Module 4 Lecture Outline: Introduction to PID

  1. History of the first PID controller:
    1. It was used for automatic ship steering
    2. It was the first “governor design”
    3. The goal was stability not general control
    4. It was based on information on current error, past error and the rate of changes taking place.
  2. Examples of using data for past, present and future corrections
    1. Losing money
    2. Making money
    3. Heating a home
  3. PID Controllers
    1. Proportional-Integral-Derivative Controller
      1. Control loop feedback
      2. Used in Industrial Control Systems
      3. Calculate the error value of a desired set point
      4. Minimizes the error by adjusting the process control outputs
    2. PID Controller Algorithm
      1. 3 separate parameters
      2. Called 3 term control
      3. P= Proportional: Depends on the present error rate
      4. I= Integral: This is the data from the accumulation of past errors
      5. D=Derivative: Prediction of future errors based on the rate of change of the variable
      6. The weighted sum of these 3 actions are used to adjust the process via a control element
      7. Examples of a controlled element
        1. Control valve
        2. Damper
        3. Power supplied to a heating element
      8. An example of a closed loop element
        1. The Process
          1. A container with water needs to be a desired temperature
          2. Hot and cold water are mixed to create the desired temperature
          3. The temperature is adjusted by mixing hot and cold water until the temperature stabilizes
          4. Process variable: Sensed water temperature
          5. Manipulated variable: input of water and output of water, controlled by the PID controller
          6. Error: The difference in measured temperature and the set point. (Is the output water too hot or too cold?)
        2. Step by Step Process
          1. Measure the temperature of the water. (PV)
          2. Calculate the error between measured temperature and desired temperature
          3. The controller determines how much change is need for the taps to adding hot and cold water. (MV)
          4. Taps can be adjusted from really cold to really hot
          5. If the water is not heating up fast enough, the PID Controller may try to speed up the process. More hot water is added. This is called the Derivative action.
          6. As more and more hot water is added, and time goes by, this is an example of Integral Control.
          7. The controller measures how close the measurements are to the Set Point when the system is stable or instable.
          8. The controller may dampen or hasten future disturbances to control the facet temperature.
  4. Tuning
    1. Methods Used:
      1. Manual Tuning
        1. No math calculations required
        2. This is done online
        3. Requires experienced personal
      2. Ziegler-Nicholas Method
        1. Proven method
        2. Done online
        3. Uses some trial and error processes
        4. It is a very aggressive tuning process
      3. Software Tools
        1. Provides consistent tuning
        2. Online or offline
        3. Can use Automated Design System Controls
        4. May include valve and sensor analysis
        5. There is some cost and training involved
        6. Good process models available
        7. There is some math involved
  5. Limitations of PID Controllers
    1. Relies on feedback systems with constant parameters
    2. PID Controllers should not be used alone but with other instruments
  6. Examples of PID Controlled Environments
    1. Gravity Drained Tanks: Open Loop
    2. Gravity Drained Tanks: Closed Loop