08-08-2012, 04:02 PM
DC Position Control System – Determination of Parameters and Significance on System Dynamics
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INTRODUCTION
Identification of parameters of any physical system plays
a vital role to choose the parameters of controllers
appropriately. This is essential to make sure that the system
controlled satisfies the desired performance specifications.
Over the years, a great deal of research has been carried out
in the estimation of parameters of systems using genetic
algorithms, fuzzy logic and neural networks. Inertia and
Friction coefficient of motor alone were determined but that
of load were not considered even though optimization,
adaptive control and artificial intelligent techniques were
used [1]-[5]. The importance of estimation of load parameters
was emphasized in [6] but strategies for estimating inertia
and friction of load were not highlighted. Even in precise
applications such as position control, viscous friction of
motor was estimated [7] but that of load was not at all taken
into consideration. In [8], load model parameters were
obtained using genetic algorithm but friction coefficient of
motor was not at all considered.
DETERMINATION OF PARAMETERS
A. Importance of estimation of dynamic parameters
Parameters of the DC servomotor such as torque constant
KT, back emf constant Kb, armature resistance Ra, armature
inductance La, moment of inertia of motor and load J, friction
coefficient of the motor and load B have to be estimated
properly so that controller parameters can be properly tuned
and the desired response can be achieved from the DC
position control system. KT, Kb, Ra and La do not vary with
load and hence these values are determined using
conventional method. However, J and B vary with respect to
load as per the details given in the subsections C and D.
Hence, their variations will have an effect on the dynamics of
the system.
CONCLUSIONS
Proposed method can be used for estimation of moment
of inertia and friction of DC motor and load under dynamic
load variations. From the illustrative studies made on DC
servo motor, it is found that inertia and friction of motor and
load can be accurately determined using the proposed
method. From the study of effect of load on the performance
of DC position control system, it is found that these
parameters have to be determined for any change in load and
controller parameters have to be tuned accordingly.
This method can be extended to on-line parameter
estimation of inertia and friction of DC motors with any type
of load arrangement. There is no need to have information
about inertia and friction well in advance. Further, controller
parameters can be tuned from estimated parameters of inertia
and friction of motor and load by employing artificial
intelligent techniques. This will improve the response of the
system in real time whenever there is a change in load.
This method can be also extended to on-line parameter
estimation of inertia and friction of induction and synchronous
motors with any type of load arrangement, if torque equation
of DC motor is replaced by that of induction or synchronous
motor.