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Intelligent Controller for Networked DC Motor Control

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					                                                              (IJCSIS) International Journal of Computer Science and Information Security,
                                                                                                                        Vol. 8, No.8, 2010

        Intelligent Controller for Networked DC Motor
                             Control
                        B.Sharmila                                                               N.Devarajan
Department of EIE, Sri Ramakrishna Engineering College                         Department of EEE, Government College of Tech.
                   Coimbatore, India                                                          Coimbatore, India
               sharmi.rajesh@gmail.com


Abstract—This paper focuses on the feasibility of Neural Network           The novelty of this paper lies in comparison of the application
controller for Networked Control Systems. The Intelligent                  of NARMA-L2 Controller and Mamdani Fuzzy Logic
Controllers has been developed for controlling the speed of the            Controller with conventional PID controller for the
Networked DC Motor by exploiting the features of Neural                    improvement of the performance of networked control DC
Networks and Fuzzy Logic Controllers. The major challenges in
                                                                           motor.
Networked Control Systems are the network induced delays and
data packet losses in the closed loop. These challenges degrade                There are two approaches to utilize a data network as
the performance and destabilize the systems. The aim of the                Hierarchical Structure and Direct Structure as shown in Fig. 1
proposed Neural Network Controller and Fuzzy Logic Controller              and Fig. 2 respectively. In the hierarchical structure the dc
schemes improve the performance of the networked DC motor                  motor is controlled by its remote controller at remote station
and also compare the results with the Zeigler-Nichols tuned                whereas in direct structure the central controller is used for
Proportional-Integral-Derivative Controller. The performance of            controlling the speed of dc motor. Since the hierarchical
the proposed controllers has been verified through simulation              structure has a poor interaction between central and remote
using MATLAB/SIMULINK package. The effective results show                  unit, direct structure is preferred.
that the performance of networked dc motor is improved by
                                                                               Recently the stability analysis and control design for NCS
using Intelligent Controller than the other controllers.
                                                                           have attracted considerable research interest [3], [4], [6] and
   Keywords- Networked Control Systems (NCS); Network                      [11]. The work of Nesic and Teel [2] presents an approach for
Challenges; Tuning; Proportional – Integral - Derivative                   stability analysis of NCS that decouples the scheduling
Controllers (PID); Fuzzy Logic Controller (FLC); Artificial Neural         protocol from properties of network free nominal closed-loop
Networks (ANN).                                                            system. Nesic and Tabbara [3] extended [2] by stochastic
                                                                           deterministic protocols in the presence of random packet
                       I.    INTRODUCTION                                  dropouts and inter transmission time and they also proposed
                                                                           wireless scheduling protocol for non-linear NCS in [6]. The
    Networked Control System is the adaptation of
communication network for information exchange between                     networked predictive control scheme for forward and feedback
controllers, sensors and actuators to realize a closed control             channels having random network delay was proposed in [4],
                                                                           and [5] addresses the problems of how uncertain delays are
loop. Networks reduce the complexity in wiring connections
                                                                           smaller than one sampling period which affects the stability of
and the costs of Medias. They are easy to maintain and also
                                                                           the NCS and how these delays interact with maximum
enable remote data transfer and data exchanges among users.
                                                                           allowable transfer interval and the selected sampling period.
Because of these benefits, many industries and institutions has
shown interest in applying different types of networks for their           Robust feedback controller design for NCS with uncertainty in
remote industrial control and automation. Regardless of the                the system model and the network induced delay has been
                                                                           addressed in [7]-[8], whereas [9] handles networked induction
types of networks, the overall performance of NCS is affected
                                                                           motor speed control by using linear matrix n equality (LMI)
by two major challenges as networked induced delay and data
                                                                           method. Ref. [1] measure the networked vehicle control
losses. The challenges of networked DC motor are generally
                                                                           performance using an H infinity norm with linear matrix
controlled by Conventional Proportional – Integral -
Derivative Controllers, since they are less expensive with                 inequalities conditions and markovian jumping parameters in
inexpensive maintenance, designed easily, and very effective.              communication losses. In case of time varying transmission
                                                                           times, model based NCSs has been proposed for stabilization
But mathematical model of the controller and tuning of PID
                                                                           problem of NCS. The stability analysis and controller
parameters are difficult and generally not used for non-linear
                                                                           synthesis problems are investigated in [11] for the NCSs with
systems. Hence to overcome these challenges auto-tuning and
                                                                           random packet losses by using H infinity control and linear
adaptive PID Controller was developed with few mathematical
calculations. The Intelligent controllers as Fuzzy Logic                   matrix inequalities. A moving horizon method was developed
                                                                           by [12], which was applied as a quantized NCS in a practical
Controller and Artificial Neural Networks were used to
                                                                           context. Since these methods transmit data specifying only a
overcoming the challenges. Thus this paper proposes
                                                                           region in which the measurements lie, it will reduce the
Intelligent Controller for the compensation of the challenges.



                                                                     131                              http://sites.google.com/site/ijcsis/
                                                                                                      ISSN 1947-5500
                                                             (IJCSIS) International Journal of Computer Science and Information Security,
                                                                                                                       Vol. 8, No.8, 2010
network stabilization of the NCS. However, this method could
reduce the stability of the control system by introducing
uncertainty in the control system. The issues of limited
bandwidth, time delay and data dropouts was taken into
consideration when NCSs controllers were designed in [12] –
[14]. The networked control system performance depends on
the control algorithm and the network conditions. Several
network conditions such as bandwidth, end-to-end delay, and
packet loss rate are major impacts on networked control
systems. Depending upon the control algorithm and network
conditions the overall performance of the networked system
may vary and hence the stability of the system.
                         II.    MODELLING
                                                                                  Figure 3. An overall real-time networked control system.
     A networked control system can be divided into the
remote unit, the central controller and the data network. Fig. 3          where u=ea is the armature winding input voltage; eb =Kbω is
shows the general block diagram of the networked control                  the back-electromotive-force (EMF) voltage; L is the armature
system under investigation. In order to focus our discussion on           winding inductance; ia is the armature winding current; R is
the performance of networked closed loop control system with              the armature winding resistance; Kb is the back-EMF constant
network conditions (delay, data loss), a networked dc motor               and ω is the rotor angular speed. Based on Newton’s law the
control system has been illustrated.                                      mechanical-torque balance equation is
A. Remote Unit                                                                                  dω
                                                                                            J      + Bω + Tl = Kia
    The Remote Unit consists of the plant (dc motor), sensor                                    dt                                     (2)
and an interfacing unit. Via the network the remote unit can              J is the system moment of inertia; B is the system damping
send measurements like motor speed, current, temperature,                 coefficient; K is the torque constant and Tl is the load torque.
and local environment information, back to the central                    By letting x1 = ia and x2 = ω, the electromechanical dynamics
controller. The electro-mechanical dynamics of the dc motor               of the dc motor can be described by the following state-space
can be described by the loop equation as first order differential         description:
equations.                                                                          •             R     K       1
                               di                                                  x1 (t ) = −      x1 − b x 2 + u
                u (t ) = ea = L a + Ria + eb                                                      L      L      L
                               dt                            (1)                                                                               (3)
                                                                                    •        K    B     1
                                                                                   x 2 (t ) = x1 − x 2 + Tl
                                                                                             J    J     L
                                                                                                                                 (4)
                                                                          The parameters of the motor Table 1 are used for determine
                                                                          the state space model of dc motor.
                                                                                            TABLE 1. DC MOTOR PARAMETERS
                                                                             J          Moment of Inertia            42.6 e-6 Kg-m2
                                                                             L          Inductance                   170 e-3 H
                   Figure 1. Hierarchical Structure.                         R          Resistance                   4.67 Ω
                                                                             B          Damping Coefficient          47.8 e-6 Nm-sec/rad
                                                                             K          Torque Constant              14.7 e-3 Nm/A
                                                                             Kb         Back EMF constant            14.7 e-3 Vsec/rad

                                                                          B. Central Controller
                                                                              The central controller will provide the control signal uC(t)
                                                                          to the remote systems. The central controller will monitor the
                                                                          network conditions of the remote unit link and provide
                                                                          appropriate control signals to each remote unit. Similarly the
                                                                          output responses are taken as feedback signal yR(t) to the
                                                                          central controller. The proposed Intelligent Controllers will
                                                                          compensate the network-induced delays, data losses and
                                                                          external disturbances. The data losses and disturbances occur
                                                                          due to missing or disturbances in input reference signal, control
                                                                          signal and feedback signal.
                        Figure 2. Direct Structure.




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                                                                                                        ISSN 1947-5500
                                                              (IJCSIS) International Journal of Computer Science and Information Security,
                                                                                                                        Vol. 8, No.8, 2010
C. Data Network
    There are different ways to define network conditions for
point-to-point (from the central control to a specific remote
unit). Two of the most popular network measures are the
point-to-point network throughput and maximal delay bound
of the largest data. One factor of interest is the sampling time.
    To keep the illustration simple, the remote unit receives the
data sent from the central controller as uR(t), which can be
mathematically expressed as
                  uR (t) = uc (t −τ R )                        (5)
where τR is the time delay to transmit the control signal uC(t)                          Figure 4. Neural Network Controller for NCS.
from the central controller to the remote unit. The remote unit
also sends the sensors signals yR(t) of the remote system back                               TABLE 2. ANN PLANT SPECIFICATION
to the central controller yC(t), and these two signals are related
                                                                            No. of Inputs                             3
as
                                                                            No. of Outputs                            2
             y C (t ) = y R (t − τ C )                         (6)          No. of Hidden Layers                      2
                                                                            No. of Training Samples                   1000
where τC is the time delay to transmit the measured signal                  No. of Training Epochs                    200
from the remote unit to the central controller. There are also
processing delays as τPC and τPR, at the central and remote unit,              The error signals are trained for number of epochs by using
respectively which could be approximate small constants or                 the NARMA-L2 controller and the control signal are generated
even neglected because these delays are usually small                      for any challenges in the network.
compared to τC and τR.
    The functions of network variables such as the network                 B. Fuzzy Logic controller
throughput, the network management/policy used, the type
                                                                               In general, fuzzy logic control is used for the control of a
and number of signals to be transmitted, the network protocol              plant where the plant modeling is difficult. For such systems
used, and the controller processing time, and the network                  that are difficult to model, fuzzy logic controller has been
traffic congestion condition are taken as the current network              successful by Mamdani. The basic principle of fuzzy logic lies
conditions n(t) and let z-t be a time delay operator which                 in the definition of a set where any element can belong to a set
defines the signals as                                                     with a certain degree of membership. Using this idea, the
              u R (t ) = u c ( z − t R , n(t ))                            knowledge of an expert can be expressed in a relatively simple
                                                              (7)
                                   −t c                                    form and the inference for given inputs can be implemented
              y c (t ) = y R ( z          , n(t ))                         very efficiently. Due to these advantages, fuzzy logic control
                                                          (8)
   In this paper, we have chosen sampling time as 0.5 ms and               is an attractive method for NCS whose modeling is very
simulations are done.                                                      difficult because of the stochastic and discrete nature of the
                                                                           network. Fig. 5 shows the structure of FLC for a single input
       III.    MODELLING CONTROLLER DESIGN FOR NCS                         single output plant. In Fig. 5 r(t) is the reference input, y(t) is
                                                                           the plant output, e(t) is the error signal between the reference
   In this session the proposed Neural Network Controller and              input and plant output and uC(t) is the control signal.
Fuzzy Logic Controller as the central controller is described                  The FLC consists of three parts as 1) Fuzzifier that
and the results are compared with the PID controller.
                                                                           converts the error signal into linguistic values, 2) Inference
                                                                           engine that creates the fuzzy output using fuzzy control rules
A. Neural Network Controller                                               generated from expert experience and 3) Defuzzifier that
    The proposed scheme utilizes the neural-network                        calculate the control input to the plant from the inferred
NARMA-L2 Controller. The Neural Network Controller is                      results. The input and output signals to the FLC are error
designed to take the error as the input and computes the output            signal e(t) and control signal uC(t) respectively. In this paper,
stabilizing signal depending on the input error signal. The                the trapezoidal fuzzy members are selected for membership
block diagram of Neural Network Controller for NCS is                      functions. Three fuzzy linguistic variables, i.e., Small,
shown in Fig. 4.                                                           Medium and Large are defined. The coefficients of the
    The NARMA-L2 controller, a multilayer neural network                   membership function depend upon the set point and are
has two steps involved as system identification stage and                  determined by several trial and error experiments with the
control design stage. In system identification stage, a neural             plant without the network. In order for faster execution of the
network model of the plant which has to be controlled is                   fuzzy logic controller, the Mamdani’s min-max inference
developed and in later stage the neural network plant model                method and the central average defuzzifier are used.
has been designed to train the controller. The ANN plant                       The rules used in this paper are as
specification has been shown in Table 2.                                       If e(t) is small then uC(t) is small



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                                                                                                         ISSN 1947-5500
                                                                     (IJCSIS) International Journal of Computer Science and Information Security,
                                                                                                                               Vol. 8, No.8, 2010
   If e(t) is medium then uC(t) is medium                                                                                        IV.       SIMULATION SETUP AND RESULTS
   If e(t) is large then uC(t) is large                                            In the simulation scenario, the direct structure of the
C. PID Controller                                                              networked DC motor control system is simulated using
                                                                               MATLAB/ SIMULINK under fully controlled environments
   It is used to compute the control signal to the remote dc
                                                                               for Neural Network Controller, Fuzzy Logic Controller and
motor for step tracking, based on the monitored system signals
                                                                               PID Controller. Equations (3) - (4) are used as the main
sent from the remote unit via the network link as in Fig. 6. The
Proportional-Integral-Derivative (PID) controller used is                      model, and it is controlled by the controller with the insertions
                                  t
                                                                               of network delays according to (5) - (6). The delays are varied
                                                    de(t )                     according to different effects of interests. The disturbance and
   U PID (t ) = K p e(t ) + K I ∫ e(t )dt + K D                                loss of input signal, control signal and the feedback signal
                                  0
                                                     dt
                                                                (9)            were made for few milliseconds at each stage and the results
where KP is the proportional gain; KI is the integral gain; KD is              were studied. The system setup is illustrated in Fig.4, Fig.5
the derivative gain; r(t) is the reference signal for the system to            and Fig.6. Using (3)-(4) and Table 1, the state model of the dc
track; y(t) is the system output; and e(t) is the error function.              motor is obtained. Then the results of the ANN and FLC are
In our case, y = ω is the motor speed, and UPID(t) is the input                compared with the PID controller.
voltage to the motor system.                                                       Output Responses of the system are obtained for all
    The results of model system with ANN, FLC and PID                          controllers used in this paper. Fig. 7 shows the comparison of
Controllers for network induced delays, losses and disturbance                 the system performance for all controllers without delays and
are simulated and compared.                                                    data losses.
                                                                                   Fig. 8 - 10 shows the response of the system for the
                                                                               controllers with different network induced delays and the
                                                                               comparison of these performances are tabulated in Table 3. The
                                                                               system responses with delay and data losses are obtained as in
                                                                               Fig. 11. From the simulation results as in Fig. 7 - 11, the
                                                                               overall system performance with Intelligent Controllers as
                                                                               ANN and FLC are better than the PID controller.
                                                                                                                                                           Output Response
                                                                                                      2000


                                                                                                      1800


                                                                                                      1600
               Figure 5. Fuzzy Logic Controller for NCS.
                                                                                                      1400
                                                                                M o to r S p e e d (rp m )




                                                                                                      1200                                                                        Fuzzy Logic Controller; ts = 7 ms; Mp = 3%
                                                                                                                                                                                  PID Controller; ts = 10 ms; Mp = 23%
                                                                                                      1000                                                                        Set Point = 1500 rpm
                                                                                                                                                                                  Neural Network Controller; ts = 7 ms; Mp = 3 %
                                                                                                             800


                                                                                                             600


                                                                                                             400


                                                                                                             200


                                                                                                              0
                                                                                                                   0         2         4     6         8         10          12          14              16              18        20
                                                                                                                                                              Time (ms)

                                                                                                                       Figure 7. Comparison of System Responses for ANN, FLC and PID
                Figure 6. ZN Tuned PID Controller for NCS
                                                                                                                                     Controller without delay and losses.

       TABLE 3. COMPARISON OF PERFORMANCE OF THE NETWORKED DC MOTOR CONTROL SYSTEM WITH DELAY IN ANN, FLC AND PID CONTROLLER.
                                              (Set point = 1500 rpm; Sampling Time = 0.5ms)

                  Time delay (ms)                                Maximum overshoot (%)                                                                        Settling Time (ms)
      Feedforward path        Feedback path                PID         FLC            ANN                                                        PID                 FLC                                ANN
             0.5                    1                      3.3          3.3            3                                                          30                   7                                 7
              1                     1                      3.3          3.3            3                                                          40                   8                                 7
              2                     2                      6.6          3.3            3                                                          62                   9                                 8
              2                     3                       8           3.3            3                                                          70                   9                                 8
              3                     2                       9           3.3            3                                                          75                  10                                 9




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                                                                                                                                                   ISSN 1947-5500
                                                                                         (IJCSIS) International Journal of Computer Science and Information Security,
                                                                                                                                                   Vol. 8, No.8, 2010
                                                                                         Output Response
                             1800


                             1600


                             1400

                                                                                                                                    Set point = 1500 rpm
                             1200
                                                                                                                                    FF: 0.5ms; FB: 1ms
M to S e d (rp )
              m




                                                                                                                                    FF:1 ms; FB:1ms
                             1000                                                                                                   FF:2ms; FB:1ms
 o r pe




                                                                                                                              FF: Feedforward Delay
                             800                                                                                              FB: Feedback Delay


                             600


                             400


                             200


                                0
                                        0           5         10         15         20         25           30           35           40           45          50
                                                                                            Time (ms)


                              Figure 8. Response of the System using PID Controller with varying delays in forward and feedback path of NCS.

                                                                                         Output Response
                             1600


                             1400


                             1200


                             1000
M to S e d (rp )
              m




                                                                                                                   FF: 0.5ms; FB: 1ms; Mp 3.3%:ts: 7 ms
 o r pe




                                                                                                                   Sep Point: 1500 rpm
                             800
                                                                                                                   FF:1 ms; FB:1ms;Mp: 3.3%;ts: 8 ms
                                                                                                                   FF:2 ms; FB:1ms; Mp:3.3%; ts: 9 ms
                             600
                                                                                                              FF: FeedForward Delay
                                                                                                              FB: FeedBack Delay
                             400


                             200


                                0
                                        0           5         10         15         20         25           30           35           40           45          50
                                                                                            Time (ms)


                                            Figure 9. Response of the System using FLC with varying delays in forward and feedback path of NCS.



                                                                                         Output Response

                             1600


                             1400
         Motor Speed (rpm)




                             1200


                             1000

                                                                                                             Set Point=1500 rpm
                              800                                                                            FF = 0.5 ms; FB = 1 ms; Mp = 3%; ts = 7 ms
                                                                                                             FF = 1 ms; FB = 1 ms; Mp = 3%; ts = 7 ms
                              600                                                                            FF = 2 ms; FB = 1 ms; Mp = 3%; ts = 8 ms

                                                                                                        Feedforward Delay = FF
                              400
                                                                                                        Feedback Delay = FB

                              200


                               0
                                    0              5          10         15         20             25         30           35              40             45        50
                                                                                            Time (ms)
                               Figure 10. Output Response of the System using ANN with varying delays in forward and feedback path of NCS.




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                                                                                                                                        ISSN 1947-5500
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                                                                                                      Output Response
                                      2500

                                                                                                                                                          Set Point = 1500 rpm
                                                                                                                                                          PID Controller
                                                                                                                                                          Neural Network Controller
                                      2000
                                                                                                                                                          Fuzzy Logic Controller
            M o to r S p eed (rpm )




                                      1500




                                                        Feedforward = Feedback Delay = 2 ms
                                      1000




                                       500
                                                                                    Loss of Input Signal


                                        0
                                             0                                     50                                                 100                                             150
                                                                                                           Time (ms)
                                                  Figure 11. Comparison of system responses of ANN, FLC and PID Controllers with delay and losses.

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by using Intelligent Controller than the convention controller in                                                [13]   K.Li, and J.Baillieul, “Robust quantization for digital finite
all network variations and deteriorations. The analysis on using                                                        communication bandwidth (DFCB) control,” IEEE Trans. Autom.
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                                                                                                                                             AUTHORS PROFILE
[6]   M.Tabbara, C. Nesic, and A.Teel, “Stability of wireless and wireline
      networked control systems,” IEEE Trans. Autom. Control, vol. 52, pp.                                       Dr.N.Devarajan received B.E (EEE) and M.E (Power Electronics) from GCT
      1615-1630, September 2007.                                                                                 Coimbatore in the year 1982 and 1989 respectively. He received Ph.D in the
                                                                                                                 area of control systems in the year 2000. He is currently working as Assistant
[7]   D. Yue, Q. Han, and P. Chen, “State feedback controller design of
                                                                                                                 Professor in the department of EEE at Government College of Technology,
      networked control systems,” IEEE Trans. Circuits Systems II, vol. 51,
                                                                                                                 Coimbatore. He published 135 papers in the national and international
      pp. 640-644, November 2004.




                                                                                                           136                                      http://sites.google.com/site/ijcsis/
                                                                                                                                                    ISSN 1947-5500
                                                                          (IJCSIS) International Journal of Computer Science and Information Security,
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conferences. He published 37 papers in international journals and 12 in                 from Maharaja College of Engineering, Coimbatore in the year 2004. She is
national journal. Under his supervision currently 10 research scholars are              currently working as Senior Lecturer in the department of EIE at Sri
working and 7 scholars completed their Ph.D. His areas of interests are control         Ramakrishna Engineering College, Coimbatore. She is a Ph.D. research
systems, electrical machines and power systems. He is a member of system                scholar and published 2 papers in international journals and also presented 4
society of India, ISTE and IE(India).                                                   papers in national and international conference. Her areas of interests are
                                                                                        networked control system and intelligent controllers. She is a member of IEEE
B.Sharmila completed B.E (EIE) from Tamilnadu College of Engineering,                   and                                                                     ISTE.
Coimbatore in the year 2000. She completed her M.E (Applied Electronics)




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