Farid Arvani

IEEE NECEC Nov. 8, 2007 St. John's NL

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Abstract—The MS150 Modular Position Servo System is a

popular system used for study of the theory and practice of

automatic control systems. It can be controlled using an analog

PID type controller or a digital controller. In this paper we

describe control of a MS-150 servo system using LabVIEW 8.2

and PC based digital controller. A number of control algorithms

are implemented in LabVIEW. It includes PID control, cascade

control, optimal state control and a fuzzy logic control. System

identification, addition of integral action to the optimal state

controller and an estimator are also discussed in the paper.

Experimental as well as Matlab/Simulink based simulation results

are presented in the paper. The PD type fuzzy logic controller

shows the best overall control performance.

Index Terms—Control systems, Digital control, Fuzzy logic

control, Comparison of controllers

I. INTRODUCTION

Proper implementation of control systems requires the sound

understanding of behavior of the underlying system. In this

work the procedure to conduct system identification and test

control algorithms are discussed. To avoid the problems of the

open-loop controller, control theory introduces feedback. A

closed-loop controller uses feedback to control states or

outputs of a dynamical system. MS150 Modular Position

Servo System is used as the test stand, which is a unique

medium for study of the theory and practice of automatic

control systems. A block diagram of the system is depicted in

figure 1. A desired position should be reached in this series of

tests under some control design constraints. LabVIEW is

employed as rapid prototyping software. LabVIEW is a

platform and development environment for a visual

programming language “G”. LabVIEW is commonly used for

data acquisition, instrument control, and industrial automation

on a variety of platforms [5].

In section II, a brief explanation of system identification is

presented. Then in Section III, a number of control algorithms

are discussed and their corresponding control diagram is

depicted. A brief discussion and conclusion is finally given.

II. SYSTEM IDENTIFICATION

A. Initial Identification

In order to simulate the system in Matlab/Simulink and design

controllers, a plant model has to be developed. Step response

method is used to determine the system transfer function [1].

In this basic method, second order dynamics are used to

estimate the system parameters. In order to obtain the

calibration equation, different values of voltage and the

corresponding position from the scale on the output

potentiometer are used. The following equation is found and

employed in controllers. Figure 2. shows the calibration graph.

0.39 - voltage22.81 Position ×=

There is no calibration equation for speed and it has been

used as such wherever used as a feedback or state and the

speed has been measured in volts not in RPM. Simulation of

identified system is shown below. This is much similar to the

Digital Control of MS-150 Modular Position

Servo System

Farid Arvani , Syeda N. Ferdaus , M. Tariq Iqbal

Faculty of Engineering, Memorial University of Newfoundland

Email: [email protected]

Fig. 1. Block Diagram of Control System

Servo System

±10V

Position Feedback ±15V Tacho Feedback 0-10V

DA

Q

Lab

VIE

W

Fig. 2. Calibration Graph


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actual system response and this confirms system identification.

B. High-order system Identification

In some experiments all of the three states of the system was

exploited in the controller design. A black-box state-space

representation [2] of the system was identified using Matlab

system identification toolbox. Several data collections were

performed using different excitation signals [3]. LabVIEW VI

used to collect the data sets is depicted in figure 3. Various

identification algorithms including IDGREY, IDSS, IDARX,

IDPROC and IDPOLY are assessed. Using general polynomial

method in the frequency domain gave the best results and after

appropriate filtering and detrending, system identification was

performed. The results were appropriate but were high-order

model. The frequency range used was 0 to 60 rad/s. A model

with two poles, one integrator and one zero is fitted into the

data sets. The initial value for gain of the plant model was

taken from the results of the initial test. Model adequacy was

tested at all models for variance and noise. Finally IDPOLY

model was used for design of the controller. As the identified

system is a continuous-time model, it should be converted to a

discrete time model. Continuous-time IDPOLY model is

e(t) u(t) F(s)

B(s) y(t) +=

where

3.043e004 - s 3.481e004- B(s) =

67.19 - s 22.12 s 20.29 s F(s) 23++=

III. CONTROLLER DESIGN AND SIMULATION

DC Modular Servo system (MS 150) is used to investigate

the feedback control systems. MS 150 system has facilitated us

to investigate different kind of control techniques and

implement simple PID controller to advanced digital fuzzy

logic controller with aid of LabVIEW [4] which have been

performed by controlling the states of the system, which might

be speed and position, current. The objective of this series of

experiments is to control the position of the rotor of motor

using different kind of controllers.

A simple PID controller is so designed and implemented

with position feedback that system matches optimum criteria

of interest. Design of a cascade controller (PID-PI) is

Fig. 2. Comparison of the simulation and actual responses of the identified

system

Fig. 3. Data set collection VI

Fig. 4. System Identification: comparison of the identified and the actual

system


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discussed where it uses the Tacho (speed) feedback in the

inner loop. This sort of controller is used in order to filter

internal disturbances. State space analysis of the system is

discussed. An optimum states controller is designed

considering all state of the system. Then the transfer function

of system is 3rd order and an estimator for the current (one of

states in the system) is implemented in LabVIEW. Design of

an optimum state controller with an integral action and an

estimator is presented. An estimator, for all states is

implemented in LabVIEW since none of the states is

measured. PD type fuzzy logic controller is designed and

implemented. In this experiment, first, the range of error, rate

of error and manipulated input to the system are approximated

using PID controller. Two input, single output FIS is selected

with Sugeno defuzzification methods.

The sampling time for whole experiment is 10ms unless

otherwise specified.

A. PID controller

A PID controller for the system (without any Tacho

feedback) with the design constraints of 10% overshoot was

designed using Z-N closed loop method to determine the

critical gain and period. LabVIEW VI is shown in figure 5.

While Integral and derivative gains were kept zero, the

proportional gain was increased gradually until oscillations

commenced.

Fig. 5. LabVIEW VI for PID controller

Fig. 6. Simulink block and simulation result

Fig. 7. LabVIEW VI for PID-PI controller: first configuration

(A) Inner Loop, (B) Outer Loop


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In simulation (figure. 6), the identified system was used and

the controller was optimized using response optimization.

The simulated response and the actual response are due to

non-modeled non-linearity different from each other. The

optimized parameters did not give the expected results in the

system and made the system unstable.

B. PID-PI cascade controller

The inner loop uses the speed feedback to filter internal

disturbances. Inner loop was tuned using pole cancellation

method however the outer loop was tuned using Z-N methods.

In both cases fine-tuning is performed. A PID noise filter is

implemented in the inner loop to filter the noise in the speed

input. Figure 7 and 8 show corresponding VIs for two different

configurations of the control system. It is noteworthy that the

latter configuration is more appropriate than the former one.

Loops cannot be wired to each other and therefore two

global variables are used to transfer position data and the PID

output the inner loop. Also note that the inner loop is running

one hundred times faster due to the updating cycles of

LabVIEW and is so chosen to compensate for updating the

graphs, allocation of resources to other applications, etc.

As it can be seen from the diagrams there is no speed

calibration equation and it has been used as such in volts.

Figure 9 shows the performance of the controller in front

panel.

Simulation results are depicted below. Response of the

Inner loop and outer loop are shown respectively in figure 10.

The difference between the responses above and the actual

plant is because of the model differences.

C. Optimal States Controller

In this design all states of the system were considered. High-

order system identification results were used. First, the system

(A)

(B)

Fig. 8. LabVIEW VI for PID-PI controller: first configuration

(A) Inner Loop, (B) Outer Loop

(A)

(B)

Fig. 9. Front Panel of LabVIEW VI for PID-PI controller

Fig. 10. Simulink Diagram and simulation results for inner loop (left)

and outer loop (right)

Fig. 11. LabVIEW VI for Optimal States controller


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is simulated using Simulink and the maximum values of states

are observed in order to identify weighting matrix Q. With the

weighting matrices Q (states) and R (Input), optimum state

controller (gain of controller) is designed using DLQR

command in Matlab. An estimator for the current (one of states

in the system) is implemented in LabVIEW since it is not

measured. The interaction between states and the control effort

is also considered in the design [7]. Using plant model, the

following equation was derived to estimate the current (one of

the three states):

)()1()1()1()( 1 kKL

TkI

L

TRkV

L

TkIkI s

a

sa

in

s

aa ω−−−−+−=

Figure 11 and 12 demonstrate the LabVIEW VI and the

performance of the controller.

The results of the simulated system is better than the actual

controller due to unknown initial values of the states and due

to noise that was presented by Matrix K in identification

process. This is concluded from the settling times and

sustained oscillations in the system. It is clear that the valuable

data has been put aside as error and noise. So there is no

wonder that the model is not a good presenter of the actual

plant. Simulink block diagram and the graphs are shown in

figure 13.

D. Optimal State Controller with an Integral Action

Similar to the procedure discussed in previous section an

estimator is designed so that it would be faster than controller

[6]. This estimator estimates all the states of the system since

none of states (except for position) is measured. The system is

simulated with optimum state controller with integral action.

The model was tested for different values but the model was

not proper system and it seems that it is mainly due to

excitation procedure. However the identified system matches

the standard state space model and the provided system

parameters. The other important issue is the selection of Q and

R, where it was relied on the max values of the states to derive

the matrices as it guaranties the stability of the designed

controller in this case.

Fig. 12. Front Panel LabVIEW VI for Optimal States controller

Fig. 13. Simulink Diagram and simulation

Fig. 14. Front Panel LabVIEW VI for Optimal States controller with an Integral

Action


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E. Fuzzy Logic Controller

Design of PD type fuzzy logic controller is presented. First,

the range of error, derivative error and manipulated input to

the system are approximated using PID controller. Two input,

single output FIS is selected with Sugeno defuzzification

methods. Three triangular membership functions are used for

each input and tested for response. The response was

satisfactory and two membership functions were added to

enhance the response of the controller and system.

The membership function fuzzification system for e& is

shown in figure 15 and the rule set is shown in figure 16.

LabVIEW diagram and front panel are shown in figure 17.

Simulation performed in Simulink verifies the sound

performance of the controller.

IV. CONCLUSION

In this brief, different kind of controllers for the MS 150

servo system has been investigated. The cascade control

technique, PID-PI can achieve substantial improvement over

the other techniques and shows better performance in both

overshoot and settling time. An advantage of the cascade

control schemes is that it does not require any estimator and is

relatively easy to implement and robust to disturbances in the

inner loop, which should be faster than outer loop. This may

be a possible drawback of this scheme compared to the

conventional PID control. State space design of controllers

shows poor performance since it uses estimation of states and

initial values and the performance of the system depends

entirely upon the estimator. It becomes even worse when the

estimator estimates all the system states. When it estimates

only one state (current) of the system it shows reasonable

performance although it uses more sensors. It is concluded that

the design of these controllers are heavily dependent on the

proper identified plant model, which is the greatest pitfall

compared to fuzzy controllers.

The PD type fuzzy logic controller shows the best overall

performance. This type of controller does not pose a problem

selecting sampling time as it appear in the case of cascade

controller (in inner loop) and the state space estimator. High-

speed sampling should occur in later cases. Moreover, FLC is

very easy to implement and most of the time it is robust and it

is designed base on the input and output of the plant and does

not require a mathematical model of the plant. However

existence of the model can help better design and the proof of

the stability of this nonlinear controller is hard to perform not

at least for this system.

REFERENCES

[1] K. Ogata, “Modern Control Engineering”, 3rd ed., Prentice Hall 1997

[2] Ljung, L., “System Identification: Theory for the User”, Prentice Hall,

1986

[3] System Identification Toolbox User's Guide, Mathworks, September

2006

[4] LabVIEW PID Control User Manual, National Instruments, November

2001

[5] LabVIEW User Manual, National Instruments, January 2006

[6] K. Ogata, “Designing Linear Control Systems with Matlab”, Prentice

Hall, 1994

[7] A. Tewari, “Modern Control design”, John Wily & Sons, 2002

Fig. 15. Sample Membership Functions for e&

Fig. 17. LabVIEW diagram and its front panel

Fig. 16. Rule set

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Farid Arvani