National Conference on Artificial Intelligence and Agents: Theory & Applications December 2011, BHU Varanasi.
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Speed Control of an Induction Motor Using Fuzzy Controller Based Methodology Swapnil Srivastava, Brijesh Singh and Abdul Zeeshaan
Abstract--Induction motors are mostly preferred and used in the industries for various drive applications as they are cheaper, more reliable, more efficient and more robust in comparison to other motors. However, the use of induction motors also has some disadvantages, mainly the controllability, due to its complex mathematical model and its non-linear behavior. Speed control of induction motors is also quite tedious. Therefore it required some numeric and intelligent based controllers. Designing all these controllers require the exact mathematical model of the machine. In the computation of exact mathematical model non-linierties are also involved. The controller required for these complex computations must be an efficient and fast. In recent past, the various intelligent controllers such as Fuzzy logic controller, Neuro-Fuzzy controller, particle swarm optimization (PSO) based controller etc have been introduced in order to solve the above said problem. This paper presents a fuzzy control strategy based speed control methodology for induction motors. The results shows the effectiveness of fuzzy controllers for speed control of induction motor.
I. INTRODUCTION
T
HERE are many strategies available in the literature for the speed control of an induction motor drive such as (i) Volts-per-hertz control, (ii) constant slip control, (iii) field oriented control etc. In Volts-per-hertz control correct magnitude and frequency of voltage is applied so as to achieve the reference speed without the use of speed feedback. In constant slip control, the drive system is designed so as to accept an input torque command, and therefore requires an additional feedback loop for the speed control. In field oriented control, nearly instantaneous torque control can be achieved, allowing the drive to act as a torque transducer. In addition to the control strategy, the performance and effectiveness of the drive mainly depend upon the control performance. The speed control issues are traditionally handled with the fixed gain proportional-integral-derivative (PID) controllers. However, the fixed gain controllers are very sensitive to parameter variations, load disturbances etc. The control parameters thus, need to be continuously adapted. Many adaptive control techniques for solving this problem are available in the literature such as internal model control (IMC), model reference adaptive control (MRAC), sliding mode control (SMC), variable structure control (VSC), self Authors are with the Department of Electrical Engineering, United College of Engineering and Research, Naini, Allahabad (U.P.). (e-mail:
[email protected],
[email protected],
[email protected] )
tuning PI controllers etc [27]. In the course of selecting the relevant topic in the field of speed control of induction and arriving at the exact crisp topic having all relevant fields that has been covered some literatures. In [2] presented the Design, Modelling, Simulation and Implementation of Vector Controlled Induction Motor Drive. The performance of the vector controlled scheme was tested on a 40hp prototype drive. The dynamic performance of the drive was extensively tested and the results were presented. The possibility of applying fuzzy algorithm in a microprocessor-based servomotor controller, which required faster and more accurate response as compared to other industrial processes, has been reported in [4]. The fuzzy proportional-plus-integral controller for the vector control system of an induction motor and discussed the performance of the system using this controller [5]. In [7] had described the fuzzy logic controller for the speed control of induction motor drive. A model reference adaptive control speed (MRAC) controller for indirect field oriented induction motor drives, based on fuzzy laws for the adaptive process is used with neuro-fuzzy procedure to optimize the fuzzy rules [9]. An indirect vector control Scheme of induction machine on an integrated Digital Signal Processor (DSP) system manufactured by the PACE GmbH. An off line parameters identification using Maximum likelihood estimation technique with a dc voltage source excitation was used to obtain the induction machine parameters required for the vector control operation [10]. Schnitman L. et al in [15] studied the use of fuzzy structures to model linear dynamic system. In [16] presented a novel speed control scheme using a new and simple structure of fuzzy logic controller (FLC) for an induction motor drive. The proposed FLC was developed to have less computational burden, which made it suitable for real-time implementation. A simplified vector control implementation strategy that can be realized in the absence of current sensors. A sensitivity analysis to study the effect of parameter deviation or mismatch was also investigated [18]. A hybrid artificial Intelligent controller for high performance of induction motor drive has also been used [20]. Cortajarena J.A. et al in [22] proposed a new high performance induction motor drive controlled with four proportional plus fuzzy PI controllers (P+FUZZY PI). This hybrid controller replaced the conventional PI controllers traditionally used for indirect vector control of induction motors. A novel adaptive neuro-fuzzy (NF)-based speed control scheme of an induction motor incorporating fuzzy
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National Conference on Artificial Intelligence and Agents: Theory & Applications December 2011, BHU Varanasi. logic laws with a five-layer artificial neural network. Only three membership functions were used in this controller for each input for low computational burden, which was found suitable for real-time implementation [23]. In [24] implemented a self-tuning fuzzy controller for speed control of three-phase induction motor drive. The proposed controller was found to have the ability to adjust its parameters online according to the error between actual machine speed and a model reference. A hybrid Fuzzy Logic Controller (FLC) with vector-control method for induction motors. The vectorcontrol method was improved by using FLC instead of a simple PD controller. In this hybrid controller high quality regulation was achieved through utilization of the FLC, while stability of the system during transient and around wide range of operating points were assured through application of the vector-control [25]. The hybrid controller was validated by applying it to a nonlinear model of the motor. The present investigation has deals with the speed control of an induction motor using fuzzy control strategy. The solution is obtained by fuzzy control actions. To demonstrate the performance of proposed method MATLAB simulation work has been used in proposed work. II. INDUCTION MOTOR AND CONTROL There are many strategies available in the literature for the speed control of an induction motor drive such as (i) Voltsper-hertz control, (ii) constant slip control, (iii) field oriented control etc. In Volts-per-hertz control correct magnitude and frequency of voltage is applied so as to achieve the reference speed without the use of speed feedback. In constant slip control, the drive system is designed so as to accept an input torque command, and therefore requires an additional feedback loop for the speed control. In field oriented control, nearly instantaneous torque control can be achieved, allowing the drive to act as a torque transducer. In addition to the control strategy, the performance and effectiveness of the drive mainly depend upon the control performance. The speed control issues are traditionally handled with the fixed gain proportional-integral-derivative (PID) controllers. However, the fixed gain controllers are very sensitive to parameter variations, load disturbances etc. The control parameters thus, need to be continuously adapted.
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tuning PI controllers etc [27]. Fig. 1 shows the basic block diagram of the closed loop speed control of an induction motor. Actual speed is compared with the desired speed continuously with the help of a feedback loop. An error is generated from comparison and is fed to the controller which is accordingly generates an appropriate signal to control the final control element output [12]. 2.1 CONTROL STARTEGIES There are mainly three strategies that are discussed herein. Volts-per-Hertz (V/f) Control Perhaps the simplest and least expensive induction motor drives speed control strategy is the constant Volt-per-Hertz (V/f) control. Actually this speed control strategy is based on two observations i. Near the synchronous speed, a torque-speed characteristic of an induction motor is normally quite steep and the rotor speed is dependent on the supply frequency. Thus the speed can be controlled approximately by controlling the value of frequency. ii. For steady state condition, the control strategy allows high peak voltage and current transients. These not only affect the drive’s dynamic performance but also the power conversion efficiency. The main drawback of this type of control strategy is that it is an open loop scheme which introduces some measure of error, particularly at low speed [27]. Constant Slip current control A three-phase bridge inverter is used for this purpose. Although the three-phase bridge inverter is fundamentally a voltage source device, by suitable choice of modulation strategy it is possible to achieve current source operation from the device. One of the primary disadvantages of this approach is that it requires phase-current feedback. However, at the same time the scheme offers the advantage that the current can be readily limited, making the drive extremely robust [12]. Field Oriented Control (Vector Control)
Fig.1Block Diagram of a Loop Speed Control Scheme of Induction Motor Many adaptive control techniques for solving this problem are available in the literature such as internal model control (IMC), model reference adaptive control (MRAC), sliding mode control (SMC), variable structure control (VSC), self
In many of the motor drive systems, it is desirable to make the drive act as a torque transducer wherein the electromagnetic torque can almost instantaneously be made equal to a torque command. In such a system, speed or position control is dramatically simplified because the electrical dynamics of the drive become irrelevant to the speed or the position control problem. The method can be used for steady state as well as transient state operations of the motor [3]. The field oriented control may be direct or indirect in nature depending upon the field angle obtaining method. Looking at the various advantages, this control strategy is being proposed in the present work which is discussed in CHAPTER 2 in detail.
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National Conference on Artificial Intelligence and Agents: Theory & Applications December 2011, BHU Varanasi. 2.2 CONTROLLERS PID controller fall amongst the oldest and the most commonly used controllers in the industries because of their simple structure and wide availability. A PID controller attempts to correct the error between a measured process variable and a desired set point by evaluating and then generating a corrective action that can adjust the process accordingly and rapidly, in order to keep the error minimal. The proportional value determines the reaction to the current error, the integral value determines the reaction based on the sum of the recent errors, and the derivative value determines the reaction based on the rate at which the error has been changing. The weighted sum of these three actions is used to adjust the process via a control element such as the position of a control valve or the power supply of a heating element etc. The use of the PID algorithm for control does not guarantee optimal operation of system control or the system stability [6]. A logic based on the two truth values True and false is sometimes inadequate when describing human reasoning which is fuzzy in nature. Fuzzy logic uses the whole interval between False (0) and True (1) to describe human reasoning. As a result, Fuzzy lo gic is being applied in rule-based automatic controllers. Almost anything called a set in ordinary conversation is an acceptable set in the mathematical sense also. The processing stage is based on a collection of logic rules in the form of IF-THEN statements, where the IF part is called the "antecedent" and the THEN part is called the "consequent". Typical fuzzy control systems have dozens of rules constituting the fuzzy rule base [29].
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Generally in addition to these components of a fuzzylogiccontroller two more components are added to the structure, which are actually not the part of fuzzylogiccontroller. They are preprocessor and postprocessor as shown in Fig. 2.
Fig. 2 Structure of Fuzzy Logic Controller Preprocessor Generally the measured inputs are not linguistic variables rather they are crisp in nature. The first block in Fig. 6.5 called the preprocessor first conditions the measurements before they enter the controller by performing some of the following functions: Quantization: This is done to convert the incoming values in order to find the best level in a discrete universe. Normalization or scaling. Filtering in order to remove noise. Averaging to obtain long term or short term tendencies. A combination of several measurements to obtain key indicators, and Differentiation and integration. This process is called the preprocessing.
III. FUZZY LOGIC CONTROLLER Fuzzy logic is a form of many-valued logic derived from fuzzy set theory to deal with reasoning that is approximate rather than fixed and exact. In contrast with "crisp logic", where binary sets have two-valued logic, fuzzy logic variables may have a truth value that ranges in degree between 0 and 1. Furthermore, when linguistic variables are used, these degrees may be managed by specific functions. Describing the real world precisely is too complicated and requires lots of effort, therefore approximation (or fuzziness) must be introduced in order to obtain a less complex yet traceable model. In this information era, human knowledge becomes increasingly important, So there is need of theory which can formulate it and put it into engineering system together with other information like mathematical model and sensory measurements [39]. 3.1 Structure of Fuzzy Logic Controller Fuzzylogiccontroller components: 1. Fuzzifier 2. Inference Engine 3. Rule-Base 4. Defuzzifier
has
the
following
four
main
Fuzzifier Fuzzification is the operation by which crisp set is transformed into a fuzzy set. The first block inside the controller is called the fuzzifier, which converts the coming input data into a degree of membership. This conversion of each piece of input data to a degree of membership is done by looking up in one or several membership functions used i.e., input data is matched with the conditions of the rules to determine. The degree of membership directly depends upon how well the conditions of the rules are matched. Inference Engine Fuzzy inference is the process of formulating the mapping from a given input to an output using fuzzy logic. The mapping then provides a basis from which decisions can be made. The process of fuzzy inference involves membership functions, logical operations, and If-Then rules. The inference engine has two basic tasks: Matching: determining the degree to which each rule is relevant to the current situation as characterized by the inputs Inference step: getting conclusions using current inputs and the knowledge in the rule-base
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National Conference on Artificial Intelligence and Agents: Theory & Applications December 2011, BHU Varanasi. Rule-Base In the rule-base there are two sets one is called the condition and the other is called the conclusion. The rules may see several variables both in condition and the conclusion of the rules. The controller can therefore be applied to a multi-inputmulti-output (MIMO) system or to a single-input-single-output (SISO) system. In the present work a SISO problem has been dealt with. Rule formats are basically a linguistic controller having rules in if-then format, but they can be represented in some other formats also. A typical rule-base is shown in Table.1. Table 1 A Typical Rule-base e ce
NL
NM
ZE
PM
PL
NL
NL
NL
NL
PM
ZE
NM
NL
NL
NM
ZE
PM
ZE
NL
NM
ZE
PM
PL
PM
NM
ZE
PM
PL
PL
PL
ZE
PM
PL
PL
PL
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it can also show reasons for it in the form of linguistic rules with their ring levels. The approach needs a feature detection, which obtains indices like variability, settling-time, rise-time etc., from the measurements. The feature detection can also be used in fuzzy tuning [29]. 3.3 Fuzzy tuning An alternative is to build up a fuzzy rule-base for the tuning. In the case of PID control, the fuzzy supervisor detects the risetime and the settling-time on each time when the set-point for the process value is changed, and decides if the PID parameters should be changed. The fuzzy supervisor performs the tuning like a human operator, by trial and error. However, in conventional control theory there exists many more powerful tuning techniques. The rule-base needed in tuning can be very general. A disadvantage is that quite a few step responses are needed before the control performance is acceptable. IV. PROPOSED SIMULINK MODEL AND TEST RESULTS
Where: e = Error ce = Change in Error NL = Negative Low NM = Negative Medium ZE = Zero Error PM = Positive Medium PL = Positive Low Defuzzifier The last block inside the fuzzy-logic controller is called the defuzzifier. Before passing the output of the controller the fuzzified output must be defuzzified, as the real world plants accepts only the crisp values as input. For a given input, several if-then rules could be fired at the same time. Each rule would have a different degree of membership, because a given input may belong to more than one fuzzy set[29]. This process of converting the fuzzy outputs into crisp value is called defuzzification. 3.2 Fuzzy Performance Measures A more intelligent approach to combine fuzzy logic with a conventional controller is an application where the fuzzy rulebase can be built up to detect necessity to retune the controller if the performance of the control loop deteriorates. The fuzzy supervisor can observe the increase in process variability, oscillations, increased settling-time, long rise-time, etc. Knowledge which is needed to decide if the retuning is needed can usually be easily converted into the form of a fuzzy rulebase. This method can be used to indicate the operators that now they should allow the on-line tuning of the controller by an auto-tuning algorithm. When a fuzzy system gives an alarm
MATLAB/Simulink is a software package for modeling, simulating, and analyzing dynamical systems. It supports linear and non-linear systems, modeled in continuous time, sampled time, or a hybrid of the two. Systems can also be of multi-rate, i.e., have different parts that are sampled or updated at different rates. For modeling, Simulink provides a graphical user interface (GUI) for building models as block diagrams, using click-and-drag mouse operations. In present paper simulation of closed-loop vector control based methodology with the proposed fuzyy controller is simulated. These simulations give the idea about starting-current, startingtorque, starting-time, settling-time, steady-state torque, steadystate current, steady-state error in speed etc with the different modes and different controllers. Simulation diagram of each drive has also been discussed. A three-phase, 200 HP, 460V, 60 Hz, 4 pole, star connected, squirrel cage induction motor has been selected for simulation. It is supplied with the rated voltage and frequency. Simulation results are collected with the help of a scope to analyze the motor performance. The parameters of the motor are: Stator resistance = 14.9 m ohm Stator inductance = 0.3027 mH Rotor resistance = 9.3 m ohm Rotor inductance = 0.3027 mH Mutual Inductance = 10.5 mH It has been observed that the problem of dead zone can be completely eliminated by using a fuzzy controller. The proposed fuzzy controller uses triangular membership function. Following are the simulation result for speed with set point at 500 rpm, 700 rpm and 1200 rpm, as shown in Fig. 4, 5, and 6 respectively. Fig. 7 and 8 respectively show the simulation results for variation of electromagnetic torque and stator current with time. The above simulation results show that there is no dead zone in vector control strategy while using fuzzy controller thereby dynamic response of the induction motor is improved.
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National Conference on Artificial Intelligence and Agents: Theory & Applications December 2011, BHU Varanasi.
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Fig.3. Implementation of Vector Control Method with the Fuzzy Controller
Fig.5 depicts that the convergence of stator current from starting current to normal running current is faster while using fuzzy logic controller, which further improves the drive performance.
Fig. 6. Speed-Time Curve with Set Point 1200 r.p.m. using FLC
Fig. 4. Speed-Time Curve with Set Point 500 r.p.m. using FLC
Fig. 7. Torque Variation with Time using FLC
Fig. 5. Speed-Time Curve with Set Point 700 r.p.m. using FLC Fig. 8 Stator Current Variation with Time using FLC V. CONCLUSION Simulation using vector control method has been implemented successfully on a 3-phase induction motor. The results in closed-loop with Fuzzy Logic Controller have been analyzed and compared to study the performance of the drive in various operating conditions. Following observations can be inferred from the above investigation :
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National Conference on Artificial Intelligence and Agents: Theory & Applications December 2011, BHU Varanasi. The Vector Control Algorithm used with Fuzzy Controller increased the speed variation range for 3-phase induction motor. The fuzzy controller completely removed the problem of dead zone while in convention PI controller. The rise time is reduced by using fuzzy logic controller The fuzzy controller improves the convergence of stator current from starting to the normal run. The fuzzy logic based controller has shown very encouraging results and indicated better dynamic response. VI. REFERENCES [1] [2]
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[5]
[6]
[7]
[8]
[9]
[10]
[11]
[12] [13]
[14]
[15]
[16]
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[17] Ibrahim A. M., “Fuzzy logic for Embedded Systems Applications,”A Text Book, Newness. Elsevier Science ,USA, 2004. [18] Wang Z.S., Ho S. L., “Indirect rotor Field Orientation Vector Control for Induction motor Drives in the absence of Current Sensors” Proceedings of IEEE Conference on Power Electronics and Motion Control, Vol. 3, pp. 1-5,August, 2006. [19] Hossain M. J., Hoque M. A., Ali M. A., Rahman M. A., “Fuzzy-LogicBased Control for Induction Motor Drive with the consideration of core losses,” Proceedings of International Conference on Electrical and Computer. Engineering., pp. 333-336,December, 2006. [20] Ko J., Choi J., Chung D., “Hybrid artificial intelligent control for speed control of induction motor” SICE-ICASE Proceedings of International joint conference, pp. 678-683,October 2006. [21] Linsen Z., Yangping T., Dailin Z., “Application of Fuzzy Controller in the speed control of Permanent Magnet Linear Motors,” Proceedings of IEEE Conference on Controls, pp. 242-245, July,2007. [22] Cortajarena J.A., Marcos J. , Alvarez P., Vicandi F.J.,.Alkorta De P “Indirect Vector Controlled Induction Motor with four Hybrid P+Fuzzy PI Controllers”, IEEE International Symposium on Industrial Electronics, p.p. 197-202, June,2007. [23] Uddin M. Nasir, “Development of a Self-Tuned Neuro-Fuzzy Controller for Induction Motor Drives”, IEEE transactions on industry applications, Vol. 43, p.p.1108-1116, july/august, 2007. [24] Masiala M., Vafakhah B., Salmon J., Knight A.M., “Fuzzy self-tuning speed control of an indirect field control induction motor drive,” IEEE Transactions on Industry Applications, Vol. 44, no.6, pp. 1732-1740, November-December,2008. [25] Kimiaghlam B., Rahmani M., Halleh H., “Speed & Torque Vector Control of Induction Motors with fuzzy logic controller, Proceeding IEEE Conference on Control, Automation and Systems, pp. 360365,October, 2008. [26] Kamalasadan S., “An Intelligent coordinated design for excitation and speed control of Synchronous Generators based on Supervisory Loops,” Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century, pp. 242-245,July 2007. [27] Bhimbra P.S.”Electrical Machinery” A Text Book, Khanna Publication (India), 2007. [28] Rajsekaran S., Vijaylakshmi Pai G.A. “Neural Network, Fuzzy Logic and Genetic Algorithm-Synthesis and Application”, A Text Book, PHI Learning Private Limited, New Delhi (India), 2009. [29] Ross Timothy J. “Fuzzy Logic with engineering applications”, A Text Book, Wiley India, 2nd Edition,, 2009. [30] Broedc H. W. Van der and Kerkman R.J., "Analysis and Realization of a pulse-width modulator based on voltage space vector", IEEE Transactions on Industry Applications, Vol. 24, pp. 142-150, January/February 1988. [31] Bose B. K., Power Electronics and AC Drives, A Text Book, Prentice Hall, 1986. [32] Koyama M., Yano M., Kamiyama I., and Yano S., "MicroprocessorBased Vector Control System for Induction Motor Drives with Rotor Time Constant Identification Function", IEEE Transactions on Industry Applications, Vol. IA-22, No.3, May/ June 1986. [33] Xhgyi Xu and Donald W. Novotny, "Implementation of Direct Stator Flux Orientation Control on a Versatile DSP Based System," IEEE Transactions on Industry Applications, Vol. 27, no. 4, July/August 1991. [34] Marino R, Peresada S., and Valigi P., "Adaptive Input-Output Linearizing Control of Induction Motors" , IEEE Transactions on Automatic Control, Vol. 38, pp. 208-220, No. 2, February 1993. [35] M. Bodson, J. N. chiasson. R T. Novolnak, "A Systematic Approach to Selecting Flux References for Torque Maximization in Induction Motors", IEEE Transactions on Control System Tech. Vol. 3, No. 4, pp 388-397,December,1995 [36] Hakiki K.,Meroufel A.,Cocquempot V., Chenafa M.,“A new adaptive fuzzy vector control for permanent magnet synchronous motor drive”,Proceedings of International Conference on Control and Instrumentation, p.p. 922-927,June, 2010.
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