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THE SEMINAR OF MASTER PROJECT The Implementation of Feedforward-Feedback Fuzzy Logic Algorithm for Level Control System at Process Mini-Plant Measurement.

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Presentation on theme: "THE SEMINAR OF MASTER PROJECT The Implementation of Feedforward-Feedback Fuzzy Logic Algorithm for Level Control System at Process Mini-Plant Measurement."— Presentation transcript:

1 THE SEMINAR OF MASTER PROJECT The Implementation of Feedforward-Feedback Fuzzy Logic Algorithm for Level Control System at Process Mini-Plant Measurement Laboratory FH-Lausitz Measurement Laboratory FH-Lausitz FachHochschule Lausitz University of Applied Sciences by R.Danu Setyo Nugroho (Matrikel.Nr ) Supervisor Prof.Dr.Ing. E.Stein Co-Supervisor Dipl.Ing (FH) Mario Sader Senftenberg, 7th July 2004

2 FachHochschule Lausitz University of Applied Sciences Discussion Topic Discussion : 1. Introduction 2. Basic Control Theory 3. Fuzzy Logic Algorithm Theory 4. Fuzzy Logic Control Design 5. Validation 6. Summary

3 FachHochschule Lausitz University of Applied Sciences INTRODUCTION Background Background Process Mini-Plant at Measurement Laboratory ON/OFF Control Mode Set Point

4 FachHochschule Lausitz University of Applied Sciences Set Point q in q out To keep the set point : q in = q out CONTINUOUS CONTROL The Goal The Goal INTRODUCTION

5 FachHochschule Lausitz University of Applied Sciences INTRODUCTION Problems Problems Lack of Parameter Systems Information GcGcGcGc GmGmGmGm GtGtGtGt ProcessSP + - econtroller control valve level transmitter mini plant Block Diagram of Close Loop Systems ? ?

6 FachHochschule Lausitz University of Applied Sciences INTRODUCTION Set Point Problems Problems Change Load Change Normal Load Load Change Error q in > q out q in should be reduced Set point Changing New Set Point in q in should be increased

7 FachHochschule Lausitz University of Applied Sciences INTRODUCTION SOLUTION SOLUTION Feedforward – Feedback Fuzzy Logic Control

8 FachHochschule Lausitz University of Applied Sciences process characteristics process characteristics BASIC CONTROL SYSTEMS time Step Response 95% 63% τdτdτdτd τcτcτcτc τrτrτrτr dead time τ d dead time τ d time constant τ c time constant τ c response time τ r response time τ r actual level

9 FachHochschule Lausitz University of Applied Sciences criteria of good control criteria of good control BASIC CONTROL SYSTEMS SP2 CV2 SP1 CV1 time time quarter amplitude decay quarter amplitude decay critical damping critical damping minimum absolute error minimum absolute error t0t0t0t0 t0t0t0t0 a a/ 4 minimum absolute error minimum absolute error |E|dt= minimum

10 FachHochschule Lausitz University of Applied Sciences criteria of good control criteria of good control BASIC CONTROL SYSTEMS critical damping critical damping SP2 CV2 SP1 CV1 time time t0t0t0t0 t0t0t0t0 over damping under damping critical damping

11 FachHochschule Lausitz University of Applied Sciences control systems control systems BASIC CONTROL SYSTEMS feedback control systems feedforward control systems valve sprayed 90 o 180 o 270 o 5m 10m 15m calibration set controller controller process process sp disturbance

12 FachHochschule Lausitz University of Applied Sciences FUZZY LOGIC ALGORITHM history history The concept of Fuzzy Logic (FL) was conceived by Lotfi Zadeh, a professor at the University of California at Berkley, and presented not as a control methodology, but as a way of processing data by allowing fuzzy set membership rather than crisp set membership or non-membership. advantages advantages free of mathematic modeling systems free of mathematic modeling systems ( e.g Laplace transform, transfer function systems are not required) empirically-based on operators experience rather than technical empirically-based on operators experience rather than technical understanding of control systems understanding of control systems ( The advance knowledge of control theory is not required) flexible and easy in design flexible and easy in design (e.g MIMO,MISO,SISO, rule base determination, simple aritmethic) fun fun

13 FachHochschule Lausitz University of Applied Sciences FUZZY LOGIC ALGORITHM Fuzzy Set Vs Crisps Set Fuzzy Set Vs Crisps Set 70 0 C warmhot F s : X [0,1] F s : X [0,1] [1] [0] Crisps Set (F S ) How about T = 69 o C ? Upss, it is warm !, it isnt hot at all ! Are you happy with that ? warm 25 0 C 75 0 C 65 0 C 85 0 C hot Fuzzy Set (μ f ) μf : X |0,1|

14 FachHochschule Lausitz University of Applied Sciences FUZZY LOGIC ALGORITHM The Properties of Fuzzy Set The Properties of Fuzzy Set μ f (x) cold warm hot oCoCoCoC universal of discorse scope domain Label crisps input degre of fuzzy membership function

15 FachHochschule Lausitz University of Applied Sciences FUZZY LOGIC ALGORITHM Operation Logic Operation Logic BooleanLogical FuzzyLogical The most common used operation logic

16 FachHochschule Lausitz University of Applied Sciences FUZZY LOGIC ALGORITHM Fuzzy Inference Engine Fuzzy Inference Engine fuzzyfication defuzzyfication rule evaluation rule base crispsinputscrispsoutputs controller

17 FachHochschule Lausitz University of Applied Sciences FUZZY LOGIC ALGORITHM Simple Fuzzy Logic Application Simple Fuzzy Logic Application Home sprinkler system How long the watering duration should take? It depends on the air temperature and soil moisture Air temperature Soil moisture FUZZY..FUZZY duration

18 FachHochschule Lausitz University of Applied Sciences FUZZY LOGIC ALGORITHM Fuzzification for air temperature cool warm hot 1 0μ305080Temp/C fuzzification C W H T_in T_in μ h = (temp_in – 50) / gradient μ h = (60 – 50) / 30 μ h = μ w = (temp_in – 80) / gradient μ w = (60 – 80) / -30 μ w =

19 FachHochschule Lausitz University of Applied Sciences FUZZY LOGIC ALGORITHM Fuzzification for soil moisture dry moist wet 1 0μ01525Moist% fuzzification D M W T_in T_in 8 %

20 FachHochschule Lausitz University of Applied Sciences FUZZY LOGIC ALGORITHM Rule Evaluation Which knowledge base should be used ? operators experiences 1.If temperature is hot AND moister is wet THEN watering duration is short 2.If temperature is hot AND moister is moist THEN watering duration is medium 3.If temperature is hot AND moister is dry THEN watering duration is long 4.If temperature is warm AND moister is wet THEN watering duration is short 5.If temperature is warm AND moister is moist THEN watering duration is medium 6.If temperature is warm AND moister is dry THEN watering duration is long 7.If temperature is cool AND moister is wet THEN watering duration is short 8.If temperature is cool AND moister is moist THEN watering duration is medium 9.If temperature is cool AND moister is dry THEN watering duration is long If antecedence 1 AND antecedence 2 THEN consequent

21 FachHochschule Lausitz University of Applied Sciences FUZZY LOGIC ALGORITHM Rule Evaluation cool warm hot rulestrength wet moist dry temp moisture Mamdani Min-Max Operation If temperature is warm (0.66) AND moisture is moist (0.56) THEN watering duration is medium (0.56) If temperature is hot (0.33) AND moisture is dry (0.43) THEN watering duration is long (0.33) Y = Min (a,b) SSSM M M L L L = = = = = = = = = SSSM M M L L L Y = Max (a,b)

22 FachHochschule Lausitz University of Applied Sciences FUZZY LOGIC ALGORITHM Singleton Defuzzification Singleton Defuzzification watering duration (min) 0 1 μshortmediumlong S M L time defuzzification COG Center Of Gravity = 0 x x x (COG) = 48.2 minute

23 FachHochschule Lausitz University of Applied Sciences Strategy of Control Design diagram block system diagram block system Process SP + - e Feedback Fuzzy Controller + + Feedforward

24 Level Membership Function μ(f) 19,30 19,30 29,35 29,35 37,90 37,90 50,35 50,35 very low low low medium medium high high very high h(cm) ,60 FachHochschule Lausitz University of Applied Sciences Strategy of Control Design fuzzification of feedforward systems fuzzification of feedforward systems The degree of membership function |1,0| very lowlowmediumhighvery high 18 0,210, ,9490, ,5430, ,1370, ,220,770 Level (cm)

25 FachHochschule Lausitz University of Applied Sciences Strategy of Control Design rule evaluation of feedforward systems rule evaluation of feedforward systems 1. IF the level is very high THEN the opening of valve is very big 2. IF the level is high THEN the opening of valve is big 3. IF the level is medium THEN the opening of valve is medium 4. IF the level is low THEN the opening of valve is small 5. IF the level is very low THEN the opening of valve is very small 1/2big 1 0 opening of valve 1 0 1/2 opening of valve big THEN level 1 0 1/2highIF fuzzification.videfuzzification.vi 5 rule

26 FachHochschule Lausitz University of Applied Sciences Strategy of Control Design defuzzification of feedforward system defuzzification of feedforward system 8 9 very small small medium big very big volt μ(f) Opening Valve Membership Function Set PointThe degree of membership function [1,0]Control Signal very lowlowmediumhighvery high 1800,9380,06005,78 V 2000,540,36006,05 V 25000,40,606,55 V ,60,46,86 V ,10,97,77 V Level (cm)

27 FachHochschule Lausitz University of Applied Sciences Strategy of Control Design fuzzification of feedback system fuzzification of feedback system NB NB NS ZE PS PB e 1 0 Error membership function μ(f) LNB PVB XLNB - 0, ,5 -1, The degree of membership function [1,0] XLNBLNBNBNSZEPSPBLPB ,9000,80, , , ,40,6 1, error (cm)

28 FachHochschule Lausitz University of Applied Sciences Strategy of Control Design rule evaluation of feedback system rule evaluation of feedback system 1.IF error is XL Negative Big THEN corrective valve is XL Negative Big 2.IF error is Large Neg. Big THEN correction valve is Large Neg. Big 2.IF error is Large Neg. Big THEN correction valve is Large Neg. Big 3.IF error is Negative Big THEN correction valve is Negative Big 4.IF error is Negative Small THEN correction valve is Negative Small 5.IF error is Zero Error THEN correction valve is Zero 6.IF error is Positive Small THEN correction valve is Positive Small 7.IF error is Positive Big THEN correction valve is Positive Big 8.If error is Large Pos Big THEN correction valve is Large Pos Big Based on P Controller

29 FachHochschule Lausitz University of Applied Sciences Strategy of Control Design defuzzification of feedback system defuzzification of feedback system NB NS NS ZE ZE PS PS PB PB volt 1 0 Defuzzification ofCorrective Valve Defuzzification of Corrective Valveμ(f) LNB LNB LPB XLNB XLNB -1, , The degree of membership function [1,0] XLNBLNBNBNSZEPSPBLPB ,9000,80, ,34 -0, ,5 1, ,40,61,8 1, Error (cm) correcting signal (v)

30 FachHochschule Lausitz University of Applied Sciences

31 FachHochschule Lausitz University of Applied Sciences VALIDATION THE COMPARISON OF PERFOMANCE CONTROLLER STEP RESPONS TESTING STEP RESPONS TESTING FUZZY LOGIC Vs PI CONTROLLER FUZZY LOGIC Vs PI CONTROLLER SET POINT CHANGING SET POINT CHANGING LOAD CHANGE LOAD CHANGE Note : Parameter PI Controller are P = 1,33 and I = 120/s. τsτs 95% of 25

32 FachHochschule Lausitz University of Applied Sciences VALIDATION LOAD CHANGE TESTING Note : - 25 cm level is the normal level without load change - Error open loop in 450% load change is 7 cm error

33 FachHochschule Lausitz University of Applied Sciences VALIDATION SET POINT CHANGE TESTING SET POINT CHANGE TESTING

34 FachHochschule Lausitz University of Applied Sciences SUMMARY -The performance of Fuzzy Logic Control here is better then PI Controller in transient response. in transient response. -The performance of PI Controller here is better then Fuzzy Logic Control in steady state response in steady state response The number of fuzzy memberships label that is used influence the smoothness - The number of fuzzy memberships label that is used influence the smoothness of the controllers reaction. of the controllers reaction. Fuzzy Logic Control is able to avoid both of overshoot and undershoot condition - Fuzzy Logic Control is able to avoid both of overshoot and undershoot condition Even plant has two tank, it is catagorized as first order systems. - Even plant has two tank, it is catagorized as first order systems. Because the second tank doesnt act as capacitive element during normal process. Because the second tank doesnt act as capacitive element during normal process.

35 FachHochschule Lausitz University of Applied Sciences SUMMARY RECOMMENDED FUTURE RESEARCH TOPICS Fuzzy Logic Control based on the PI controller - Fuzzy Logic Control based on the PI controller Adaptive Neuro-Fuzzy Logic Control - Adaptive Neuro-Fuzzy Logic Control Self Tuning or Gain Scheduling PI Controller using Fuzzy Logic Algorithm - Self Tuning or Gain Scheduling PI Controller using Fuzzy Logic Algorithm THANK YOU FOR YOUR ATTENTION


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