DIRECT TORQUE CONTROL OF BLDC MOTOR USING FUZZY LOGIC IN LABVIEW

DIRECT TORQUE CONTROL OF BLDC MOTOR USING FUZZY LOGIC IN LABVIEW

ABSTRACT

Brushless dc motors are widely used in many industrial applications due to their high efficiency, high power density and ease of control. In this paper, sensorless direct torque control (DTC) of bldc motor is implemented using fuzzy logic. In actual DTC both torque and stator flux linkage is controlled. In the proposed system, the control of stator flux linkage is avoided because every commutation will cause the stator flux linkage decreasing dramatically and sharp dip appears on the locus of the stator flux linkage every 60 electrical degrees. The best way to control the stator flux linkage amplitude is to know the exact shape of it, but it is considered too cumbersome in the constant torque region. Therefore the amplitude of stator flux linkage can be considered as a constant. The proper voltage vector selection is done using fuzzy logic controller which improves the dynamic performance. The sensorless operation is achieved by using a state observer. All the simulations were done in LabVIEW software of virtual instrumentation.

Keywords—Brushless DC motor(BLDC), Direct torque control (DTC), fuzzy Logic controller, stator flux linkage, voltage vector selection, state observer, LabVIEW, virtual instrumentation.

INTRODUCTION

Permanent magnet Brushless DC (BLDC) motors are nowadays widely used in industries such as HVAC industry, medical, electric traction, road vehicles, aircrafts, military equipment, hard disk drive, etc. due to their high efficiency, high power density, reliability and ease of control. Today there are basically two types of instantaneous electromagnetic torque controlled ac drives used for high-performance applications: vector and direct torque control (DTC) drives. The vector control, the most popular method uses a decoupling control which transforms the motor equations into a coordinate system that rotates in synchronism with the rotor flux vector. The second method, Direct Torque Control (DTC) is a form of hysteresis or bang-bang control to control torque (and thus speed) of electric motors. The basic concept behind the DTC of ac drive, as its name implies, is to control the electromagnetic torque and flux linkage directly and independently by the use of six or eight voltage space vectors found in lookup tables. The advantages associated with DTC are simplicity and high dynamic performance, no vector transformation and faster torque response. The torque ripple is the major problem associated with DTC (1-8).Various papers proposed new methods for eliminating the problems associated with classical DTC. Some papers proposed multilevel inverter in which there are more voltage space vectors available to control the flux and torque and hence smoother torque can be obtained

(2). Here more power switches are required which increases system cost and complexity. In (6) and (11) two PI regulators are required to control the flux and torque and they need to be tuned properly. In DTC,\ the stator flux is estimated by integrating the back- EMF which should be reset regularly to reduce the effect of the dc offset error. In papers (12), (13) dc offset is eliminated by introducing low pass filters for estimating the stator flux linkage. In this paper fuzzy logic is used for proper voltage vector selection. Fuzzy logic can be considered as a mathematical theory combining multi-valued logic, probability theory, and artificial intelligence to simulate the human approach in the solution of various problems by using an approximate reasoning to relate different data sets and to make decisions. It has been reported that fuzzy controllers are more robust to plant parameter changes than classical PI or controllers and have better noise rejection capabilities. The introduction of fuzzy logic quickens the torque response and provides smooth torque. Hence overall static and dynamic performance can be improved (7- 8).The torque and rotor speed are obtained from the back-EMFs, which are estimated using a state observer. In this paper, all simulations had done in the LabVIEW (Laboratory Virtual Instrument Engineering Workbench) software. In comparison with the other software tools, the simulation with LabVIEW provides easy debugging features and user friendly environment. The conventional six step control of BLDC motor is also simulated. The simulation results shows that the proposed DTC scheme using fuzzy logic has good control performance, compared with conventional method.

BLDC MOTOR DRIVE MODEL

The assumptions made for modeling BLDC motor are

1) The motor’s stator is a star wound type.

2) The motor’s three phases are symmetric, including their resistance, inductance and mutual inductances.

3) There is no change in rotor reluctance with angle due to non-salient rotor.

The BLDC motor is modeled in the stationary reference frame using phase currents, speed, and rotor position as state variables. The BLDC motor is modeled as

(1)

Where Va,Vb,Vc, Ia,Ib,Ic, and Ea,Eb,Ec are the stator voltages, stator currents and back-EMFs of all the three phases respectively, R and L are the resistance and inductance of stator phase winding respectively and p is the differential operator(d/dt).

The generated electromagnetic torque is given by

(2)

where ω is the speed.

The induced back-EMFsis of trapezoidal shapes and can be written as

Ea = fa(θ)λω (3)

Eb = fb(θ)λω (4)

Ec = fc(θ)λω (5)

wherefa(θ), fb(θ), fc(θ) are functions having trapezoidal shapes as back-EMFs and λ is the back-EMF constant.

BLDC – DTC SYSTEM

A conventional six-switch 3-phase inverter fed BLDC motor in two-phase conduction mode, as show in Fig. 1.

Fig. 1. An inverter driving BLDC motor

The primary voltages Van, Vbn, and Vcn are determined by the status of the six switches, S1, S2, S3, S4, S5 and S6. There are six nonzero voltage vectors:- V1(100001), V2(001001), V3(011000), V4(010010),V5(000110), V6(100100) and one zero voltage vector V0(000000). The six nonzero voltage vectors are 60 degrees apart from each other as in Fig. 2.

Fig. 2. Stator flux linkage space vector representation

The basic principle of direct torque control (DTC) is to choose the appropriate stator voltage vector out of eight possible voltage vectors according to the difference between the reference and actual torque and flux linkage so that the stator flux linkage vector rotates along the stator reference frame trajectory and produces the desired torque. The stator flux is controlled by properly selecting voltage vectors and hence the torques by stator fluxes rotation. The faster torque response is achieved by increasing the stator vector rotation speed. In this proposed paper (14), the DTC of a BLDC motor drive operating in two-phase conduction mode is simplified by controlling only torque and by intentionally keeping the stator flux linkage amplitude constant by eliminating the flux control in the constant torque region. Since the flux control is removed, fewer algorithms are required for the proposed control scheme. There is no need to control the stator flux linkage amplitude of a BLDC motor in the constant torque region due to the sharp changes which occur every 60 electrical degrees and hence the flux control is quite difficult. Therefore the stator flux linkage amplitude is kept almost constant on purpose and only torque is controlled. Also the zero voltage vector suggested in to decrease the electromagnetic torque could have some disadvantages, such as generating more frequent and larger spikes on the phase voltages that deteriorate the trajectory of the stator flux-linkage locus, increase the switching losses, and contributes to the large common-mode voltages that can potentially damage the motor bearings. To overcome these problems, a new simple voltage space vector look-up table is developed. The rotating speed of the stator flux linkage can be controlled easily by selecting proper voltage vector. For instance, in the region I of Fig. 2, for counterclockwise operation, if the actual torque is bigger than the reference, voltage vector V5 is selected to keep flux linkage rotating in the reverse direction. The torque angle decrease as fast as it can, and the actual torque decrease as well. Once the actual torque is smaller than the reference, voltage vector V2 is selected to increase torque angle and the actual torque. Hence the region of the stator flux linkage is known, selecting proper voltage vector can reach fast torque

control. The new simplified switching table is as follows:

TABLE I

THE SWITCHING TABLE FOR INVERTER

Here the ET represents the error in torque and is determined by the difference between reference torque and estimated torque. The value ‘0’ or ‘1’ stands for that the estimated value is smaller or bigger than the reference value respectively. I, II, III…, VI denotes the stator flux linkage region.

BLDC-DTC SYSTEM USING FUZZY LOGIC

In the past decade, fuzzy logic control techniques have gained much interest in many applications. Fuzzy Logic is a form of many-valued logic or probabilistic logic. It deals with reasoning that is approximate rather than fixed and exact. In traditional logic theory, have two valued logic-true or false (0 or 1). It has been extended to handle the concept of partial truth, where the truth value may range between completely true and completely false. They have a real time basis as a human type operator, which makes decision on its own basis. In the proposed BLDC-DTC system the fuzzy logic controller is used for selecting the proper voltage vector. It involves basically three steps:-

A. Fuzzification

The fuzzification is the process of a mapping from input to the corresponding fuzzy set in the input universe of discourse. There are two inputs to the fuzzy logic controller: - Torque error (ET) and angle information. Output of the fuzzy logic controller is proper voltage vector (V1 to V6).Torque error (ET) is divided into four fuzzy subsets with the linguistic value {PB, PS, NS, NB} and its universe of discourse is [-0.1 0.1]. Flux linkage angle (Angle) is divided into six fuzzy subsets {A1, A2, A3, A4, A5, A6} and its universe of discourse is [-π π]. The output Space voltage vector (V) is divided into six singleton fuzzy subsets {V1, V2, V3, V4, V5, V6}. Membership functions of twofuzzy input variables (ET and Angle) and one fuzzy output variables (Vi) are triangle type as shown in Fig.3.

(a) Membership function of torque error

(b) Membership function of flux linkage angle

(c) Membership function of space voltage vector

Fig. 3. Membership functions of the fuzzy controller

B. Rules and fuzzy reasoning

Fuzzy control rules are expressed in the IF-THEN format as IF ET is Ai and Angle is Bi, THEN V is Vi Where Ai, Bi, Vi denote fuzzy sets. The entire fuzzy rules expressed as a table as shown in the table II

TABLE II

FUZZY REASONING RULES FOR BLDC- DTC

Mamdani’s Min-Max method is employed in the fuzzy reasoning.

C. Defuzzification

The defuzzification is not required in the controller because output of the fuzzy controller is just six singleton fuzzy subsets which are the actual PWM voltage vector sequence composed of only seven different states, and these states could be directly used as the successor of the fuzzy rules.

SENSORLESS OPERATION USING BACK-EMF OBSERVER

In BLDC motor, the electromagnetic torque can be estimated directly from the back-EMF and the speed.

Here in this proposed method, an observer is used to estimate the back-EMF waveform. By choosing α- axis and β-axis stator currents and back-EMFs as the state-variables, the following state equations can be obtained

ẋ = Ax + Bu(6)

y = Cx(7)

Where x = [ia, ib, ea, eb]T is the state vector, u = [ua, ub]T is the input vector, y = [ia, ib]T is the output vector, and

The back-emf is estimated as

e^= K (y -y^)(8)

Where e^ = [ea, eb]T is the back-EMF vector, K= diag(k1, k2) is the gain matrix, k1 and k2 are positive constants. The relation between the rotor speed and amplitude of the back-EMF is given by

E = PKeω(9)

Where P is the number of pole pairs, Ke is the back- EMF constant of the motor, E is the amplitude of every phase back- EMF. The estimated speed is given by

The estimated rotor position is obtained by θ􀷠

 

LabVIEW IN VIRTUAL INSTRUMENTATION

LabVIEW (Laboratory Virtual Instrumentation Engineering Workbench) is a graphical programming language that uses icons instead of lines of text to create applications.LabVIEW programs are called virtual instruments, or Vis, because their appearance and operation imitate physical instruments, such as oscilloscopes and millimeters. Every VI uses functions that manipulate input from the user interface or other sources and display that information or move it to other files or other computers. LabVIEW object oriented programming uses concepts from other object oriented programming languages such as C++ and Java, including class, structure, encapsulation, and inheritance. We can use these concepts to create code that is easier to maintain and modify without affecting other sections of code within the application. We can use object oriented programming in LabVIEW to create user-defined data types. That is different types of physically existing systems can be simulated using this software. Here in this project we are using this software to design the control schemes of hybrid electric vehicle. The advantage of the LabVIEW software over other simulating software is that a wide range of hardware components are available which are helpful in testing as well as implementation of various applications that we develop in this software. In this proposed paper all the simulations had done in LabVIEW software.

BLOCK DIAGRAM OF SENSORLESS FUZZY BLDC-DTC SYSTEM

The block diagram of a sensorless fuzzy based BLDC DTC system is shown in Fig. 4. In the proposed system, there is an inner torque loop and outer speed loop. The reference torque is obtained from the speed controller and is limited at a certain value. Both voltages and currents are measured and then transformed into the stationary reference frame alphabeta components in the system.A back-EMF observer provides the estimated back-EMF. A fuzzy logic controller generates the switching signal which drives the inverter.

Fig. 4. Block diagram of proposed fuzzy BLDC-DTC system

SIMULATION RESULTS

All the simulations had done in LabVIEW software.The parameters of the BLDC used in the system are listed in the table 3.

TABLE III

BLDC MOTOR SIMULATION PARAMETERS

Fig. 6 shows the performance comparison between the conventional PWM method using the sensor and the proposed method. The torque ripple and the current ripple are much less, compared with the PWM scheme. A load torque of 2 Nm is applied. The figure shows the response performance of the proposed sensorless drive at 1000 rpm. Although the small deviation occurred after injecting the load, the performance is generally good.

(a) Theta waveform

(b) Current waveform

(c) Actual speed response for a reference speed of 1000 rpm

(d) Electromagnetic torque for a load torque of 2 Nm

Fig. 5. Simulation results of the proposed fuzzy based DTC scheme

(e) Actual speed response for a reference speed of 1000 rpm

(f) Electromagnetic torque for a load torque of 2 Nm

Fig. 6. Simulation results of conventional PWM method

CONCLUSION

The proposed two-phase conduction mode for DTC of BLDC motors is introduced as opposed to the conventional PWM control in the constant torque region. Much faster torque response is achieved compared to conventional PWM current and especially voltage control techniques. It is also shown that in the constant torque region under the two-phase conduction DTC scheme, the amplitude of the stator flux linkage cannot easily be controlled due to the sharp changes and hence it is kept constant. The proper voltage vector selection is done using fuzzy logic controller which improves the dynamic performance. The sensor less operation is achieved by using a state observer which also improves the performance. Fuzzy controller is virtually created in LabVIEW and utilized to implement the algorithm.

Speed control of BLDC motor is achieved using virtual instrumentation. This proposed method controls the speed for various ranges. The simulation results show that the proposed scheme has good estimation performance in low and high speed range and good control performance, compared with the conventional PWM method.

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