NOISE ANALYSIS

Mahmoud Taha; Demonstrator

Sensorless Speed control of BLDC motor Using Geno-Fuzzy Controller

Benha UniversityBenha Faculty of EngineeringElectrical Engineering Technology Department

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Outlines:

BLDC motor OverviewSensorless Control of BLDC motorSpeed control of BLDC motor usingPI controllerFuzzy logic controllerGeno - Fuzzy controllerFuzzy logic controllerGenetic algorithmProposed workReferences

Many of the limitations of the classic permanent magnet "brushed" DC motor are caused by the brushes pressing against the rotating commutator creating frictionAs the motor speed is increased, brushes may not remain in contact with the rotating commutatorSparks and electric noise may be created as the brushes encounter flaws in the commutator surfaceBrushes eventually wear out and require replacement, and the commutator itself is subject to wear and maintenanceBrushless DC motors avoid these problems with a modified design, but require a more complex control systemWhy a Brushless DC Motor ?1

Brushless DC MotorA brushless dc motor can be described as an inverted brush dc motor with its magnet being the rotor and its stationary windings forming the stator. Its a type of synchronous motormagnetic fields generated by the stator and rotor rotate at the same frequencyHas no brushes and commutators.It requires external commutation circuit to rotate the rotor.Rotation of the rotor depends on the accurate position with stator.

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How Does it Works?

Three-phase brushless dc motors are operated by energizing two of its three phase windings at a time depending on the rotor position.Halls Sensors sense the position of the coilslogic Circuit turns appropriate switches on and offThe voltage through the specific coils turns the motorOther wise , sensorless Technique are used.

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AdvantagesHigh Reliability & EfficiencyLonger Life & Low maintenance Elimination of Sparks from CommutatorReduced Friction silent operationHigh dynamic ResponseGood speed control

DisadvantagesMay be more expensive than "brushed" DC motorsMore complex and expensive drive circuit than "brushed" DC motorsRequires additional Sensors

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Application of BLDC motor5We find an application in every segment of market as appliances, industrial control, Automation and so on .Categories of BLDC motor control:Applications with Constant LoadsVariable speed important than accuracy , low cost controller, open loop controller as fans, pumps and blowersApplications with Varying LoadsRequire high speed control accuracy ,good dynamic response complex & high cost controller. closed loop controller as Home appliance, EV and Robotic Arm controlPositioning ApplicationsRequire good dynamic response of speed & Torque complex & high cost controller. closed loop controller as CNC machine

Control of Brushless DC MotorsIn order to keep in synchronism we need to energize the coils at the correct point in time.Also for maximum torque per amp we need to align the stator magnetic field at 90 to the rotor magnetic field i.e. vector controlHence we need to identify the position of the rotor fluxIdentifying the position of the rotor flux is not very difficultIn a BLDC machine this is usually achieved using Hall effect transducers.Alternatively provided that the processor has enough bandwidth this can be done in a sensor less manner.

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T4

T6

T1VLinkT3T5

PWM commutation control6

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Sensorless Control of BLDC Motor (Ebemf)

zero crossingTime Bemf

Measurement

PWM commutation control

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T6

T1VLinkT3T5VoltageEbemfZero crossingdetector7

Sensorless Speed control of BLDC

Controller

Back EMF Detection

PWM commutation control

Position & speed

Reference current generator

Three PhaseVSI

S1-S6Vdc

ia , ib , ic

Current Control LoopSpeed Control Loopui*a , i*b , i*c

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control system response

Speed control done by improving speed response by minimization of settling time, steady state error and maximum overshoot.9

Using PI controller

To achieve desired level of performance motor require suitable speed controller Advantages of PI controllersimple structureeasy implementation Dis advantages:It require precise linear mathematical modelIts difficult where are some control:Complexity non linearLoad disturbancesparametric variation

overshoot of 6.667%settling time 0.05 sec10

Using Fuzzy logic controller

settling time of 0.03 seczero steady state errorfuzzy controller is designed based on trial-error method and simulation.FLC is more effective in BLDC motor drives.In this controller the input is speed and the output is voltage.The membership function is figured between error and change in error. After that using pre defined rule the controller produces signal this signal is called control variable.11

Using Genetic algorithm The optimal design of FLC requires the determination of a great deal of features and parameters.Parameters :normalization parametersMembership functions Slide mode rule base parameters12

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In control applications, the fitness is usually related to performance

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Applying this method to BLDC motorApplying this method to DC motor

Fuzzy logic controller

IntroductionThe fuzzy logic, unlike conventional logic system, is able to model inaccurate or imprecise models. The fuzzy logic approach offers a simpler, quicker and more reliable solution that is clear advantages over conventional techniques. Fuzzy logic is a way to make machines more intelligent enabling them to reason in a fuzzy manner like humans.Fuzzy logic, proposed by Lotfy Zadeh in 1965Its totally different from other controllers, fuzzy logic's principle is to think like an organic creature; human.

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How does it works?

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FUZZY LOGIC REPRESENTATION

SlowestFastestSlowFast[ 0.0 0.20 ][ 0.10 0.50 ][ 0.40 0.90 ][ 0.75 1.00 ]SlowFast

Speed = 0Speed = 1

Classical SetFuzzy Set17

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A fuzzy set defined by the function that maps objects in a domain of concern to their membership value in the set. Such a function is called membership functionThe most common types are singleton, triangular, trapezoidal, and bell-shaped

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Structure of Fuzzy logic controller

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Design procedureStep 1: Determination of state variables and controlvariablesStep 2: Selection of the inference method common-Mamdani-SugenoStep 3: Selection of the fuzzification method20

Design procedureStep 4: Determination of the knowledge base- Partition of variable space.- Selection of the MF shapes- Design of the rule baseStep 5: Selection of defuzzification strategy Centroid or Mean-of-maximum or Weighted averageStep 6: Test and tuningStep 7: Construction of a lookup table21

Advantages over conventional control techniquesDeveloping a fuzzy logic controller is cheaper than developing model based or other controller with comparable performance.Fuzzy logic controller are more robust than PID controllers because they can cover a much wider range of operating conditions.

Disadvantages of fuzzy systemIt requires a lot of RulesThe sophisticated design process is usually implemented by an expert using the conventional trial-and-error methods which consume more time.22

genetic algorithmA genetic algorithm is a search technique used in computing to find true or approximate solutions to optimization and search problems, Based on ideas from Darwinian Evolution theory Survival of the fittestPart of Evolutionary AlgorithmsBasic concept - to simulate processes in natural system necessary for evolution.Provide efficient, effective techniques for optimization started with Nils Barricelli in 1954, Developed by John Holland, University of Michigan - 1970Got popular in the late 1980s

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It is started with a set of randomly generated solutions and recombine pairs of them at random to produce offspring.Only the best offspring and parents are kept to produce the next generation then repeated to get optimal solution.Reproduction mechanisms have no knowledge of the problem to be solvedLink between genetic algorithm and problem:EncodingFitness function

Possible individuals encodingBit strings (0101 ... 1100)Real numbers (43.2 -33.1 ... 0.0 89.2) Lists of rules (R1 R2 R3 ... R22 R23)... any data structure ...

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Working Principle of Genetic Algorithm

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Genetic Algorithms: Case StudyA simple example will help us to understand how a GA works. Let us find the maximum value of the function (15x x2) where parameter x varies between 0 and 15. For simplicity, we may assume that x takes only integer values. Thus, chromosomes can be built with only four genes:

Suppose that the size of the chromosome population N is 6, the crossover probability pc equals 0.7, and the mutation probability pm equals 0.001. The fitness function in our example is defined by f(x) = 15 x x227

The fitness function and chromosome locations

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Advantages :A GA has a number of advantages. Concept is easy to understandBad proposals do not effect the end solution negatively as they are simply discarded. The inductive nature of the GA means that it doesn't have to know any rules of the problem - it works by its own internal rules. This is very useful for loosely defined problems.Flexible building blocks for hybrid applicationsThey can be used to solve difficult problems quickly and reliably.

Dis advantages :A practical disadvantage of the genetic algorithm involves longer running times on the computer. Fortunately, this disadvantage continues to be minimized by the ever-increasing processing speeds of today's computers.30

Proposed workA Fuzzy Logic Controller (FLC) for speed control of BLDCM has been proposed Using genetic algorithm (GA) to Auto tune FLC parameters and obtain optimal design in a short time without the need of an expert assistanceThe following stages will be followed to acheive our goal:-Survey the existing literature; this will include modeling and control of BLDC motor, control techniques used to control BLDC motors, fuzzy systems for controlling BLDC motors and the application of Genetic Algorithm in optimization .Theory and development; this will include the study of MATLAB as a powerful simulation toolbox. The setup will be prepared for simulating Fuzzy logic controller based on Genetic Algorithm

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ReferencesI.Rojas, J.J.Merelo, H.Pomares, A.Prieto , Genetic Algorithms for Optimum Designing of Fuzzy Controllers.Goldberg, D. (1989) , Genetic algorithms in search, optimization and machine learning. Addison-Wesley. S. Rajasekaran , Neural Networks, Fuzzy Logic, and Genetic Algorithms.Jan Jantzen , Tutorial On Fuzzy Logic.Kevin M. Passino and Stephen Yurkovich , Fuzzy Control Ahmad M. Ibrahim,FUZZY LOGIC for Embedded Systems Applications.S.Rambabu, MODELING AND CONTROL OF A BRUSHLESS DC MOTOR, Department of Electrical Engineering National Institute of Technology Rourkela 2007. Rahul Malhotra , Yaduvir Singh and Narinder Singh , Design of Embedded Hybrid Fuzzy-GA Control Strategy for Speed Control of DC Motor, International Journal of Computer Applications (0975 8887) Volume 6 No.5, September 2010.Mehmet Cunkas, Omer Aydodu , Realization of fuzzy logic controlled brushless dc motor drives using matlab/simulink. Mathematical and Computational Applications, Vol. 15, No. 2, pp. 218-229, 2010.

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