# Digital Implementation of induction motor Drives with DSP Based Control System.docx paper digital implementation of induction motor drives with dsp based control system

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```					  Speed Estimation of induction motor Drives with DSP Based Control System

Solly Aryza, Zulkeflee Khalidin, Ahmed N Abdalla, Zulkarnain Lubis
Faculty Electrical Engineering, University Malaysia Pahang, pekan26600, Malaysia

Abstract-- Digital signal processors (DSP's) and neural            important, when a slip-dependent parameter model is used
network is becoming more popular in the area of ac and dc          [11].
motor control, in particular, the induction motor drives. In             In this case, the model coefficients of equations (1) are
this paper, adaptive speed control of induction motor using        difficult about impossible to be evaluated without the use of
neural network inverse control scheme are proposed. The            an estimation procedure [4].
controller-structure design is based on a vector control                 When the rotor circuit parameters are to be estimated,
scheme that transforms the three phase motor currents into         assuming that the stator and core parameters are known, the
flux and torque generating current components. The                 slip-torque characteristic of the motor provides adequate
experiment results show that the proposed scheme has               information. However, when all the equivalent circuit
excellent dynamic and static control performance.                  parameters are to be estimated (6) in total if constant rotor
parameters are assumed and (9) if slip-dependent rotor
Keywords- Speed Control, Digital Signal                            parameters are used, the slip-torque characteristic itself is
not enough and some additional information is
Processing, neural network, Induction Motor
necessary[12]. Such information can be provided using, for
example, the slip current characteristic of the motor along
I. INTRODUCTION                                  with the slip-torque curve. In this paper, the estimation
The induction motor is a multi-variable, nonlinear,            problem will be formulated in its general form, assuming
strong coupled system. Its rotor parameters change very            that all the equivalent circuit parameters are to be estimated
prominently with the time-varying conditions. All                  [8]. The parameters' estimation problem can be formulated
unmolded dynamic effects, such as the change of load               as a least square optimization problem, the objective being
torque, the existence of disturbance and magnetic saturation       the minimization of the deviation between the measured
make the differential geometry and inverse system method           torque and current curves and the model generated curves.
difficult to be applied in practice [7-10].                        These curves are known as a set of discrete measurement
Control method to tackle nonlinear system with uncertain           points [11-13].
factor. In order to enhance the dynamic response
performance of the induction motor, the differential
=-                            +                    (1)
geometry and the inverse system decoupling control
methods are investigated. However, the decoupling and              +
linearization of a multivariate nonlinear system demand
exact mathematics model of a controlled objects [1-2].
=                      -                           (2)
Much researchers work in this field but less used dsp
for media controlling. The proposed algorithm is applied to
the described induction motor models and estimates the                  These equations are used in the estimation procedure
model rotor parameters using the slip-torque motor                 because the motor curves and thus the measurement data
characteristic. First, describes the overview of induction         are obtained under nominal operating conditions. Therefore
motor parameter estimation algorithm that is based on              the estimation is performed under nominal conditions.
numerical solution techniques [15]. The algorithm is                                                             d
implemented based on the state of the art nonlinear least          Vsd (t )  Rs .isd (t )  n p m (t ).sq       sd       (3)
squares numerical solution techniques. For the estimation of
dt
d
all the motor equivalent circuit parameters the slip-torque        Vsq (t )  Rs .isq (t )  n p m (t ).sd     sq         (4)
characteristic alone is not enough and the slip-current                                                        dt
characteristic or the slip-power factor characteristic can be                                                      d
additionally used, to provide extra information. Such              Vrd (t )  0  Rr .ird (t )  n p m (t ).rq  rsd       (5)
characteristics can be most of the times obtained by the                                                           dt
manufacturers. Here the characteristics are assumed to be                                                            d
known as a number of discrete points [14].                         Vrq (t )  0  Rr .irq (t )  n p m (t ).rd       rq    (6)
dt
II. MOTOR PARAMETER ESTIMATION                              Vrd (t ) , Vrq (t ) = 0                                     (7)
The estimation methodology makes use of data that, in
general, can become available from the motor manufacturer,             These equations are used in the estimation procedure
or are easily measured, like the slip-torque characteristic, or    because the motor curves and thus the measurement data ar
the slip-current or slip-power factor characteristics [3],[5]. A        Obtained under nominal operating condition.
parameter estimation procedure becomes even more                   Therefore the estimation is performed under nominal
conditions A pseudo-linear composite system can be gotten
by cascading the inverse system before the original system          programming of new structures on the base of recently
[8]. And it was equivalent to two second-order integral             debugged and tested macroinstructions.
linear subsystems, so that system control of induction                   The programs are written in the form of macro
motor which is complex multi-variable and strong coupling           instructions. This method permits fast and well-arranged
was transformed into two second-order integral linear               programming of new structures on the base of recently
subsystems control, a fully dynamic decoupling was                  debugged and tested Macroinstructions.
achieved between flux and speed of induction motor [9-10].
sd  Ls .isd (t )  Lm .ird (t )                            (8)            IV.                   ADAPTIVE SPEED CONTROL OF
MOTOR INDUCTION
sq  Ls .isq (t )  Lm .irq (t )                            (9)

rd  Ls .ird (t )  Lm .isd (t )                            (10)         The adaptive speed controller is derived from the
mechanical model of system. The conversion of Eq.(2.5)
rq  Ls .irq (t )  Lm .isq (t )                            (11)   into its discrete form gives
Lm                                                                          .
:T      np      (rd .isq (t )  rq .isd (t ))             (12)
( J eq s  Bm ). m (t )  Tem (t )  TL (t )
em
Lr                                                                                                                                                        (16)
d                                                        (13)
J eq  m (t )  Tem (t )  Tb (t )  TL (t )
dt
Tem ( s)      n p .Lm (rd .isq ( s)  rq .isd ( s)) (17)
Tb (t )  Bm . m (t )                         (14)    m ( s)                           
( J eq s  Bm )            Lr .(J eq s  Bm )
d
 m (t )   m (t )                       (15)
dt                                                   Vsd (t )  (( n p . m (t ).rq 
Rr          L         L       R     d
rd ). m  ( m 2  s ).isd (t )  isd (t ). .Ls
(18)
III. HARDWARE STRUCTURE OF CONTROL                                                                      Lr        .Ls .Lr  .Ls .Lr  .Ls  dt
SYSTEM
The DSP-based control system of the three-phase                                     Lm                RL            L                                                  (19)
induction motor consists of three interconnected modules:           V sd ( s)  n p .        m ( s). rq  r 2m  rd  ( m  R s   .Ls .s).i sd ( s)
Lr                 Lr           Lr
the eZDSP board, 161/08 DSPLINK interface and a Pulse
Width Modulated (PWM) output circuit.
The overall system block diagram is shown in Fig.1.                                n p .Lm (rd .isq ( s)  rq .isd ( s))
TMF28335 DSP is used as the central processor of the                 m ( s)                     Lr .(J eqs  Bm )
control system and implements the corresponding control                                                                                    
Lm
Vsd ( s) n .  ( s).     Rr Lm             L
algorithms - vector control and direct torque control. DC                      p    m    rq         2
rd  ( m2  Rs   .Ls .s).isd ( s)
link voltage Ed, the stator currents ias, ibs, i,, ih , and the                  Lr              Lr             Lr
speed w, are sampled and transmitted to the DSP through                                                    n p .Lm (rd .isq (s)  rq .isd (s))
161/08 DSPLINK interface. With the combination of these                                                                     Lr
Information and control methods, the required PWM gating                                                                                           2   2
Lm           RL                 n p Lm
signals are generated to drive the three-phase induction            J eq. .Ls .s 2 .isd (s)  (( Rs        ).i (s)  r 2m rd ) J eqs  2 (rd .isq (s)  rq .isd (s))
2 sd
motor                                                                                                    Lr            Lr                   Lr
 m (s)                  1.78             (20)
         3
Vsd ( s )   0.72 x10 s  0.0157 s  3.168
2

 m ( s)         2470
 2                                                                                           (21)
Vsd ( s )  s  21 .79 s  4400

2470 K ( K P  K D s )                                                  (22)
G ( s) 
s 2  (21 .79  2470 KK D ) s  6870  2470 KK P

Fig. 1: Overall hardware architecture of control system                                     435 .8 ( K P  K D s )                                                   (23)
G ( s) 
s 2  (21 .79  435 .8 K D ) s  6870  435 .8 K P
The TMS320F28335 DSP used is a 60MHz, 64-
bit floating point processor with two on-chip 64 bit timers,                                                                                           (26
an enhanced external memory interface, a two channel
DMA controller and a serial port. This arrangement offers a                                                              435 .8 K P
KV  lim sG ( s )                                                                                     (24)
versatile and powerful development tool for motor drives.                             s 0                                21 .79
A detailed discussion of the architecture and programming                       20 K P
of F28 is given in [14-151.
All mathematical operations of both processors are
Finally, the algorithm of controller computation task can be
preceded at an integer form to insure the high-speed
outlined as follows:
processing. The programs are written in the form of macro
1. Initialization: select a reference model.
instructions. This method permits fast and well-arranged
2.    Select the initial values of gain matrix K and
covariance matrix P
3.    Read the k" value of rotor speed obtained from
encoder.
4.    Compute the required control signal from the
updated plant parameters along together with the
information from the reference model.
5.    Record the control signal u(k)given to the power
module. Calculate and update the information
vector.
6.    Calculate and update the gain and covariance
matrices.
.
V.        Experimental System
Fig3. Inverter of connection
The hardware components used in this experiment
consist of Digital Signal Processor (DSP) TMS320F28335
dedicated motor controller that is equipped with analog
inputs, encoder inputs and PWM output channels. The
PWM output channels of the DSP id fed to a power module
the DSP are used to read the phase motor currents that are
required by the inner loop current controllers.
The software components consist of DSP
programming tools and motor control development
environment that can be used as Human Machine Interface
(HMI). Estimation and control signal computation are
implemented using Mat lab in the Development Tool
Environment. The program is compiled on DSP compiler
DSP board using eZDSP subsystem. In the implementation,                   Fig 4: Circuit of Stator Controlling
the value of measured Variables must be scaled and
formatted to fixed-point representation because of the use
fixed point type DSP.
Scaling for the speed signal is made according to the
properties of the motor encoder and DSP’s encoder input
ports, where the signal is represented in bits in which one
bit equivalent to 15 rpm. Under 2ms sampling time, the
speed error will be 2 bits such that the speed response of
the motor control system will have 2 bits of noise.

VI.      EXPERIMENTAL RESULT

In the experiment, the speed control of the induction
motor will be tested under conditions: step input set point,
set-point changes as well as disturbance changes. The
induction motor used in this experiment is 4poles, 60 Hz,
280V and 1.8A rating

Fig5. Circuit Diagram for controlling Induction Motor

Fig 2.Digital Signal Processor (DSP) TMS320F28335
Fig 10. Experimental Result of Torque Used DSP for
Controlling
Comparing the simulated responses (Fig.4 - Fig.6) and
measured responses (fig.7 - Fig.10), it can be stated that the
simulation gives relatively exact results. Some differences
at the current response or Control voltage response may be
caused for example by the actual properties of the real
Fig 6. Estimators Adaptive in controlling       sensors which are not of course ideal. Nonlinearity of the
current sensors and their dynamic behavior or no
concentricity of the increment sensor and motor may
appear. Generally it can be stated that the correspondence
between measured arid simulated responses is acceptable to
Carry out sufficiently exact analysis of the drive processes

VII.      CONCLUSION
The paper has demonstrated the implementation
of this high performance direct torque control technique
utilizing the floating point TI DSP, TMS320F28335. The
experimental results show that an excellent torque response
is achieved and agree with the theoretical and simulation
results. In the end, the system is implemented on Mat lab-
Fig 7. Experimental Result of current used DSP for              Simulation and experiment results show that the
Controlling                       system has good dynamic and static properties and
excellent characteristic of speed tracking, and the
decoupling effect between controlling Induction. For all
experimental (speed responses for step set point input,
increasing and decreasing set point changes as well as
disturbance changes), the adaptive speed control of
induction motor can give good tracking and disturbance
rejection performance, where it yields zero steady state
error and quick response time (most of responses have
response times less than 200 ms).

VIII.     REFERENCE
[1]. K. Kim, R. Omega, A. Cliarara, et al.. Theoretical and
experimentalcomparision of two nonlinear controllers for
current-fed induction motors, IEEE Trans. on Control
Systems Technology, 1997, 5(3):1-11
Fig 9. Experimental Result of Speed Used DSP for     [2]. R.MIarino, S.Peresada, P.Tomei. Global adaptive
Controlling                        output feedback control of induction motors will uncertain
rotor resistance, IEEE Trans on Automatic Control, 1999,
44(5):967-983
[3]. C.Kwan, F.Lewis. Robnst backstepping control of
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[6]. A.Ba-Razzonk, A.Cheriti, G..Olivier, et al.. Field-
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[9]. G. Liu, Y. Sun, Y. Shen, and etc. "Dynamic
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