1) The document describes modeling a DC motor system using Simulink and modeling the mechanical and electrical portions. It also involves producing translational motion and observing outputs with different spring rates. 2) PID control of the DC motor system is explored through proportional, PI, PD, and full PID control. Effects of changing gains on response are examined. PID tuning in Simulink is demonstrated to automatically determine gains. 3) Tuning the PID controller for the DC motor model in Simulink is shown, with the controller sampled at 0.02 seconds. Gains are automatically determined through PID tuning to achieve reasonable performance and robustness based on the linearized plant model. Output response is observed and commenting on speed

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EC 350 - Control Systems
Laboratory 02 – DC motor Control and PID Control
Model a DC motor using the physical model approach
Use SIMULINK simscape to model the electromechanical system as follows,
 Change PSS-converter block parameter to A (Amperes).
 Change Model Configuration Parameter as, Solver-> ode15s
 RUN the simulation and observe the current produce and comment on it
Model the mechanical portion of the system as follows,

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 RE-RUN the model and observe the output current and comment on it.
Modified the simulation file as follows,
 Change PSS-simulink converter 1 parameter to RPM.
 RE-RUN the Simulink model again and observe the scope 1 and comment on it.

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Produce translational motion as follows,
 What does Wheel and Axle block do?
 RE-RUN the model again and observe the scope 1 and comment on it.
 Disconnect PSS Simulink converter 1 from port W of the Ideal Rotational Motion Sensor and
reconnect it with port A (change the PSS Simulink converter parameter to deg)
 RE-RUN the model and observe the scope 1 and comment on it.
 Change the spring rate of the Translation spring to 100 and 10, RE-RUN the model and observe
the output of the scope 1 and comment on it.
PID Control System
Introduction
Recall the open-loop transfer function of the DC Motor.
𝑃(𝑠) =
∅(𝑠)
𝑉(𝑠)
=
𝐾
𝑠((𝐽𝑠 + 𝑏)(𝐿𝑠 + 𝑅) + 𝐾2)
The structure of the control system has the form shown in the figure below.

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Transfer function for a PID controller has the following form.
𝐶(𝑠) = 𝐾 𝑝 +
𝐾𝑖
𝑠
+ 𝐾 𝑑 𝑠 =
𝐾 𝑑 𝑠2
+ 𝐾 𝑝 𝑠 + 𝐾𝑖
𝑠
A proportional controller ( ) will have the effect of reducing the rise time and will reduce but
never eliminate the steady-state error. An integral control ( ) will have the effect of eliminating
the steady-state error for a constant or step input, but it may make the transient response slower.
A derivative control ( ) will have the effect of increasing the stability of the system, reducing the
overshoot, and improving the transient response.
The effects of each of controller parameters, , , and on a closed-loop system are
summarized as in the table below.
Parameter Rise Time Overshoot Settling Time Steady State Error
Kp Decrease Increase Small Change Decrease
Ki Decrease Increase Increase Eliminate
Kd Small Change Decrease Decrease No Change
The structure of the control system has the form shown in the figure below.

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Transfer function for a PID controller has the following form.
𝐶(𝑠) = 𝐾 𝑝 +
𝐾𝑖
𝑠
+ 𝐾 𝑑 𝑠 =
𝐾 𝑑 𝑠2
+ 𝐾 𝑝 𝑠 + 𝐾𝑖
𝑠
Where = Proportional gain, = Integral gain and = Derivative gain
Use below coding to directly define a PID controller in MATLAB using the transfer function:
Kp = 1;
Ki = 1;
Kd = 1;
s = tf('s');
C = Kp + Ki/s + Kd*s
Alternatively MATLAB's pid controller object can be used to generate an equivalent continuous time
controller as follows:
C = pid(Kp, Ki, Kd);
1. Proportional control
The closed-loop transfer function of a system with a proportional controller is shown below
Use Matlab to answer below questions

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Questions
1.1 Draw a rough sketch of the response of the system to a step reference with different
values of Kp
1.2 Comment on the effect of increasing proportional gain on overshoot and settle time?
2. PD control
The closed-loop transfer function of the given system with a PD controller is shown below
Initially set the value of Kp = 300 and test derivative gain Kd value set to 10. Change your m-file to obtain
the step response of the above system with PD control.
Questions
2.1 Draw a rough sketch of the response of the system to a step reference with different
values of Kd.
2.2 Briefly explain the use of Proportional-Derivative Controller in a particular system?
Comment on the effect of increasing derivative gain (Kd) on overshoot and settle time?
3. PI control
The closed-loop transfer function of the given system with a PI controller is shown below
Initially set the value of Kp = 30 and test integral gain Ki value set to 70. Change your m-file to obtain the
step response of the above system with PI control.
Questions
3.1 Draw a rough sketch of the response of the system to a step reference with different
values of Ki.
3.2 Explain the effect of increasing Ki in the particular system?

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4. PID control
The closed-loop transfer function of the given system with a PID controller is shown below
Now modify the above m file as = 350, = 300, and = 50 to obtain the step response of the above
system with PID control.
Questions
4.1 Draw a rough sketch of the response of the system to a step reference with different
values of Kp, Ki and Kd?
 Use the Matlab command “stepinfo” to obtain the precise characteristics of the step response.
4.2 Comment on the effect of Kp, Ki and Kd parameters on the system?
PID control design with Control System Toolbox

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>> pidtool(sys)
 Change the controller type P, PI, PID and obtain the Step Plot and comment.
 From the Show Parameters, obtain the PID gains and characteristics of the system.
 Change the Response Time and Phase Margin and see what happened to the Step Plot.
Tune PID controller for a DC Motor in MATLAB SIMULINK
Consider an example of designing a PID controller for a dc motor. The model of a closed loop system
uses the new PID Controller block. This block generates a voltage signal driving the dc motor to track
desired shaft rotation speed. In addition to voltage, the dc motor subsystem takes torque disturbance as
an input, allowing us to simulate how well the controller rejects disturbances.
Open the CSlab2.slx

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The PID controller is a discrete-time controller running at 0.02 seconds (the red color shows the sampling
time in the model). Let’s now look at the dialog of the PID Controller block. In the upper half of the dialog
we specified basic configuration of the PID controller: type (PID, PI, PD, P, or I), time-domain, integration
methods, and sample time. In the lower part, we specified PID controller form and gains (shown at default
values).
Block documentation provides detailed information about the block and all its parameters.

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Check the scope of the PID block
PID Tuning
Our first task is to tune the PID controller. Pressing the “Tune…” button in the PID Controller block
dialog, we launch PID Tuner, which linearizes the model at the default operating point and automatically
determines PID controller gains to achieve reasonable performance and robustness based on linearized
plant model.

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Update the PID Tuner
Check the PID controller values again

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Then again RE-RUN the Simulink model and observe the PID scope and comment on it.
 How to make the system faster?
 What are the drawbacks of that method?