Guided by‐Prof. K. N. WAKCHAURE
“ FUZZY LOGIC & AND IT’S APPLICATIONS”
CONTENTS IntroductionApplicationsFuzzy logic representationFuzzy logic in control systemBenefits using fuzzy logic Fuzzy inference system & Fuzzy rulesConclusionReferences
INTRODUCTION Definition of fuzzy
Fuzzy – “not clear, distinct, or precise; blurred”
Definition of fuzzy logic
A form of knowledge representation suitable for notions that
cannot be defined precisely, but which depend upon their
contexts.
APPLICATIONS
Boiler temp and water level control Tool wear conditions by fuzzy logic Optimization of laser welding process Video Cameras Automatic Transmissions Control units Expert systems
TRADITIONAL REPRESENTATION OF FL
Slow FastSpeed = 0 Speed = 1
bool speed; get the speed if ( speed == 0) {// speed is slow} else {// speed is fast}
FUZZY LOGIC REPRESENTATION
Slowest
[ 0.0 – 0.25 ]
Slow
[ 0.25 – 0.50 ]
Fast
[ 0.50 – 0.75 ]
Fastest
[ 0.75 – 1.00 ]
For every problem must represent in terms of fuzzy sets.
What are fuzzy sets?
FUZZY LOGIC IN CONTROL SYSTEMS
Fuzzy Logic provides a more efficient and resourceful way
to solve Control Systems.
Some Examples
Temperature Controller
Anti – Lock Break System ( ABS )
TEMPERATURE CONTROLLER
The problem Change the speed of a heater fan, based off the room
temperature and humidity.
A temperature control system has four settings Cold, Cool, Warm, and Hot
Humidity can be defined by: Low, Medium, and High
Using this we can define the fuzzy set.
BENEFITS OF USING FUZZY LOGIC
Nonlinear and dynamic in nature Inputs for Intel Fuzzy ABS are derived from
Brake 4 WD Feedback Wheel speed Ignition
Outputs Pulsewidth Error lamp
ANTILOCK BRAKING SYSTEM
PROPOSED METHODOLOGY FOR BOILER CONTROL The proposed method consists of two sections. First section is to develop a steam temperature monitoring and control sys‐ tem and the second section consists of water level control. For both of the sections Fuzzy Logic Control will be used. which will indicate the water level inside the boiler chamber. The microcontroller will take the temperature sensor output and level indicator output as the two inputs for the Fuzzy Inference System .After fuzzification of the inputs and applying suitable rules and de‐fuzzifyingthe output the microcontroller generates appropriate control signals.
Fig: Proposed FLC Based boiler control
The Fuzzy Inference system fuzzifies the inputs and applies suitable rules and calculates the defuzzified value. It then decides the suitable control action to be per‐formed..The microcontroller gives command to perform required control action to turn the heater ON/OFF for safe operation of the boiler.
• TEMPERATURE CONTROL
WATER LEVEL CONTROL
The water level control is also an important parameter for boiler control. The water level inside the boiler chamber needs to be controlled because of changing load demand When there is a need of more steam water level should be high and when there is a need of less steam the water level should be low .To maintain the water level inside the boiler chamber a level indicator circuit is used and the circuit is interfaced with the microcontroller. The Fuzzy Inference System stored inside the microcontroller then fuzzifies the inputs and applies suitable rules and then gives the defuzzified values which is then processed by the microcontroller to give the suitable control action to turn ON/OFF the inlet pump and OPEN/CLOSE the outlet valve.
FUZZY INFERENCE SYSTEM
APPROACH Usage
1. Define the control objectives and criteria What am I trying to control? What do I have to do to control the system? What kind of response do I need? What are the possible (probable) system failure modes?
2. Determine the input and output relationships Choose a minimum number of variables for input to the FL
engine
3. Use the rule-based structure of FL Break the control problem down into a series of rules
4. Create FL membership functions Define the meaning (values) of Input/Output terms used in the
rules
5. Test, evaluate, tune and retest
FUZZY RULES
Fig: Input membership function
Fig: Input membership function
Output membership functions
CONCLUSION
Fuzzy logic provides an alternative way to
represent linguistic and subjective attributes of the
real world in computing.
It is able to be applied to control systems and other
applications in order to improve the efficiency and
simplicity of the design process.
REFERENCES
[1]. R. Lagneborg, “New steels and steel applications for vehicles”, Materials Design, 1991,12(1), pp3–14.
[2] A P Tadamalle, Y P Reddy and E Ramjee, (2013) Influence of laser welding process parameters on weld pool geometry and duty cycle”, International Journal of Advances in Production Engineering and Management, Vol8, No. 1, pp 53‐60.
[3] Smith Gregory C, Lee Samson S, “A method for detecting tool wear on a CNC lathe using a doppler radar detector”,
[4]Springer‐Verlag London Limited, 2004 2. Ko, T. J., and Cho, D. W., ”Tool Wear Monitoring in Diamond Turning by Fuzzy Pattern Recognition”, ASME J. Eng. Ind., 116, No. 2, 1994.
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