Feedback Control - Microcontroller Systems - Lecture Slides, Slides of Microcontrollers

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Feedback Control

A Simple Abstraction

• An open loop control system
• Goal/desired state

Controller

The process

Under control

Desired

output

Process

Process output

input

Open Loop Control

• May use expected disturbance
  • Static environment

Controller

The process

Under control

Desired

output

Process

output

Process

input

Disturbance

Predicted

disturbance

Examples

• Putting book on a desk
• Activating an event
  • Start a sensor
  • Play a sound
  • Conduct a scripted movement
• Question: What about inserting a light bulb?

Feedback Control (Closed Loop)

• Include all the parameters as included into the output.

Controller

The process

Under control

Desired

output

Process

output

Process

input

Disturbance

Sensors

Feedback Control

• Another diagram

Controller

The process

Under control

Desired

output

Process

output

Process

input

Sensors

Errors

• Direction (sign)
• Magnitude (distance)
• Frequent feedback is needed
  • Sensor rates can effect response
• Control may not be immediate
  • May be a delay from when you decide to change, and when a change actually occurs!

A Wall Following Robot

• How would you use feedback control to implement a wall-following behavior in a robot?
• What sensors would you use?
• Would they provide magnitude and direction of the error?
• What will this robot's behavior look like?

Sensor Noise

• What happens when there is sensor noise in the system?
• Example:
  • Sensor tells the robot it is far from a wall, when it is close?
  • vice versa?
• How might we fix these problems?

Control Theory

• Studies the behavior of control systems
• Major basic controllers:
  • P: proportional control
  • PD: proportional derivative control
  • PID: proportional integral derivative control

P: Proportional Control

• Q: What happens if the gain is increased?
• A: Loop may go unstable
• Q: What if the gain is decreased?
• A: It takes along time to get close enough to the setpoint.
• Determining the gain: hard problem
  • analytically (mathematics)
  • empirically (trial and error)

Setting Gain

• Determining the gain depends on the physics of the system:
• Analytical approaches:
  • System should be understood well
  • System should be characterized mathematically.
• Trial and error (ad hoc, system-specific):
  • System should be tested extensively.
  • Can be done
    • manually
    • Automatically by the system
• Wrong gain may put the system into oscillation!

Damping

• The process of systematically decreasing oscillation
• Properly damped : Reduces and removes oscillation in a reasonable amount of time.

P: Proportional Control

• Q: What happens if the system is very dynamic?
  • Example: Following another robot
• A: P control does not work well.
  • It senses the present time.
• Q: What happens close to the setpoint?
• A:
  • If gain is fixed: May not work for low errors
    • Leaves some offset
  • If gain is high: Tends to overshoot