Vol.03, Issue 04, April 2018, Available Online: www.ajeee.co.in/index.php/AJEEE A CRITICAL REVIEW: CONTROL STRETAGIES FOR POWER QUALITY IMPROVEMENT
OF BLDC MOTOR
NARAYAN PRASAD CHOUDHARY Research Scholar Me, Deptt. EE, JEC Jabalpur
HEMANT AMHIA Prof. Deptt. EE, JEC Jabalpur
Abstract: The brushless DC motor drives behave very well during the test trajectories. The DC bus voltages are maintained at relatively constant levels during the deceleration of the motors. The developed torques are proportional to the motor currents' amplitudes. This demonstrates the good operation of the field-oriented control algorithms. For this research we reviewed number of research article for proposed research.
INTRODUCTION
In recent years BLDC motors are used in high performance drive system because of its advantages like high ratioof starting torque to inertia and faster dynamic response. A BLDC servo motor which is usually a BLDC motor of lowpower rating can be used as an actuator to drive a load. Because of their high reliability, flexibility and low cost, BLDCservo motors are widely used in industrial applications, robot manipulators and home appliances where speed and positioncontrol of motor are required [1], [2]. BLDC servo motor is a common actuator in control system.
Servo drive systemsnormally use the full four quadrant operations which allow bidirectional speed control with regenerative braking capabilities [3].This paper demonstrates the position control based drive by closed loop PI controller.
The output of the motor iscoupled to a control circuit when the motor turns, its speed and/or positions are relayed to the control circuit. If the rotation of the motor is impeded for whatever reason, then the feedback mechanism senses that the output of the motor is not yet inthe desired location.
The control circuit continues to correct the error until the motor finally reaches its proper point [4] –[6]. The information regarding the degree of rotation on its axis always sends to the robotic control circuit. Hence it ispossible for the control circuit to sense the inappropriate position and actual position to be made [7] – [9]. A 360o freerotating vertical arm with load torque curve is show in Figure 1.During the horizontal motion of the arm in full rotation, there is no change in the load torque whereas when the arm rotates in vertical motion, the load torque varies with respect
to change of position. If the vertical rotating arm position gets changes from 0o to 180o, arm will rotate clockwise directionand the load is increasing propositionally with angular displacement. When the arm needs to reach 90o, it is requiredmaximum torque to oppose the gravitational force.
From 90o to 180o, torque will gradually decrease and reaches zero loadtorque at 180o. During this period motor will work on forward motoring mode and the load curve looks like half sin wave. Whatever the load is varying we need to maintain angular velocity constant, otherwise arm will be delayed in time to reachthe desired position.
Similarly if the position gets changes from 180o to 360o the load torque is negative due to gravitationalforce, then the arm will be pulled down to downward direction since the speed may go higher than safe limited. During thiscondition we need to work drive on breaking mode with constant speed and hence it is called as forward braking.
Figure 1: Torque versus Angle of Vertical Arm
Vol.03, Issue 04, April 2018, Available Online: www.ajeee.co.in/index.php/AJEEE POSITION CONTROL OF BLDC SERVO
DRIVE
When actual position of thearm, obtained through the position sensor positions is compared to a set position, a difference between two generates anerror. If the set position is greater than the actual position, then the position error output of the comparator is positive else itis negative if the set position is lower than the actual position. The position error so obtained is processed in position PIcontroller which generates the pulse to control the decoded gate signals to feed three phase inverter. In many applications, asimple voltage regulation would cause lots of power loss on control circuit thus, a pulse width modulation method (PWM)is used in many motors controlling applications. In the basic PWM method, the operating power to the motors is turned ONand OFF to modulate the current to the motor.
The PWM control method uses the width of the pulses in a pulse train tocontrol the speed of the motor.The duty cycle of the pulses determines the speed of the motor. Therefore, the higher the duty cycles, higher thespeed. This would give the motor the ability to safely vary the position from stand still to its maximum position.Sometimes, the rotation direction needs to be changed.
The period of current variation and also the number of pulses areproportionally related to the motor speed. In the BLDC motors, the rotation is changed by changing the hall signal decoding sequence. The inverter is formed by MOSFET switches, energizing the perticular winding of the motor according to thecontrol signals of the decoder.
The motor shaft and robot arm are connected through gear arrangement.
Most of theactuators are using self locking gear mechanism arrangment to provide holding torque such as warm and worm wheelgears. The corresponding position of the motor is controlled without any speed change and oscillations. The position PIcontroller block model is given in Figure 3. It is one of the most common approaches for position control in industrialelectrical drives. In general, because of its simplicity and the clear relationship existing between its parameters and thesystem response specifications, the Ziegler-Nichols method
is used for determining the Kpand Ki constants of the PIcontroller.
OVERVIEW
Robot manipulator field is one of the challenging fields in industrial automation systems. Many researchers focus to study the improvement of the robot arm controller. This is due to the nonlinearities and physical input present in the dynamics of the robot arm. This thesis presents the design and modelling of a 6 DOF robot arm controller using fuzzy PID controller. The study of robot manipulator can be divided into two parts which is the mathematical modelling of the manipulator and also the control method of the robot arm. The modelling of robot arm includes the analysis for kinematics and dynamics computations.
The kinematics model is a prerequisite for the dynamic model and fundamental for practical aspects like motion planning, singularity and workspace analysis.
PROBLEM STATEMENT
Generally, different types of robot controller give different output performance of robot movement. The performance of a robot arm can be analyzed by evaluating the movement from an initial position to a final position.
Nonlinearities, parameter uncertainties and external disturbances exist in linear robot controllers. Therefore, it is difficult to eliminate these undesired effects in order to achieve good stability and precise tracking control performance. Output response of linear controller would sometimes deviate from the desired input trajectory. Thus, a robot controller must be designed to handle both linear and nonlinear systems. The controller is used to minimize the error between the intended and the actual positions. The controller must meet certain specifications. These specifications such as reducing overshoot, minimizing rising time and eliminating the steady state error. In addition the robot arm should be able to operate as usual when the external disturbance is applied to any joint of the robot arm.
LITERATURE REVIEW
Industrial robot manipulators are generally used in positioning and handling devices. Rapid technology
Vol.03, Issue 04, April 2018, Available Online: www.ajeee.co.in/index.php/AJEEE changes in industrial robot manipulator
urged human to develop and improve the performance of control strategy which can be used to replace human in hazardous, complex and boring tasks [1]. Generally, the dynamics behavior of robot manipulators can be described by the nonlinearity and external input parameter such as in friction, load and disturbances [2]. In the field of robot manipulators, many researchers have proposed literatures and discussed the kinematics analysis of industrial robots such as SCARA, PUMA 560 and SG5-UT robot manipulator [7], Other papers discussed about the control techniques problems such as PID, FLC, neural network algorithm and also the combination of the three controllers.
Some of the research employed the DenavitHartenberg method which it is used to model the mathematical equation of kinematics [11]. The forward and inverse kinematic equation analysis was generated and implemented using a simulation program , Nowadays, PID controller is a good control technique in industrial automation applications due to its simplicity and robustness [3].
Although PID is most widely used controller in industry, it has several drawbacks. One of the drawbacks is that the PID controller is not sufficient to obtain the desired tracking control performance because of the nonlinearity of the robot manipulator [4]. Therefore, PID controller is not a good technique to control a system with nonlinear environment.
Fuzzy logic controller (FLC) was introduced to overcome the drawbacks of PID controller. Then, fuzzy logic control was developed by [6] and Assilian. Fuzzy logic controller can be used to achieve better performance of robot manipulator
because its algorithm can implement a human’s heuristic knowledge on how to control a system. The regularity use of PID and the good algorithm of FLC can be combined into a controller to optimize the control performance of robot manipulator.
There are a lot of papers discussed about the PID tuning. Fuzzy gain scheduling of PID controllers was presented by [7]
which proposed the uses of fuzzy rules and reasoning to determine the PID controller parameter and how to control it online. This method was also proposed by [8] which discussed about the fuzzy supervisory control in reducing the amount of additional PID tuning. An approach to combine a fuzzy logic controller and PID controller was presented by [9]. In that paper, he replaced the proportional term in the PID with fuzzy P controller and thus became the fuzzy P+ID controller.
TRACKING PERFORMANCE OF THE MOTOR DRIVES
The test trajectories described above constitute one of the most demanding trajectories for the motor drive of the first and second joints. They are used here to evaluate the tracking performance of the two electric drive systems. In the example, the manipulator is programmed to rotate from -30° to 30° during 1.5 seconds, and at the same time the arm is moved from the back position (Θ2 = -45°) to the most advanced position (Θ2 = 45°). The simulation is run using a time step of 1 µs. The responses of the manipulator and the motor drives 1 and 2 are displayed on three scopes connected to the output variables of the AC6 and Robot blocks.During the movement, the joint positions Θ1 and Θ2 follow the imposed cubic trajectories with low tracking error
Table 1 Literature Survey
SN AUTHOR NAME TITLE NAME LIMITATIONS
1 PadmarajaYedamale Brushless DC (BLDC)
Motor Fundamentals, Brushless Direct Current (BLDC) motors are one of the motor types rapidly gaining popularity. BLDC motors are used in industries such as Appliances, Automotive, Aerospace, Consumer, Medical, Industrial Automation Equipment and Instrumentation.
As the name implies, BLDC motors do not use brushes for
Vol.03, Issue 04, April 2018, Available Online: www.ajeee.co.in/index.php/AJEEE commutation; instead, they are electronically commutated. BLDC motors have many advantages over brushed DC motors and induction motors. In this application note, we will discuss in detail the construction, working principle, characteristics and typical applications of BLDC motors. Refer to Appendix B:
?Glossary? for a glossary of terms commonly used when describing BLDC motors.
2.
SwethaPola,
DrK.P.Vittal Simulation of Four Quadrant Operation
& SpeedControl of BLDC Motor on MATLAB / SIMULINK
The modeling procedure presented in this paper helps insimulation of various operating conditions of BLDC drivesystem.
The performance evaluation results show that, such a
modeling is very useful in studying the drive system beforetaking up the dedicated controller design, accounting therelevant dynamic parameters of the motor.
3.
C. SheebaJoice, S. R.
Paranjothi, and V.
JawaharSenthil Kumar
Digital Control Strategy for Four Quadrant Operation of Three Phase BLDC Motor With Load Variations
In this paper, a control scheme is proposed for BLDC motorto change the direction from CW to CCW and the speed control is achieved both for servo response and regulator response.The motor reverses its direction almost instantaneously, it willpass through zero, but the transition is too quick. The time takento achieve this braking is comparatively less. The generatedvoltage during the regenerative mode can be returned back to thesupply mains which will result in considerable saving of power.This concept may well be utilized in
the rotation of
spindles,embroidery machines and electric vehicles where there is frequentreversal of direction of rotation of the motor.The significant advantages of the
proposed work are:
simplehardware circuit, reliability of the control algorithm, excellentspeed control, smooth transition between the quadrants andefficient conservation of energy is achieved with and withoutload conditions. The designed and implemented
Vol.03, Issue 04, April 2018, Available Online: www.ajeee.co.in/index.php/AJEEE prototype
model may be implemented even for higher rated motors.Arcing might occur during the switching on and off of therelay contacts, when implemented in higher rating motors. Butif the proposed method is implemented in low power motors,
like motor used in
sewing/embroidery machines, arcing will bevery less which is not even visible.
4.
SnehaK.Awaze Four Quadrant
Operation Of BLDC
Motor In
MATLAB/SIMULINK.
The application of this work is that on the basis ofsimulation and analysis carried out can be to select theproper motor specifications to match the load requirements.
The simulation is used to predict the behavior of actualsystem.
These predicted results can be used to determine therange of parameters of controller while designing thesystem.
5.
Rong-Jong Wai, Meng-
Chang Lee Intelligent Optimal Control of Single-Link Flexible Robot Arm
This paper has successfully demonstrated the application ofan intelligent optimal control system to the position control ofa nonlinear mechanism system.
The nonlinear mechanism usedin this study is a flexible robot arm
driven by a PM
synchronousmotor drive. The proposition of the control scheme is to achievegood tracking performance without the strict constraints andprior knowledge of the controlled system. The design procedureand the theoretical stability proof of the proposed intelligentoptimal control system were described in detail. Moreover,simulation and experimentation were carried out using periodicreference trajectories to verify the effectiveness of the proposedcontrol system. The major contributions of this paper are: 1) thesuccessful development of an intelligent optimal control system,
in which the proposed controller was made of an optimal controllerwhich minimize a quadratic performance index, a FNNcontroller which learn a
Vol.03, Issue 04, April 2018, Available Online: www.ajeee.co.in/index.php/AJEEE nonlinear function to implement the optimalcontroller, and a robust controller which compensate theapproximation error of the FNN controller; 2) an adaptive boundestimation algorithm was proposed to estimate the bound of uncertainterm avoiding the chattering phenomena; and 3) the successfulapplication of the intelligent optimal control system tocontrol the motor-mechanism coupling system considering thepossible occurrence of uncertainties.
6.
V. M. Hern´andez-
Guzm´an, V.
Santib´a˜nez and R.
Campa
PID control of rigid robots actuated by brushless DC motors
We have presented a stability analysis for rigid robots
actuated by BLDC motors when the electric dynamics ofthese actuators is taken into account.
We have found, forthe first time, theoretical evidence indicating that a linearPD controller suffices to globally regulate position underthese conditions. Contrary to the common assumption, ourcontroller does not require inductance to be small. Wehave presented for the first time a formal stability analysisfor torque control. The proposed Lyapunov functions arepartitioned into one Lyapunov function for each closed loopdynamical equation.
Results in proposition 2 have beenrecently extended by the authors to design an adaptivePID controller achieving global
convergence to the
desiredpositions. This means that the exact knowledge of any robotor actuator parameter is not required. Such a result has beensubmitted for publication some where else.
Mehmet
TuranSoylemezMetinG okasan 0. Seta Bogosyan
Position Control of a Single-Link Robot- Arm Using
a Multi-Loop PI Controller
A new approach on assigning dominant poles of the closedloopsystem is applied lo a single-link robot arm in this paper.It has been shown that single-link robot arm can be modeledas a second order system, and it is possible to assign 2 ofthe closed-loop system poles by the help of a multi-loop PIcontroller. After assigning the dominant poles of the
Vol.03, Issue 04, April 2018, Available Online: www.ajeee.co.in/index.php/AJEEE
7. closedloopsystem, two
parameters of the controller can be chosenfreely. It has been illustrated that this freedom in design canbe used to place the remaining pole and the zero of the systemto the left of the dominant poles, to bound the control signal(o(t)), to reduce the effects of the disturbances, and to increasethe robustness of closed-loop system with respect
to parameter(load)
changes.Suggested future work includes a comprehensive comparisonof different control techniques for the single-link robotarm, and searching for the possibility of combining some of the gain scheduling techniques with the dominant poleplacement approach introduced in this paper.
8.
R.Manikandan,
R.Arulmozhiyal Position Control of DC Servo Drive
Using Fuzzy Logic Controller
The fuzzy logic controller based position control of DCservo motor drive with simulation and real time experimentalimplementation using dspic30F2010 controller has beenpresented in this paper.
It is found that the position of DCmotor can be controlled and return back to desired valueeasily. In Fuzzy logic control it is not necessary to change thecontrol parameters at any conditions. It does not happen inconventional PI. It is clear that the fuzzy PI controller is moreadvantageous than conventional PI because the settling time isvery low compared to the conventional PI Controller.
It givesbetter dynamic response.
This proposed scheme is verysuitable for applications of industrial position control drives.
CONCLUSION
The brushless DC motor drives behave very well during the test trajectories. The DC bus voltages are maintained at relatively constant levels during the deceleration of the motors. The developed torques are proportional to the motor currents' amplitudes. This demonstrates the good operation of the field-oriented control algorithms. For this research we
reviewed number of research article for proposed research.
REFERENCES
1. Bimal K. Bose. (1996) Power Electronics and Variable Frequency Drives Technology and Applications, Wiley-Blackwell.
2. PadmarajaYedamale,Brushless DC (BLDC) Motor Fundamentals, microchip application note AN-885.
3. Vinatha U, et al, (2008) Simulation of Four Quadrant Operation & Speed Control of BLDC Motor onMATLAB/SIMULINK, IEEE Conference on TENCON 2008, 1- 6
Vol.03, Issue 04, April 2018, Available Online: www.ajeee.co.in/index.php/AJEEE 4. Joice, C.S, et al, (2013) Digital Control
Strategy for Four Quadrant Operation of Three Phase BLDC Motor WithLoad Variations. IEEE Transactions on Industrial Informatics, 9, 2, 974- 982.
5. Awaze, S.K, (2013) Four Quadrant Operation of BLDC Motor in
MATLAB/SIMULINK, 5th
InternationalConference on Computational Intelligence and Communication Networks (CICN), 569 – 573.
6. Ong, C.M., (1998) Dynamic simulation of
electric machinery using
MATLAB/SIMULINK, 1st edn, Prentice hallPTR, New Jersey.
7. Rong-Jong, et al, (2004) intelligent optimal control of single-link flexible robot arm, IEEE Transactions onIndustrial Electronics, 201- 220.
8. Hernandez-Guzman, et al, (2008) PID control of rigid robots actuated by brushless DC motors, American ControlConference, 1430- 1435.
9. Soylemez, M.T, et al, (2003) Position control of a single-link robot-arm using a multi- loop PI controller, IEEEConference on Control Applications, 2, 2, 1001 – 1006.
10. Manikandan, R, et al, (2014) Position control of DC servo drive using fuzzy logic controller, InternationalConference on Advances in Electrical Engineering (ICAEE), 1- 5.
