Speed Control Of DC Motor Using PID Smart Controller
International Journal of Scientific & Engineering Research, Volume 5, Issue 11, November-2014ISSN 2229-55181044Speed Control of DC Motor Using PID & SmartControllerPurushotam Kumar, Prabhakar Kumar Prabhat, Mithun Kumar, Dr. S.D. ChoudharyAbstract- The thesis describes about the concept of DC motor and Speed control separately excited DC motor. Motor speed iscontrolled with PID controller and first system is checked without controller on loaded and unloaded condition then add PID controllerand system is tuned using its existing tuning methods. After it system is further tuned in order to get desired value with less steady stateerror. And then the result is discussed. This paper describes about the basic concepts of Fuzzy Set and Fuzzy Logic, the speed controlwith the help of Fuzzy controller. Fuzzy controller provides better control strategies than other controllers. Optimization of Fuzzycontroller with Simulink model describes in this paper and a new way for faster response and smooth output. The comparison of thesetwo controllers’ results is also showed. From the results it is proved that Fuzzy Controller is the best controller. Finally the MATLABSimulation is discussed.Index Terms: DC Motor, PID Controller, Existing tuning, Steady State Error, Fuzzy logic, Fuzzy controller, Simulink, MATLAB.1. CONCEPTS OF DC MOTORFor A motor convert electrical energy into mechanical energy.There are two types of motor: AC motor & DC motor. Asimple DC motor use electricity and magnetic field forproducing torque which rotate the motor. PMDC permanentmagnet DC motor outperforms to AC motor because itprovide better speed control on high torque loads and use inwide industrial application. The applied voltage describes thespeed of motor while current in the armature windings showsthe torque. If applied load increased in the shaft of motor thenin order to sustain its speed motor draw more current fromsupply and if supply is not able to provide enough currentthen motor speed will be affected. DC motor provides moreeffective results if chopping circuit is used. Low power DCmotors usually use in lifting and transportation purposes aslow power AC motors don’t have good torque capability. DCmotor used in railway engines, electric cars, elevators, roboticapplications, car windows and wide verity of small appliancesand complex industrial mixing process where torque cannotbe compromised.IJSER2. SPEED CONTROL OF DC MOTORThe term speed control stand for intentional speedvariation carried out manually or automatically DC motorsare most suitable for wide range speed control and are therefor many adjustable speed drives. Purushotam Kumar, Research Scholar, Department of ElectricalEngineering, Asansol Engineering College, WB, India E-mail:purushotamkumar88@gmail.comPrabhakar Kumar Prabhat is currently associated with thedepartment of Electrical Engineering,DAVIET,Daltonganj,Jharkhand, India, E-mail: pkp.nitp@gmail.comMithun Kumar is currently associated with the department ofElectrical Engineering, DAVIET, Daltonganj, Jharkhand, India,E-mail: mithunkism@gmail.comDr. S.D. Choudhary is currently associated with the departmentofElectronics & Communication Engineering, DAVIET,Daltonganj, Jharkhand, India, E-mail: suryadeo.bit@gmail.comWhere,V A is the armature voltage. (In volt)E b is back emf the motor (In volt)I a is the armature current (In ampere)R ais the armature resistance (In ohm)L a is the armature inductance (In Henry)T m is the mechanical torque developed (In Nm)J m is moment of inertia (In kg/m²)B m is friction coefficient of the motor (In Nm/ (rad/sec))ω is angular velocity (In rad/sec)We know thatω (V a -I R)/K aϕ . (1)Where, ϕ Field flux per poleKa Armature constant PZ / 2πaWhere, P No. of poleSpecification of the dc motor:Armature resistance (R a) 0.5ΩIJSER 2014http://www.ijser.orgArmature inductance (L a) 0.02 HArmature voltage (V a) 200Mechanical inertia (j) 0.1 Kg .mFriction coefficient (B m) 0.008 N.m/rad/secBack emf constant (k) 1.25 V/rad/secRated speed 1500 r.p.mMotor torque constant N.m/A
International Journal of Scientific & Engineering Research, Volume 5, Issue 11, November-2014ISSN 2229-55181045Calculation:Speed at full load when ω 157.07 rad/secE b 1.25x157.07 196.3 VVA E b I R200 196.3 I RIA 7.325 Amps3. SPEED CONTROL WITH PIDCONTROLLERA proportional–integral–derivative controller (PID controller)is widely used in industrial control systems. It is a genericcontrol loop feedback mechanism and used as feedbackcontroller. PID working principle is that it calculates an errorvalue from the processed measured value and the desiredreference point. The work of controller is to minimize the errorby changing in the inputs of the system. If the system is notclearly known then applying PID controller provide the bestresults if it is tuned properly by keeping parameters of thesystem according to the nature of system.Fig: Three values of Kp , Ki and KdThe integral term contribute error and duration of errorproportionally. Error sum gives offset that correctedpreviously. The calculated error is multiplied by integral gainand then added to controller output. It finally reduced thesteady state error.IJSER4. TUNING OF PID CONTROLLER BYZIEGLER-NICHOLS METHODFig: Blok diagram of PID controllerThe PID measurement depends upon three parameters whichis called the proportional, the integral and derivative partwhich is called P, I and D part. P determine the reaction tocurrent error, I determine reaction to the sum of recentlyappeared errors, D Determine reaction according to the rateoff error changing. The sum of all three parts contribute thecontrol mechanism such as speed control of a motor in whichP value depends upon current error, I on the accumulation ofprevious error and D predict future error based on the currentrate of change. As derivative action is sensitive to noise somostly the controllers are PI controller rather than PID as it isnot possible a system without disturbances. Integral part helpsthe system to reach onto its target value while P part increaseovershoots. The P term take the output to proportional of errorvalue. Its response can be adjusted by multiplying the error bya constant Kp which is called proportional gain. Ifproportional gain is large then it creates a high overshootwhich unstable the system, while a small output changemakes a small control action.Fig: Simulink model of DC motor with PID controllerAs in this project the target is to control the speed so speed issend back for checking the system in closed loop and tunedPID controller. The method used for tuning is Ziegler–Nicholsmethod.According to Ziegler–Nichols method: Run the controller by taking only P value. Increase P value of the system until it self-oscillatingwith constant amplitude.Then take controller gain time period IJSER 2014http://www.ijser.org
International Journal of Scientific & Engineering Research, Volume 5, Issue 11, November-2014ISSN 2229-55181046Different values of K P , K I & o K d of PID controller for positionfrom graph analysis:-IJSERCONCLUSSION:Applying PID controller both system become marginallystable. But settling time is 10 sec for angle of pendulum whichshould be decreased. Too much damping is occurs for bothangle and position. So we can’t say that PID is good forinverted pendulum.Different values of k P ,k I & o K d of PID controller for angle from graphanalysis:-5. SPEED CONTROL WITH FUZZYCONTROLLERControllers based on the fuzzy logic give the linguisticstrategies control conversion from expert knowledge inautomatic control strategies. The first fuzzy logic basedcontrollers application was done by Assilian and Mandani [2].The recent fuzzy logic controller application [3] in waterquality control, train operation automatic system, elevatorscontrol, nuclear reactor control and fuzzy computers shows anefficient way for using the fuzzy control in complex processwhich can be controlled by a skilful human being withoutIJSER 2014http://www.ijser.org
International Journal of Scientific & Engineering Research, Volume 5, Issue 11, November-2014ISSN 2229-55181047knowing its dynamic. The fuzzy logic controller consists of aothers control system. While the others control system uselinguistic propositions and rules set, which defines individualdifficult mathematical calculation to provide a model ofcontrol actions. A fuzzy logic controller designed on the basisthe controlled plant, it only uses simple mathematicalof the fuzzy logic is an approximate reasoning-basedcalculation to simulate the expert knowledge. Fuzzy set ofcontroller, which does not require exactly analytical modelstheory represent the human reasoning with knowledge that isand is much closer in spirit to human thinking and naturalalmost impossible to represent in quantitative measures or forlanguage than the traditional logic system. Essentially, thethat control plants that are hard to control or ill defined. Fuzzycontrol strategies in the FLC are based on expert experience,inference system models the system using if-then rules. Fuzzyso the fuzzy logic controller can be regarded as the simulationset theory proposed the membership function at range ofof a humanoid control model. When the designing a FLC, thenumbers [0, 1] or False or true membership function. Thiscontrol strategies have to be regarded as the simulation of atheory provides the mathematical strength to check thehumanoid control rules pre-constructed by control result failsuncertainties connected with human thinking or reasoning.to meet the system requirement due to a change in the outsideFuzzy logic is suitable for a model that is hard to control orenvironment. The possible solution to this problem is that wenon linear models. This system also provides control overcan adjust either the membership function of the fuzzy sets orthe control rules to achieve the control objective.MIMO systems and also allows decision making withincomplete information. Human reasoning can also be knownas multi valued imprecise. [21] The requirement for theapplication of a FLC arises mainly in situations where:6.The description of the technological process isIJSERavailable only in word form, not in analytical form.7.It is not possible to identify the parameters of theprocess with precision.8.The description of the process is too complex and it ismore reasonable to express its description in plainlanguage words.9.The controlled technological process has a "fuzzy"character.10. It is not possible to precisely define these conditions.A fuzzy logic controller has four main components as shownin Figure:Fuzzy logic is a type of multi valued logic. It deals withapproximate reasoning rather than precise. Fuzzy logicderived from fuzzy set theory. Fuzzy logic was first proposedby LotfiZadeh in 1965. Fuzzy logic has currently used incontrol theory, artificial intelligence systems specially tocontrol complex aircraft engines and control surfaces,1.Fuzziffication2.Inference engine3.Rule base4.DefuzzificationFuzzificationhelicopter control, messile guidance, automatic transmission,The first step in designing a fuzzy controller is to decidewheel slip control, auto focus cameras and washing machines,whichrailway engines for smooth drive and fuel consumption andperformance mustmany industrial processes. Fuzzy logic provide better results ifcontroller. Fuzzy logic uses linguistic variables instead ofwe compared it with PID controller. Fuzzy logic control is anumerical variables. The process of converting a numericalcontrol algorithm based on a linguistic control strategy,variable (real number or crisp variables) into a linguisticwhich is derived from expert knowledge into an automaticvariablecontrol strategy. The operation of a FLC isbased onachieved with the different types of fuzzifiers. There arequalitative knowledge about the system being controlled .Itgenerally three types of fuzzifiers, which are used for thefuzzification process; they are:doesn't need any difficult mathematical calculation like theIJSER entthe system dynamicbe taken as the inputnumber)issignaltothecalled fuzzification. This is
International Journal of Scientific & Engineering Research, Volume 5, Issue 11, November-2014ISSN 2229-55181048Singleton fuzzifierabscissa of the center of gravity of the fuzzy set is calculated as Gaussian fuzzifierfollows: Trapezoidal or triangular fuzzifier Rule BaseWhere x i is a point in the universe of the conclusionA decision making logic which is, simulating a human(i 1, 2, 3 .) and µ c (x i) is the membership value of thedecisionresulting conclusion set. For continuous sets summations areprocess,intersfuzzy control action from theknowledge of the control rules and linguistic variablereplaced by integralsdefinitions [9]. The rules are in “If Then" format and formallythe If side is called the conditions and then side is called theconclusion. The computer is able to execute the rules andcompute a control signal depending on the measured inputserror (e) and change in error (de). In a rule based controller thecontrol strategy is stored in a more or less natural language. Arule base controller is easy to understand and easy to maintainfor a non- specialist end user and an equivalent controllercould be implemented using conventional techniques [14].Fig: Centre of GravityInference engineIJSERInference engine is defined as the Software code whichprocessestherules,cases,objectsor other type ofknowledge and expertise based on the facts of a givensituation. When there is a problem to be solved that involveslogic rather than fencing skills, we take a series of inferencesteps that may include deduction, association, recognition,Bisector of area (BOA)The bisector of area (BOA) defuzzification method calculatesthe abscissa of the vertical line that divides the area of theresulting membership function into two equal areas. Fordiscrete sets, is the abscissa x j that minimizes.and decision making. An inference engine is an informationprocessing system (such as a computer program) thatsystematically employs inference steps similar to that of ahuman brain.DefuzzificationHere i max is the index of the largest abscissa x i max. BOA is acomputationally complex method.The reverse of Fuzzification is called Defuzzification. The useMean of maximum (MOM)of Fuzzy Logic Controller (FLC) producesinalinguisticvariablerequiredoutput(fuzzy number). Accordingtorealworld requirements, the linguistic variables have tobetransformedtocrispoutput.There are manydefuzzification methods but the most common methods are asfollows [11]:In this method the crisp value is to choose the point with thehighest membership. There may be several points in theoverall implied fuzzy set which have maximum membershipvalue. Therefore it is a common practice to calculate the meanvalue of these points. This method is called mean of maximum(MOM) and the crisp value is calculated as follows: Center of gravity (COG) Bisector of area (BOA) Mean of maximum (MOM)Here I is the (crisp) set of indices i where µ c(x I)reaches its maximum µmax cardinality (the number ofCenter of gravity (COG)members), and I is its Implementation of an FLC requiresFor discrete sets COG is called center of gravity forthe choice of four key factors:singletons (COGS) where the crisp control value is theIJSER 2014http://www.ijser.org
International Journal of Scientific & Engineering Research, Volume 5, Issue 11, November-2014ISSN 2229-5518 Number of fuzzy sets that constitute1.Centre of gravity (COG)1049linguistic variables.2.Centre of gravity method for singletons (COGS) Mapping of the measurements onto the3.Bisector of area (BOA)support sets.4.Mean of Maxima (MOM) Control5.Left most maximum (LM), and right most maximum(RM)protocolthatdeterminesthecontroller behavior. Shape of membership functions.Sugeno Fuzzy InferenceMamdani fuzzy inferenceMamdani style is not computationally efficient as it find theThe most common method is used currently is fuzzy inferencecentroid of two dimensional shapes by integration of carryingsystem. In 1975, Professor Ebrahim Mamdani of Londonfunction. Michio Sugeno proposed a new method to use singleUniversity introduced first time fuzzy systems to control aspike, a singleton, as a membership function inputs. Its meansteam engine and boiler combination. He applied a set offuzzy set is at unity point at one particular point on thefuzzy rules experienced human operators. The mamdaniuniverse of discourse and zero at remaining area. This systemsystem usually done in four steps. [21]is almost same of Mamdani method but with the exception of Fuzzification of the inputs Rule evaluation. Aggregation of the rules.Defuzzification.consequent change and instead of fuzzy set it use amathematical function as input variable. [21]RulesIJSERThe general rules for Dc motor speed control is that if motorFuzzification converts input data to degree of membershipspeed is less than desired speed then speed up the motor andfunctions. In this process data is matched with condition ofif motor speed is more than reference speed then slows itrules and determined how well data is matched with rule atspeed. There are nine possible conditions which motor can beparticular instance. Thus a degree of membership function isseen and nine possible regions are selected from which 25developed.possible rules in fuzzy controller are written. In the process ofproducing necessary output voltage with Fuzzy LogicController the speed error should be minimized. The biggerspeed error causes the bigger controller input. In additionchanging of the error plays an important role to definecontroller input.Then in Rule-base block rules are written according to systemrequirement. Fuzzy controller work on both MIMO and SISO.In case of Dc motor there are two input variables Error andChange in error are selected. This system is limited to singleloop control. Usually rules are in if, and, then form. Ininference engine aggregation is done in which degree offulfillment is calculated of the condition specifies by a rule. Inactivation min of two aggregated value is selected and onlythickened part of singleton are activated. Its multiplicationresult in slighter smooth control. Then all activatedconclusions are accumulated using max operation.Defuzzification block converted resulting fuzzy set into anumber that is sent to the system and this number is actuallythe control signal. There are seven defuzzification methods.Sugeno Fuzzy InferenceIJSER 2014http://www.ijser.org
International Journal of Scientific & Engineering Research, Volume 5, Issue 11, November-2014ISSN 2229-55181050In region one according to Fig. error is in positive reign whileerror direction is going to negative reign, so output ofcontroller must be positive. In this case speed still not touch tothe reference point so increase in speed is required which isdirectly proportional to the voltage of controller so increase involtage help the motor to got reference speed. In third reign, iferror value is negative large and change of error value isnegative large than output will be negative large. Thiscondition is corresponding to the reign 3 interval but thisresult gives us crisp value for fuzzy control this crisp valueshould be converted into linguistic form.The surface in the next Fig. shows that motor touch thereference speed in smooth way. According to this designmotor can reach to the maximum speed of 300 rad/sec. IJSERIf error is NL and change in error is NL thenoutput is NL If error is NL and change in error is NZ thenoutput is NL If error is NL and change in error is ZZ thenoutput is NZFig. Surface ViewFig. shows the simulink model of DC motor speed controlthrough fuzzy controller.In fuzzy controller error and change in error is measure byfollowing formulas:Error (ek) Wref – wmThe output of controller plotted against the rules describes.Rules behavior can be checked by changing of error or changein error point. From the surface an idea can be built that in aChange in error (ce) e(k) – e(k-1)Back emf provides the error while change in error measuredcertain case what will be the output of controller.that error goes in negative direction or positive direction. InFig. Simulated Outputs (Sugeno Fuzzy Inference)Fig. shows the output of DC motorsimulink model reference speed is selected 150 rad/sec.IJSER 2014http://www.ijser.org
International Journal of Scientific & Engineering Research, Volume 5, Issue 11, November-2014ISSN 2229-55181051In Fig. desired speed is 150 rad/sec. Motor achieve this speedComparison between fuzzy PID and conventional PIDin less than 0.2 second and torque load is applied at 0.5 secondcontroller:but it did not have any effect on the speed and it remain constant. This is the advantage of fuzzy controller that in poleSelf-tuned tuning PID controller is less compared toconventional PID controller.placement and PID controller speed reduced for minor time The three parameters "K P ", "K I ", "K D " of conventionaland then goes to reference point but in this case speed notPID control need to be constantly adjusted online inreduced.order to achieve better control performance. Design of Fuzzy RulesFuzzy self-tuning PID parameters controller canautomatically adjust PID parameters in accordancewith the speed error and the rate of speed error-Rule bases for tuning K Pchange, so it has better self-adaptive capacity fuzzyPID parameter controller has smaller overshoot andless rising and settling time than conventional PIDcontrollerandhasbetterdynamicresponseproperties and steady-state properties. Steady state error in case of self tuned fuzzy PID isless compared to conventional PID controller.Rule bases for tuning K IIJSERDifferent values of K p ,K i & K d for fuzzy logic controller:Sl. No.KdKpKiRule bases for tuning K D10.7515123.552030.02-3154102.50.85554-10.2511. RESPONSE OF FUZZY LOGICCONTROLLERIJSER 2014http://www.ijser.org
International Journal of Scientific & Engineering Research, Volume 5, Issue 11, November-2014ISSN 2229-551812. SIMULATION RESULTSIJSERIJSER 2014http://www.ijser.org1052
International Journal of Scientific & Engineering Research, Volume 5, Issue 11, November-2014ISSN 2229-551813. DISCUSSION AND CONCLUSION OF THERESULTIn this project we have studied about different method forspeed control of DC motor. The steady state operation and itsvarious torque-speeds, torque-current characteristics of DCmotor are studied. We have also studied basic definition andterminology of fuzzy logic and fuzzy set. This projectintroduces a design method of two inputs and three outputsself-tuning fuzzy PID controller and make use of MATLABfuzzy toolbox to design fuzzy controller. fuzzy controlleradjusted the proportional, integral and derivate (K P , K I , K D )gains of the PID controller according to speed error andchange in speed error .From the simulation results it isconcluded that ,compared with the conventional PIDcontroller, self-tuning PID controller has a better performancein both transient and steady state response. The FLC has betterdynamic response curve, shorter response time, smallovershoot, small steady state error (SSE), high steady precisioncompared to the conventional PID controller.14. FUTURE SCOPEdone which can be implemented in hardware to observeactual feasibility of the approach applied in this thesis. Thistechnique can be extended to other types of motors. Theparameters of PID controller can also be tuned by usingREFERENCES[7] Weiming Tang, Guanrong Chen and Rongde Lu, “A ModifiedFuzzy PI Controller for a Flexible-joint Robot Arm withUncertainties”, Fuzzy Set and System, 118 (2001) 109-119.[8] PavolFedor, Daniela Perduková, “A Simple Fuzzy ControllerStructure,”ActaElectrotechnica ET Informatica No. 4, Vol. 5, 2005[9]Maher M.F. AlgreerandYhyaR.M.Kuraz, “Design Fuzzy SelfTuning of PID Controller for Chopper-Fed DC Motordrive.”Kuraz[10] BomedieneAlloua ,AbdellahLaoufBrahimGasbaoui andABdessalamAbderrahamani, “Neuro-Fuzzy DC Motor speedControl Using Particle Swarm Optimization,” Leonaro ElectronicJournal of Practices and Technologies ISSN,1583-1078.[11]ManafeddinNamazov and OnurBasturk, “DC motor positioncontrol using fuzzy proportional-derivative controllers withdifferent defuzzification methods,” Turkish Journal of FuzzySystems (eISSN: 1309–1190), Vol.1, No.1, pp. 36-54, 2010.[12] Wang Xiao-kan, Sun Zhong-liang, Wanglei, Feng Dong-qing,"Design and Research Based on Fuzzy PID-Parameters SelfTuning Controller with MATLAB," Advanced Computer Theoryand Engineering, International Conference on, pp. 996-999, 2008International Conference on Advanced Computer Theory andEngineering, 2008.[13] GaddamMallesham and AkulaRajani ,”AUTOMATICTUNING OF PID CONTROLLER USING FUZZY LOGIC .” ION SYSTEMS Suceava, Romania, May 25 – 27, 2006.[14]NurAzliza Ali, “Fuzzy logic controller for controlling DCmotor speedusing MATLAB applications”.[15]M.Chow and A. Menozzi ,”on the comparison of emergingand conventional techniques for DC motor control” proc.IECON,PP.1008-1013,1992.[16]A.course in fuzzy systems and control by Li-Xin Wang,Prentice-Hall international, Inc.[17]Ogata, K., Modern Control Engineering. Englewood Cliffs, NJ:Prentice Hall, 2001.[18]MATLAB and SIMULINK Version 2010a, the MathsworksInc,USA.[19]P.S Bhimbhra,electrical machinery, New Delhi, KhannaPublishers.[20] www.wikipedia.orgBooksIJSERMATLAB simulation for speed control of DC motor has beengenetic algorithm (GA).1053[1]B.J. Chalmers, “Influence of saturation in brushless permanentmagnet drives.” IEE proc. B, Electr.PowerAppl, vol.139, no.1,1992.[2]C.T. Johnson and R.D. Lorenz, “Experimental identification offriction and its compensation in precise, position controlledmechanism.” IEEE Trans. Ind ,Applicat, vol.28, no.6, 1992.[3]J. Zhang, N. Wang and S. Wang, “A developed method oftuning PID controllers with fuzzy rules for integrating process,”Proceedings of the American Control Conference,Boston, 2004,pp.1109-1114.[4] K.H. Ang, G. Chong and Y. Li, “PID control system analysis,design and technology,” IEEE transaction on Control SystemTechnology, Vol.13, No.4, 2005, pp. 559-576[5] H.X.Li and S.K.Tso, "Quantitative design and analysis of FuzzyProportional-Integral-Derivative Control- a Step towards Autotuning", International journal of system science, Vol.31, No.5,2000, pp.545-553.[6] Thana Pattaradej, Guanrong Chen and PitikhateSooraksa,"Design and Implementation of Fuzzy PID Control of a bicyclerobot", Integrated computer-aided engineering, Vol.9, No.4, 2002.Gopal, M., Control Systems-Principles and Design; 3rd Edition, NewDelhi: McGraw-Hill, 2008.Ajit K. Mandal, Introduction to Control Engineering-Modeling,Analysis and Design; 1st Edition, New Delhi, New AgeInternational, 2008.Franklin, G.F., J.D. Powel and A. Emami-Naeini; Feedback Controlof Dynamical Systems; 5th Edition, Upper Saddle River, NJ: PearsonEducation 2005.Dorf, R.C. and R.H. Brishop; Modern Control System, 10th Edition,Upper Saddle River, NJ: Pearson Education 2004.Nise, N.S.; Control System Engineering; 4th Edition Danvers, MA:John Wiley, 2003.Kuo, B.C. and F. Golnaraghi: Automatic Control System; 8th EditionDanvers, MA: John Wiley, 2003.IJSER 2014http://www.ijser.org
SPEED CONTROL WITH PID CONTROLLER A proportional-integral-derivative controller (PID controller) is widely used in industrial control systems. It is a generic control loop feedback mechanism and used as feedback controller. PID working principle is that it calculates an error
Oriental Motor Co., Ltd. offers a wide variety of speed control motors. Our speed control motor packages include the motor, the driver (controller), and a potentiometer which allows for easy speed control adjustment. There are three speed control motor product groups. The "AC speed control motor unit" that uses the most
Digital sensorless Torque Control of a BLDC motor. 78 Digital sensorless Speed Control of a Brushless motor. 79 Digital Speed Control of a Brushless motor using Encoders 80 Digital sensorless Speed Control of a DC Brushed motor 81 Digital Speed Control of a DC Brushed motor using Encoder 82 Digital Open-loop Control of a Brushless motor 83
speed control of induction motor obtain wide speed range of induction motor with smooth drive control, reduces torque ripples, noise and also reduces loss. So, that it will improve the efficiency of induction motor. Induction motor has wide speed range from 300 RPM to 1415 RPM or rated speed of particular induction motor. For the digital speed .
control option, control wiring issues can be tested by disconnecting the control wires from the motor. The motor should then operate using the dial on the motor for speed control. Motor will not reach maximum speed 1. Make sure dial is rotated full clockwise, if applicable. 2. Make sure motor is receiving 10 VDC, if applicable. 3.File Size: 284KBPage Count: 12
In a frequency converter drive, the actual operating speed of the motor may deviate considerably from its nominal speed (i.e. the speed stamped on the rating plate). For higher speeds, ensure that the highest permissible rotational speed of the motor or the critical speed of the entire equipment is not exceeded. When high speed operation exceeds the nominal speed of the motor, the following .
3.1 3-phase AC Induction Motor Drives The AC induction motor is a workhorse with adjustable speed drive systems. The most popular type is the 3-phase, squirrel-cage AC induction motor. It is a maintenance-free, less noisy and efficient motor. The stator is supplied by a balanced 3-phase AC power source. The synchronous speed ns of the motor is .
The speed control using PI and PID control modes is explained and an implementation of the controller using OP-AMPs is given. The response of the controller to load variations is looked at. 1 Introduction Figure 1: DC Motor Speed Control System The speed control of a motor is frequently required in industrial applications, robotics, home .
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