DC Motor Position: System Modeling


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1 1 of 7 01/03/ :07 Tips Effects TIPS ABOUT BASICS INDEX NEXT INTRODUCTION CRUISE CONTROL MOTOR SPEED MOTOR POSITION SUSPENSION INVERTED PENDULUM SYSTEM MODELING ANALYSIS DC Motor Position: System Modeling Key MATLAB commands used in this tutorial are: tf, ss CONTROL PID ROOT LOCUS FREQUENCY STATESPACE Contents Physical setup System equations Design requirements MATLAB representation DIGITAL Physical setup SIMULINK MODELING A common actuator in control systems is the DC motor. It directly provides rotary motion and, coupled with wheels or drums and cables, can provide translational motion. The electric equivalent circuit of the armature and the freebody diagram of the rotor are shown in the following figure. CONTROL
2 2 of 7 01/03/ :07 For this example, we will assume the following values for the physical parameters. These values were derived by experiment from an actual motor in Carnegie Mellon's undergraduate controls lab. (J) moment of inertia of the rotor E6 kg.m^2 (b) motor viscous friction constant E6 N.m.s (Kb) electromotive force constant V/rad/sec (Kt) motor torque constant N.m/Amp (R) electric resistance 4 Ohm (L) electric inductance 2.75E6H In this example, we assume that the input of the system is the voltage source (V) applied to the motor's armature, while the output is the position of the shaft (theta). The rotor and shaft are assumed to be rigid. We further assume a viscous friction model, that is, the friction torque is proportional to shaft angular velocity. System equations In general, the torque generated by a DC motor is proportional to the armature current and the strength of the magnetic field. In this
3 3 of 7 01/03/ :07 example we will assume that the magnetic field is constant and, therefore, that the motor torque is proportional to only the armature current i by a constant factor Kt as shown in the equation below. This is referred to as an armaturecontrolled motor. The back emf, e, is proportional to the angular velocity of the shaft by a constant factor Kb. (1) (2) In SI units, the motor torque and back emf constants are equal, that is, Kt = Ke; therefore, we will use K to represent both the motor torque constant and the back emf constant. From the figure above, we can derive the following governing equations based on Newton's 2nd law and Kirchhoff's voltage law. (3) (4) 1. Transfer Function Applying the Laplace transform, the above modeling equations can be expressed in terms of the Laplace variable s. (5) (6) We arrive at the following openloop transfer function by eliminating I(s) between the two above equations, where the rotational speed is considered the output and the armature voltage is considered the input. (7) However, during this example we will be looking at the position as the output. We can obtain the position by integrating the speed, therefore, we just need to divide the above transfer function by s.
4 4 of 7 01/03/ :07 (8) 2. StateSpace The differential equations from above can also be expressed in statespace form by choosing the motor position, motor speed and armature current as the state variables. Again the armature voltage is treated as the input and the rotational position is chosen as the output. (9) (10) Design requirements We will want to be able to position the motor very precisely, thus the steadystate error of the motor position should be zero when given a commanded position. We will also want the steadystate error due to a constant disturbance to be zero as well. The other performance requirement is that the motor reaches its final position very quickly without excessive overshoot. In this case, we want the system to have a settling time of 40 ms and an overshoot smaller than 16%. If we simulate the reference input by a unit step input, then the motor position output should have: Settling time less than 40 milliseconds Overshoot less than 16% No steadystate error, even in the presence of a step disturbance input MATLAB representation
5 5 of 7 01/03/ :07 1. Transfer Function We can represent the above openloop transfer function of the motor in MATLAB by defining the parameters and transfer function as follows. Running this code in the command window produces the output shown below. J = E6; b = E6; K = ; R = 4; L = 2.75E6; s = tf('s'); P_motor = K/(s*((J*s+b)*(L*s+R)+K^2)) P_motor = e12 s^ e05 s^ s Continuoustime transfer function. 2. State Space We can also represent the system using the statespace equations. The following additional MATLAB commands create a statespace model of the motor and produce the output shown below when run in the MATLAB command window. A = [ b/j K/J
6 6 of 7 01/03/ :07 0 K/L R/L]; B = [0 ; 0 ; 1/L]; C = [1 0 0]; D = [0]; motor_ss = ss(a,b,c,d) motor_ss = a = x1 x2 x3 x x x e+06 b = u1 x1 0 x2 0 x e+05 c = x1 x2 x3 y d = u1
7 7 of 7 01/03/ :07 y1 0 Continuoustime statespace model. The above statespace model can also be generated by converting your existing transfer function model into statespace form. This is again accomplished with the ss command as shown below. motor_ss = ss(p_motor); Published with MATLAB 7.14 Copyright 2012 All rights reserved. No part of this publication may be reproduced or transmitted without the express written consent of the authors.
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