flow is soft then cold is open fast, hot is open slow. If temp is hot and flow is good then cold is open slow, hot is close slow. If temp is hot and flow is hard then cold is close slow, hot is close fast. Figure.2. FLC Inference Structure Figure.3. Membership Functions of Input Variable 1 2

3 Figure.4. Membership Functions of Input Variable 2 Figure.5. Membership Functions of Output Variable 1 Figure.6. Membership Functions of Output Variable 2 3

4 Figure.7. Rule Editor The SIMULINK block diagram of the shower system is shown in figure.1 there are four subsystems in this model such as, flow set point, time set point, cold water valve and hot water valve. The flow set point subsystem consists of signal generator that generates the flow rate variation. The input signal supplied by the signal generator is square wave type with amplitude of 0.2 and natural frequency of 0.3 rad/sec. the temperature set point subsystem consists of signal generator that generates the temperature variations. The input signal supplied with the amplitude 4 and natural frequency.2143 rad/sec. the cold water valve system and the hot water valve system receive signals from the fuzzy logic controller (FLC). Figure.8. Under Mask Structure of Hot Water Valve Figure.9 Under Mask Structure of Cold Water Valve 4

5 Figure.10 Under Mask Structure of Flow Variation Figure.11 Under Mask Structure of Temp Variation RESULTS AND DISCUSSION The step response of the flow rate and the temperature controls of the shower system are shown in figure 2 and 3. It is seen from the figures that the step response of the shower system is considerably efficient with minimum overshoot, fast rise time and zero steady state error. The surface view of the fuzzy logic controlled shower system is shown in figure 14. Figure 13 shows the rule viewer for the following input: Input temp = 15, and flow rate = 0.3 From figure it is seen that for the given input, the outputs are cold =.154 and hot =.186. Figure.12 Step Response of Flow Controller Figure.13 Step Control of Temperature Controller 5

6 Figure.14. Rule Viewer CONCLUSION The fuzzy logic controller has been successfully implemented in this process control system. It is seen that the flow control and temperature control of the shower system give good step response with the fuzzy controller. It is also seen that the FLC has combined the flow and temperature controllers into one controller. Figure.15 Surface Viewer REFERENCES [1] S. Rajasekhran, G. A. Vijayalakshmi Pai, Neural Networks, Fuzzy Logic and Genetic Algorithm. [2] Design of Fuzzy Controllers: [3] Fuzzy control system: en.wikipedia.org/wiki/fuzzy _control_system [4] Current Mirror: _mirror 6