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DESIGN AND SIMULATION OF FUZZY LOGIC CONTROLLER USING MATLAB

I Planned to give a specific training on Fuzzy Logic Controller using MATLAB simulation. This type of intelligent controller is very useful for the research work in all discipline.

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DESIGN AND SIMULATION OF FUZZY LOGIC CONTROLLER USING MATLAB

  1. 1. 21.04.2018 muruganm1@gmail.com 1 I hopeyourdayis Beautiful…….
  2. 2. TRAINING ON DESIGN AND SIMULATION OFTRAINING ON DESIGN AND SIMULATION OF FUZZY LOGIC CONTROLLER USINGFUZZY LOGIC CONTROLLER USING MATLABMATLAB Dr.M.MURUGANANDAM,M.E.,Ph.D Associate Professor, Department of Electrical and Computer Engg, Institute of Technology, Wollega University, Nekemte, Ethiopia
  3. 3. 21.04.2018 muruganm1@gmail.com 3 Before we beginBefore we beginss…… some intellectual people have saidsome intellectual people have said …… Precision is not truth. —Henri Matisse So far as the laws of mathematics refer to reality, they are not certain. And so far as they are certain, they do not refer to reality. —Albert Einstein As complexity rises, precise statements lose meaning and meaningful statements lose precision. -Lotfi A. Zadeh
  4. 4. muruganm1@gmail.com TOPICS TO BE DISCUSSED What is fuzzy and fuzzy logic? Basic Applications of Fuzzy Logic? About simulation What is MATLAB? MATLAB SIMULINK Mathematic operation Power Electronic Drive circuit Design of Fuzzy Logic Controller 21.04.2018 4
  5. 5. 21.04.2018 muruganm1@gmail.com 5 Meaning OF Fuzzy  Most of the phenomena we encounter everyday areMost of the phenomena we encounter everyday are impreciseimprecise - the imprecision may be associated with their- the imprecision may be associated with their shapes, position, color, texture, semantics that describeshapes, position, color, texture, semantics that describe what they arewhat they are  Fuzziness primarily describesFuzziness primarily describes uncertainty(partial truth),uncertainty(partial truth), vague, unclear and imprecisionvague, unclear and imprecision  The key idea of fuzziness comes from theThe key idea of fuzziness comes from the multi-valuedmulti-valued logiclogic  Imprecision raises in several faces,Imprecision raises in several faces, e.g. as a Languagee.g. as a Language ambiguityambiguity
  6. 6. 21.04.2018 muruganm1@gmail.com 6 What is Fuzzy Logic?  Fuzzy logic (FL) is a way to make machines more intelligentFuzzy logic (FL) is a way to make machines more intelligent enabling them to reason in a fuzzy manner like humans, Itenabling them to reason in a fuzzy manner like humans, It resembles human reasoning.resembles human reasoning.  The approach of FL imitates the way of decision making inThe approach of FL imitates the way of decision making in humans that involves allhumans that involves all intermediate possibilitiesintermediate possibilities betweenbetween digital values YES and NO.digital values YES and NO.  In conventional, computer can takes precise input and producesIn conventional, computer can takes precise input and produces a definite output as TRUE or FALSE, which is equivalent toa definite output as TRUE or FALSE, which is equivalent to human’s YES or NO.human’s YES or NO.  Fuzzy logic is a form of multi-valued logic; it deals withFuzzy logic is a form of multi-valued logic; it deals with approximate rather than fixed and exact.approximate rather than fixed and exact.  In contrast with traditional logic theory, where binary sets haveIn contrast with traditional logic theory, where binary sets have two-valued logic: true or false, fuzzy logic variables may have atwo-valued logic: true or false, fuzzy logic variables may have a truth value that ranges between 0 and 1truth value that ranges between 0 and 1
  7. 7. • Instead of using complex mathematical equations fuzzy logic uses linguistic description to define the relationship between the input information and the output action. • Just as fuzzy logic can be described simply as “Computing with words rather than numbers”, fuzzy control can be described simply as “Control with sentences rather than equations”. 21.04.2018 muruganm1@gmail.com 7 What is Fuzzy Logic?
  8. 8. 17.05.2014 muruganm1@gmail.com 8 Lotfi A. Zadeh  Fuzzy logic, proposed by Lotfy Aliasker Zadeh in 1965,Fuzzy logic, proposed by Lotfy Aliasker Zadeh in 1965, emerged as tool to deal with uncertain, imprecise oremerged as tool to deal with uncertain, imprecise or qualitative decision making problems.qualitative decision making problems.  Lotfi A. Zadeh was a mathematician, computerLotfi A. Zadeh was a mathematician, computer scientist, electrical engineer, artificial intelligencescientist, electrical engineer, artificial intelligence researcher and professor emeritus of computerresearcher and professor emeritus of computer science at the University of California, Berkeley.science at the University of California, Berkeley.  Zadeh was best known for proposing fuzzyZadeh was best known for proposing fuzzy mathematics consisting of these fuzzy-related concepts:mathematics consisting of these fuzzy-related concepts: fuzzy sets, fuzzy logic, fuzzy algorithms, fuzzyfuzzy sets, fuzzy logic, fuzzy algorithms, fuzzy semantics, fuzzy languages, fuzzy control, fuzzysemantics, fuzzy languages, fuzzy control, fuzzy systems, fuzzy probabilities, fuzzy events, and fuzzysystems, fuzzy probabilities, fuzzy events, and fuzzy information.information. Lotfi Aliasker Zadeh Born: February 4, 1921 Died : September 6, 2017 (Aged 96)
  9. 9. 17.05.2014 muruganm1@gmail.com 9 Lotfi A. Zadeh Google Scholar
  10. 10. 21.04.2018 muruganm1@gmail.com 10  By fuzzifying crisp datafuzzifying crisp data obtained from measurements, Fuzzy Logic enhances the robustness of a systemrobustness of a system  Imprecision raises in several faces - for example, as a semantic ambiguity the statement “the soup is HOTthe soup is HOT” is ambiguous, but not fuzzy e.g. [20º,80º] The temperature of the soup Hot The amount of spices used Definition of the domain of discourse Transaction to FuzzinessTransaction to Fuzziness Fuzzy Example
  11. 11. empty half-full full? almost full? or half-empty? nearly full? …… Does it remain empty? 21.04.2018 11muruganm1@gmail.com Fuzzy Example  Fuzzy theory handles nonrandom uncertaintynonrandom uncertainty
  12. 12. 21.04.2018 muruganm1@gmail.com 12 Block Diagram of FLC
  13. 13. 21.04.2018 muruganm1@gmail.com 13 STEPS IN FUZZY SYSTEM  Identify the inputs and their ranges and name them  Identify the outputs and their ranges and name them  Create the degree of fuzzy membership function for each input and output  Construct the rule base that the system will operate under  Decide how the action will be executed by assigning strengths to the rules  Combine the rules and defuzzify the output
  14. 14. 21.04.2018 muruganm1@gmail.com 14 Fuzzy Logic Membership Function  Input to the fuzzyInput to the fuzzy logic is given aslogic is given as membershipmembership function, which isfunction, which is called linguisticcalled linguistic variablevariable
  15. 15. 21.04.2018 15muruganm1@gmail.com Linguistic variables(Triangular Membership Function)Linguistic variables(Triangular Membership Function) and their rangesand their ranges Linguistic Value Notation Numerical Ranges (Normalised) Negative Big Negative Medium Negative Small Zero Positive Small Positive Medium Positive Big NB NM NS Z PS PM PB [-2.667 -2 -1.333] [-2 -1.333 -0.6665] [-1.333 -0.6665 0] [-0.6665 0 0.6665] [0 0.6665 1.333] [0.6665 1.333 2] [1.333 2 2.667]
  16. 16. Variable Width Membership Function 21.04.2018 muruganm1@gmail.com 16
  17. 17. Rules :- Fuzzy logic usually uses IF-THEN rules, or constructs that are equivalent. -IF variable is property THEN action Example:- A simple temperature regulator that uses a fan might look like this: IF temperature is very cold THEN stop fan IF temperature is cold THEN turn down fan IF temperature is normal THEN maintain level IF temperature is hot THEN speed up fan 21.04.2018 17muruganm1@gmail.com
  18. 18. Mamdani versus Sugeno Models • Most of our examples were for Mamdani Model. • Another famous model comes from Sugeno. • Mamdani-style inference, as we have just seen, requires us to find the centroid of a two-dimensional shape by integrating across a continuously varying function. In general, this process is not computationally efficient. • Michio Sugeno suggested to use a single spike, a singleton, as the membership function of the rule consequent. A singleton, or more precisely a fuzzy singleton, is a fuzzy set with a membership function that is unity at a single particular point on the universe of discourse and zero everywhere else. 21.04.2018 18muruganm1@gmail.com
  19. 19. Surface Viewer 21.04.2018 muruganm1@gmail.com 19
  20. 20. Rule Editor 21.04.2018 muruganm1@gmail.com 20
  21. 21. Rule Viewer 21.04.2018 muruganm1@gmail.com 21
  22. 22. Building System with the MATLAB Fuzzy Logic Toolbox 21.04.2018 muruganm1@gmail.com 22
  23. 23. APPLICATION OF FUZZY LOGIC 21.04.2018 muruganm1@gmail.com 23
  24. 24. FUZZY LOGIC IN CONTROL SYSTEMS  Fuzzy Logic provides a more efficient and resourceful way to solve Control Systems.  Some Examples  Temperature Controller  Motor Speed control system 21.04.2018 24muruganm1@gmail.com
  25. 25. Fuzzy Logic Applications 21.04.2018 25muruganm1@gmail.com The problem is to Change the speed of a heater fan, based on theThe problem is to Change the speed of a heater fan, based on the room temperature and humidity.room temperature and humidity. A temperature control system has four settingsA temperature control system has four settings  Cold, Cool, Warm, and HotCold, Cool, Warm, and Hot Humidity can be defined byHumidity can be defined by  Low, Medium, and HighLow, Medium, and High Using this we can define the fuzzy set.Using this we can define the fuzzy set. TEMPERATURE CONTROLLER
  26. 26. Fuzzy Logic Applications 21.04.2018 26muruganm1@gmail.com Fuzzy Logic in a Washing Machine Fuzzy logic washing machines are gaining popularity. These machines offer the advantages of performance, simplicity, productivity, and less cost. Sensors continually monitor varying conditions inside the machine and accordingly adjust operations for the best wash results. As there is no standard for fuzzy logic, different machines perform in different manners.
  27. 27. Fuzzy Logic in a Washing Machine 21.04.2018 27muruganm1@gmail.com Fuzzy logic controls the washing process, water intake, water temperature, wash time, rinse performance, and spin speed. This optimizes the life span of the washing machine. Machines even learn from past experience, memorizing programs and adjusting them to minimize running costs. Most fuzzy logic machines feature ‘one touch control’. Equipped with energy saving features . The fuzzy logic checks for the extent of dirt and grease, the amount of soap and water to add, direction of spin, and so on. The machine rebalances washing load to ensure correct spinning. Else, it reduces spinning speed if an imbalance is detected. Even distribution of washing load reduces spinning noise. Neurofuzzy logic incorporates optical sensors to sense the dirt in water and a fabric sensor to detect the type of fabric and accordingly adjust wash cycle.
  28. 28. Air Conditioner 21.04.2018 28muruganm1@gmail.com
  29. 29. Fuzzy Logic Applications  Aerospace o Altitude control of spacecraft, satellite altitude control, flow and mixture regulation in aircraft vehicles.  Automotive o Trainable fuzzy systems for idle speed control, shift scheduling method for automatic transmission, intelligent highway systems, traffic control, improving efficiency of automatic transmissions  Defense o Underwater target recognition, automatic target recognition of thermal infrared images, naval decision support aids, control of a hypervelocity interceptor, fuzzy set modeling of NATO decision making.  Electronics o Control of automatic exposure in video cameras, humidity in a clean room, air conditioning systems, washing machine timing, microwave ovens, vacuum cleaners. 21.04.2018 29muruganm1@gmail.com
  30. 30. Fuzzy Logic Applications 21.04.2018 30muruganm1@gmail.com  Business  Decision-making support systems, personnel evaluation in a large company  Financial  Banknote transfer control, fund management, stock market predictions.  Industrial  Cement kiln controls heat exchanger control, activated sludge wastewater treatment process control, water purification plant control, quantitative pattern analysis for industrial quality assurance, control of constraint satisfaction problems in structural design, control of water purification plants  Chemical Industry  Control of pH, drying, chemical distillation processes, polymer extrusion production, a coke oven gas cooling plant
  31. 31. Fuzzy Logic Applications 21.04.2018 31muruganm1@gmail.com  Manufacturing  Optimization of cheese production.  Marine  Autopilot for ships, optimal route selection, control of autonomous underwater vehicles, ship steering.  Medical  Medical diagnostic support system, control of arterial pressure during anesthesia, multivariable control of anesthesia, modeling of neuropathological findings in Alzheimer's patients, radiology diagnoses, fuzzy inference diagnosis of diabetes and prostate cancer.
  32. 32. Fuzzy Logic Applications 21.04.2018 32muruganm1@gmail.com Mining and Metal Processing  Sinter plant control, decision making in metal forming. Robotics  Fuzzy control for flexible-link manipulators, robot arm control. Securities  Decision systems for securities trading. Signal Processing and Telecommunications  Adaptive filter for nonlinear channel equalization control of broadband noise Transportation  Automatic underground train operation, train schedule control, railway acceleration, braking, and stopping
  33. 33. When to use Fuzzy Logic? • If the system to be modelled in a linear system which can be represented by a mathematical equation or by a series of rules then straight forward techniquesstraight forward techniques should be used. • Alternatively, if the system is complexcomplex,, fuzzy logic may be the technique to follow.21.04.2018 33muruganm1@gmail.com
  34. 34. When to use Fuzzy Logic? • We define a complex systemcomplex system : – when it is nonlinear, time-variant, ill- defined; – when variables are continuous; – when a mathematical model is either too difficult to encode or does not exist or is too complicated and expensive to be evaluated; – when noisy inputs; – and when an expert is available who can specify the rules underlying the system behaviour.21.04.2018 34muruganm1@gmail.com
  35. 35. Chemistry Physics
  36. 36. Geology Mathematics We develop a general model for representing several processes in Mathematics Education (e.g. learning, mathematical modelling, problem-solving, etc) involving fuzziness and uncertainty 21.04.2018 36muruganm1@gmail.com
  37. 37. Management 21.04.2018 37muruganm1@gmail.com
  38. 38. Computer 21.04.2018 muruganm1@gmail.com 38
  39. 39. CIVIL 21.04.2018 39muruganm1@gmail.com
  40. 40. Mechanical 21.04.2018 40muruganm1@gmail.com
  41. 41. Nursing 21.04.2018 41muruganm1@gmail.com
  42. 42. 21.04.2018 muruganm1@gmail.com 42 Riddles time…Riddles time…
  43. 43. WELCOME BACKWELCOME BACK 21.04.2018 muruganm1@gmail.com 43
  44. 44. Application of Fuzzy Logic inApplication of Fuzzy Logic in Electrical DrivesElectrical Drives 21.04.2018 muruganm1@gmail.com 44
  45. 45. Power Electronic Drives • A machine is driven by a power electronic converter is called power electronic drives. Electrical drives are • AC drives • DC drives muruganm1@gmail.com21.04.2018 45
  46. 46. ELECTRICAL DRIVES BLOCK DIAGRAM OF DRIVE SYSTEM SOURCE AC/DC POWER MODULATOR MOTOR CONTROL UNIT SENSING UNIT INPUT COMMAND
  47. 47. b)CHOPPER (dc-to-dc)a)RECTIFIER (ac-to-dc) POWER MODULATOR FOR DC DRIVES
  48. 48. CONVERTERS EQUATION RECTIFIER CHOPPER For Single phase semi converter For Step down chopper INVERTER V0= m X Vs m= Ar / Ac 2 1 0 2 21             +−= α απ π Sin VV sRMS AC VOLTAGE CONTROLLER
  49. 49. SIMULATION What is simulation? simulation is the discipline of designing a model of an actual or theoretical physical system, executing the model on a digital computer, and analyzing the execution output. Simulation embodies the principle of ``learning by doing'‘ to learn about the system we must first build a model of some sort and then operate the model. In other words the process of limitating a real phenomenon with a set of mathematical formulas.
  50. 50. SIMULATION Why simulation? Simulation is often essential in the following cases: 1)The model is very complex with many variables and interacting components 2)The underlying variables relationships are nonlinear 3)There is no wastage of money due to damage of circuit components. 4)No limitation in the parameters range during simulation.
  51. 51. The different simulation software • MATLAB • OrCAD PSPICE • MiPower • Pscad • Mathcad • ETAP • LABVIEW 21.04.2018 muruganm1@gmail.com 51
  52. 52. MATLAB SIMULATION VIEW muruganm1@gmail.com21.04.2018 52
  53. 53. muruganm1@gmail.com MATH FUNCTION 21.04.2018 53
  54. 54. DC MOTOR where J- Moment of Inertia B- Friction coefficient Kt- Torque constant Kb- Back emf constant TL- Load torque applied io- Armature current Vo- Armature voltage applied R- Armature resistance and L- Armature inductance Eb= Back EMF Eb VOLTAGE EQUATION EQUIVALENT CIRCUIT
  55. 55. DC MOTOR EQUATIONS VOLTAGE EQUATION TORQUE EQUATION
  56. 56. TRANSFER FUNCTION FOR DC MOTOR Motor Parameters J=0.011;D=0.004 R=0.6 ;L=0.008 K=0.55
  57. 57. DESIGN OF DC MOTOR USING MATLAB
  58. 58. DESIGN OF Fuzzy Logic CONTROLLER IN MATLAB GUI 21.04.2018 muruganm1@gmail.com 58
  59. 59. FUZZY LOGIC CONTROLLER FOR DC DRIVE 21.04.2018 muruganm1@gmail.com 59
  60. 60. MATLAB CIRCUIT FOR FUZZY CONTROLLED DC DRIVE 21.04.2018 muruganm1@gmail.com 60
  61. 61. 21.04.2018 muruganm1@gmail.com 61 FUZZY SPEED CONTROLLER
  62. 62. 21.04.2018 muruganm1@gmail.com 62 INPUT MEMBERSHIP FUNCTION PLOTS FOR ERROR
  63. 63. 21.04.2018 muruganm1@gmail.com 63 INPUT MEMBERSHIP FUNCTION PLOTS FOR CHANGE IN ERROR
  64. 64. 21.04.2018 muruganm1@gmail.com 64 OUTPUT MEMBERSHIP FUNCTION PLOTS
  65. 65. 21.04.2018 muruganm1@gmail.com 65 FUZZY RULE TABLE
  66. 66. 21.04.2018 muruganm1@gmail.com 66 Advantages of fuzzy control  It can work with less precise inputs  Fuzzy logic is conceptually easy to understand.  It doesn’t need fast processors  It needs less data storage in form of membership functions and rules than conventional lookup table for non-linear control  The mathematical concepts behind fuzzy reasoning are very simple.
  67. 67. 21.04.2018 muruganm1@gmail.com 67 Advantages of fuzzy control  Fuzzy logic is flexible.  We can create a fuzzy system to match any set of input-output data.  Fuzzy systems don't necessarily replace conventional control methods. In  Fuzzy logic is based on natural language.  It is more robust than other non-linear controllers
  68. 68. muruganm1@gmail.com Dr.M.MURUGANANDAM Muruganm1@gmail.com 0949529493 Learn by Doing Excel Thru Experimentation Lead by Example Acquire skills and get employed Update skills and stay employed THANK YOU 21.04.2018 68

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I Planned to give a specific training on Fuzzy Logic Controller using MATLAB simulation. This type of intelligent controller is very useful for the research work in all discipline.

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