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PID Control 33-100 Analogue/Digital Servo Mechanism


The objectives of the laboratory report are familiarizing oneself with the servo mechanism, having a first had experience with encoders and sensors as well as Simulink real-time window targets and exploring the PID (Proportional, Integral and Derivative controls) speed and positional controls of the servo mechanism using the Simulink real-time control.

For the purposes above, the report explores the behavior of the most common feedback loop mechanism of the Proportional-Integral Derivative (PID) under varying conditions and the way the device measures and minimizes error in control systems. Accuracy is paramount to quality engineering. In addition to that, speed and precision are the best qualities of a good engineering control system. For these reasons, exploring the behavior of the PID under varying states and the way it measures and minimizes the errors can help engineers make a device with the best efficiency and accuracy.

The Proportional Integral Derivative is the best feedback controller and it is very reliable. However, in the presence of noise, the PID feedback controller does not work at optimum standard and fails to deliver with accuracy. For that reason, improvisation of the device to work under all circumstances is a high priority as control systems in production plants produce extreme noise that can hamper the operations of the PID device leading to errors that will not be detected in the measurements. For clarity, in noise environments, there will be unnecessary changes to the loop’s Controller Output (CO). In addition to that, the noise causes the Final Control Element (FCE) to wear out hence affect the reliability of the information from the feedback loop.

 The PID has several applications though, for instance, in Neutralization pH Control systems, Furnance Temperature Controls, as well as in Batch Temperature Controls among other many uses.

To meet the objectives set above, the laboratory report explores the behaviors of the Proportional Integral Derivative loop feedback control by examining a feedback servo mechanism fitted with a PC that has 626 PCI sensory interfaces and a mains split power supply of 15 Volts. Fluctuations of the voltage will lead to varying error measurements and make the PID correct the control system by adjusting to meet the demands of the system. The above explanation constitutes the proportional, integral and derivative responses that stem from the PID theory that explores the operations of the device.

For simplicity and clarity, the results and findings are in hierarchical order following the differences of the voltage. In that order, comparison becomes easy. In addition to that, explaining the discrepancies between the data is simple as in this report; all the instants have a picture to prove the assertions. The findings agree with the universal PID theory that explores the proportional response, the integral and derivative responses at length and seeks to draw a conclusion on how best the device can work to eradicate errors in control systems. However, the theory does not detail possible measures that one can apply to make the device operate under noise environments.

I recommend that engineers need to be very careful when using the PID loop feedback control in systems that produce noise.

Keywords: PID, Feedback, Control systems, Errors, Response


A Proportional Integral Derivative controller (PID) is a standard loop feedback mechanism that engineers use in industrial control systems. In many companies with furnace temperature controls and even Neutralization pH controls, use the PID devices as they are easy to employ and are compatible with many unit controls except the ones that produce noise as they affect the efficiency of the systems. In tuning the PID device or rather setting the optimal gains for the proportional response, integral and derivative responses, an individual should ensure that the device is at the best of conditions and no possible voltage leakages can cause disruptions to the systems.

The proportional-integral derivative controller works by calculating an error. In most cases, the error represents the difference between a measurement of a process value or variable and the actual point variable that the operator sets as their control or reference value. The controller will in most instances try to reduce the error by adjusting the control inputs of the process of the control system of a particular machine.

In the proportional control response chamber, the output in most cases varies with the distance from the target point. For the integral control response section, the output variables vary because of how long one is taking to reach the target set value. In addition to these, the derivative control center ensures that the output varies with respect to changes in the errors with a greater error than the set value resulting to a greater response than a small change in the error.

The product of the error measurement and the gain constitutes the proportional factor. The larger the proportional factor than the set point value, there will be a great output stemming from the proportional variable. For some cases, setting the proportional gain very high makes the controller to fluctuate the set point hence results in oscillations of the control systems. However, in cases where the error is too small, the loop output fades and becomes almost negligible. Integral factors behave like a store where the loop stores the error values. For these reasons, the proportional eliminates errors; the integral gets rid of accumulating errors while the derivative section eradicates recurrence of errors with reference to the nature of the error from a previous record.

Materials and Method

The requirements for the experiment and laboratory report are

  1. Feedback 33-100 servo mechanism
  2. PC with sensoray  626 PCI interface
  3. 15 Volt split power supply


The experiment is in three sections,

  1. The Proportional control
  2. The Integral Control
  3. The Derivative control

The Proportional-Integral-Derivative Control

  1. Set the gain to a maximum value for instance 20.
  2. Then, ensure the error is positive by opening the blower window between 50 and 90.
  3. Set the gain to 5 and set integral time to be 2 seconds.
  4. Ensure the error is both positive and negative by opening the blower window to between 50 90 and then between 50 and 10.
  5. Using a gain of 3 and an integral time of 3 seconds, observe the results with a window blower opening of between 50 and 90 and then between 50 and 10 for the negative error values.
  6. Determine the value of the proportional gain, Kp by setting the derivative gain, Kd = 0.
  7. Determine the value of the derivative gain, Kd,so that the value of the KdKhw= 50 N/ (m/s)3.
  8. Set up the values of Kd and the integral gain, Ki to be zero, and using oscillations of about 50 to 90 and then 50 to 10, record the errors from the loop feedback controller.
  9. Determine the value of the integral gain Ki by setting KiKhw = 7500 N/(m-second)2.
  10. Increase the integral gain by a factor of four, observe the error, and then record your observations.
  11. Set the data acquisition for the encoders and for the commanded positions and then record the data after every 5 servo cycles.
  12.  Adjust the Kp and Kd gains and observe the best response. Caution, do not set Kp > 1.2 neither should one set the derivate gain to the range 0.01<Kd<0.05.

Results and Discussion

In the closed loop position control using the incremental encoder data, the PC projects the below image and the results from variations of the Kp value follow

A gain of 1 for the proportional factor causes overshooting as on the below picture

For values lower than 0.01 of the proportional factor lead to a unstable system as on the below picture

When the value Kp = 0.01, the result is as shown below

For a proportional gain range of 0.001<Kp< 0.015, the systems under damps as on the below images,

However, when Kp =0.012, the system over damps

In addition to that, for a Kp value of 0.0012, the systems critically damps

Secondly, the Close Loop Position control using the absolute encoder data and the variations of the values results to the following observations on the PC.

When the Kp value is 1 and 0.7 the system overshoots while a 0.5 value results to a minor overshoot of the errors as below

When the value of Kp =0.88, the controller system critically over damps

In a Close loop speed control using a step input varying between 2 to 4 volts and the tacho signal feedback behaves as below

When the speed varies from 2 to 4 volts, the PC projects the following image

When the Kp value fluctuates between 0.9 and 1.5, the PC projects the image below

In the proportional control, a gain results to a decrease in overshoot and settling time. Integral gains fluctuates the overshoot, settling time and the rise time (Lobsiger, Giulian, & Rexford, 2015, Pg. 84). For that reason, an increase in the integral gain results to a decrease of the rise time, an increase of the setting time and overshoot. A change in the derivative gain reduces rise time and setting time as well as a decrease of the overshoot (Alam, 2012, Pg. 100). The above are the results of fluctuations in a close loop response. Positive and negative errors have impact on the output voltages. Ziegler-Nichlos frequency response approach helps in designing high order PID devices (Mohindru, Sharma, & Pooja, 2015, Pg. 1). The Ziegler-Nichlos parameters aid in fine-tuning the PID systems to ensure good response in the close loop performance. Poor tuning of the PID results to uncontrollable oscillations (Pillay, Govender, 2013, Pg.2). The scenario results to poor close loop occurrences that distort the behavior and the output of the controller system. Tracking the control system performance is important for good industrial performance of the PID devices. For instance, since the existence of noise results to distortion of the output error values, a system engineer can ensure that the Proportional-Integral-derivative loop feedback controller operates under minimal noise. Achieving a god ground for a servo system to acquire a high PID control performance is a dream to most engineers who gear towards minimizing control system errors. In most cases, such a scenario stems from the data from the output process in the open loop case.


Proportional-Integral-Derivative loop feedback controllers have wide applications. The devices help in detecting and minimizing errors in control systems such as Furnace Temperature Control systems. However, their performance is minimal in the presence of noise in their background in the close loop performance is under the influence of fluctuations of the proportional, integral and derivative gains. In addition to that, the variation of the input signals results to either positive or negative errors from the system. However, similar incidences of errors are observable in the open loop state of the PID loop feedback controllers.


Alam, F. (2012). Using Technology Tools to Innovate Assessment, Reporting, and Teaching

            Practices in Engineering Education. Hershey: Engineering Science Reference.

Lobsiger, D. Giulian, P., & Rexford, K. (2015). Electrical Control for Machines. Boston:

            Cengage Learning.

Mohindru, P. Sharma, G., Pooja. (2015). Simulation Performance of PID and Fuzzy Logic

Controller for Higher Order System. Communications on Applied Electronics, Vol.1,  No.7.

Pillay, N. Govender, P. (2014). A Data Driven Approach to Performance Assessment of PID

            Controllers for Setpoint Tracking. Procedia Engineering, Vol. 69, PP. 1130-1137.

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