Analysis on Separately Excited DC Motor Using Proportional Integral Derivative Controller

IJBSTR RESEARCH PAPER VOL 1 [ISSUE 7] JULY 2013

ISSN 2320 – 6020

Analysis on Separately Excited DC Motor Using Proportional Integral Derivative Controller Puneet Kumar and K. P. Singh ABSTRACT: In this project I have designed a separately excited DC motor whose speed can be controlled. PID controller has been designed and the system is simulated using MATLAB to analyze the performance. The parameters of PID controller are used to control the speed of the separately excited DC Motor. The study shows that precise characteristics of PID controller are used to improve the dynamic response of separately excited DC MOTOR. The simulation results demonstrate that the designed s PID controller realize a good dynamic behaviour of the DC motor, with less rise and settling time, minimum overshoot, minimum steady state error and give better performance.

KEYWORDS: Proportional Integral Derivative (PID)Controller, DC (Direct Current) Motor, Simulink MATLAB.

INTRODUCTION The development of high performance motor drives is very important in industrial as well as other purpose applications such as steel rolling mills, electric trains and robotics.

Generally, a high performance motor drive system must have good dynamic speed command and load regulating response to perform task. DC drives, because of their simplicity, ease of application, high reliabilities, flexibilities and favourable cost have long been a backbone of industrial applications, robot manipulators and home appliances where speed and position control of motor are required. DC drives are less complex with a single power conversion from AC to DC. Again the speed torque characteristics of DC motors are much more superior to that of AC motors.

The controllers of the speed that are conceived for goal to control the speed of DC motor to execute one variety of tasks, is of several conventional and numeric controller types, the controllers can be: proportional integral (PI), proportional integral derivative (PID) Fuzzy Logic Controller (FLC) or the combination between them: Fuzzy-Neural Networks, Fuzzy-Genetic Algorithm, Fuzzy-Ants Colony. The proportional – integral – derivative (PID) controller operates the majority of the control system in the world. .

Puneet Kumar and S. K. Singh Research Scholar and Associate Professor Department of Electrical Engineering M.M.M. Engineering College Gorakhpur 273010 (UP) India Email: puneet009.akg@gmail.com and kp1960@radiffmail.com

It has been reported that more than 95% of the controllers in the industrial process control applications are of PID type as no other controller match the simplicity, clear functionality, applicability and ease of use offered by the PID controller.

PID controllers provide robust and reliable performance for most systems if the PID parameters are tuned properly.

The major problems in applying a conventional control algorithm (PI, PD, PID) in a speed controller are the effects of non-linearity in a DC motor. The nonlinear characteristics of a DC motor such as saturation and fiction could degrade the performance of conventional controller’s .Generally, an accurate nonlinear model of an actual DC motor is difficult to find and parameter obtained from systems identification may be only approximated values.

Now days, Induction motors, Brushless DC motors and Synchronous motors have gained widespread use in electric traction system. Even then, there is a persistent effort towards making them behave like dc motors through innovative design and control techniques. Hence dc motors are always a good option for advanced control algorithm because the theory of DC motor speed control is extendable to other types of motors as well.

DC MOTOR A. Modelling of DC Motor In this circuit model a load is connected with armature mechanically and armature is supplied by terminal voltage. The term speed control stand for intentional speed variation carried out manually or automatically DC motors are most suitable for wide range speed control and are there for many adjustable speed drives.

In the modelling of DC motor the basic aim is to convert circuit model into transfer function model. This is achieved by taking the Laplace of circuit equations i.e. converting into time domain. Hence both the electrical and mechanical time constants are required.

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IJBSTR RESEARCH PAPER VOL 1 [ISSUE 7] JULY 2013

ISSN 2320 – 6020 B. Block Diagram

Figure 1: Separately excited DC motor model

Figure 2: Block Model of Separately Excited DC Moto

From figure 1: The armature voltage equation is given by:

The equation can be written as:

đ???đ??ˆđ??š

Va= Eb+ IaRa + La đ???đ??­ Now the torque balance equation will be given by: Tm= Jm

ω(s) / Va(s) = (1/Km)/((1 + STem)(1 + STa)

đ???đ?›š

+ B m ω + Tl đ???đ??­ Where, Tl is load torque in N-m.

Friction in rotor of motor is very small (can be neglected), so Bm= 0 Therefore, new torque balance equation will be given by:

Tem and Ta are the time constants of the above system transfer function which will determine the response of the system.

Hence the dc motor can be replaced by the transfer function obtained in above equation in the DC drive model.

C. Specifications of the dc motor:

đ???đ?›š

Tm = J m + Tl --------- (i) đ???đ??­ Taking field flux as ÎŚ and Back EMF Constant as K. Equation for back emf of motor will be: Eb = K ÎŚ ω Also,Tm = K ÎŚ Ia

--------- (ii)

--------- (iii)

Taking Laplace transform of the motor’s armature voltage equation we get Ia(S) = (Va – Eb) / (Ra + LaS) Now, taking equation (ii) into consideration, we have: Ia(s) = (Va – KΌω)/ Ra (1+ LS/Ra) And ω(s) = (Tm – Tl) /Js = (KΌIa – Tl) /JmS (Armature Time Constant)

Ta = La/Ra

Armature resistance (Ra) = 0.5Ί Armature inductance (La) = 0.02 H Armature voltage (Va) = 200 V Mechanical inertia (Jm) = 0.1 Kg.m2 Friction coefficient (Bm) = 0.008 N-m/rad/sec Back emf constant (K) = 1.25 V/rad/sec Rated speed = 1500 r.p.m Motor torque constant= .5 N-m METHODOLOGY Proportional Integral Derivatives (PID) Controller PID control is a control method used normally in industrial applications. PID involves three separate parameter, the proportional, the integral and derivatives. A PID and a PI controller are designed for buck converter for operation during a start-up transient and steady state, respectively. The derivative term in a controller is to noise and measurement error of the system, which may result in oscillation of the duty cycle during steady state [7].

Proportional term makes a change to the output that is proportional to the current error value. The contribution from

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IJBSTR RESEARCH PAPER VOL 1 [ISSUE 7] JULY 2013 integral term is proportional to both the magnitude of error and duration of error. The rate of change of the process error is calculated by determining the slope of the error over time and multiplying this rate of change by derivative gain Kd. By tuning the three constant in PID controller algorithm, the controller can provide control action designed for specific process requirement [8].

There are various methods are available for the tuning of PID controller. But for convenience purpose trial and error method is generally used.

ISSN 2320 – 6020 Simulink model of DC motor

Figure 5: Simulink Model of separately excited dc motor RESULTS Figure 3: A block diagram of PID controller MATLAB/SIMULINK

Figure 4: Simulink Model for Speed Control of Separately

Figure 6: Speed Vs time response of PID controlled DC

Excited DC Motor using PID controller

motor

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CONCLUSION

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In this project I have studied designing and modelling of separately excited DC motor. In the modelling of DC motor I have calculated the overall transfer function of motor. I have also studied the basics of PID controller and its response. PID controller has many advantages over the other controllers.

This project introduces modelling of DC motor with PID controller with use of MATLAB simulation. From the simulation result it is concluded that the dynamic response of DC motor improves by the use of PID controller.

By adjusting the PID gain parameters the system response can be change accordingly. I have also studied that there are various methods are available which can be used to give such type of performance like FUZZY LOGIC, NEURAL NETWORKS, ANN and etc.

FUTURE SCOPE

Figure 7: Rate of Change of speed Vs time response of PID controlled DC motor

MATLAB simulation for speed control of separately excited DC motor has been done which can be implemented in hardware to observe actual feasibility of the approach applied in this project. This technique can be extended to other types of motors. The parameters of PID controller can also be tuned by using genetic algorithm (GA), ANN, FUZZY LOGIC and etc.

REFERENCES

Figure 8: Change of speed Vs time response of PID controlled DC motor

1.

Bimbhra, P.S., Power Electronics. New Delhi, Khanna Publishers, 2006.

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Dubey, G.K., Fundamentals of Electrical Drives.

New Delhi, Narosa Publishing House, 2009.

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Mohan, Ned, Electrical Drives-An Integrated Approach. Minneapolis, MNPERE, 2003. [5] Ogata, K., Modern Control Engineering. Englewood Cliffs, NJ: Prentice Hall, 2001.

5.

K.H. Ang, G. Chong and Y. Li, “PID control system analysis, design and technology,” IEEE transaction on Control System Technology, Vol.13, No.4, 2005, pp. 559-576

6.

MATLAB and SIMULINK Version 2010a, the MathsworksInc, USA.

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http://en.wikipedia.org/wiki/PID_controller

8.

http://en.wikipedia.org/wiki/Modeling

9.

http://en.wikipedia.org/wiki/DC _machine

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10. C. K. Tse and K. M. Adams, “Quasi linear modeling and control of DC/DC converters,” IEEE Trans.

Power Electron., vol. 7, no. 3, pp.

11. P. R. K. Chetty. “Modeling and Analysis of CUK Converter Using Current Injected Equivalent Circuit Approach, ” IEEE Trans. Ind. Electron., I E - 3 0 , 1, pp. 56-59, (Feb. 1983).

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