Brian Jo, Kenneth Parker, Jeff Irlande
Control theory is a useful and practical field of engineering that spans several discipline,
including robotics. One needs to know calculus to fully understand it in depth, but this
class will be an overview of some of the topics that doesn't involve calculus.
How do you stay upright when walking? How does a drone stay balanced in the air? How does
the cruise control in a car work? All of these are examples of control theory in action.
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Open loop vs. closed loop control
| Open loop | Toaster, washing machine, dishwasher |
| Closed loop | Cruise control in a car, autopilot in a plane, thermostat |
Feedback vs. Feed forward
Feedback vs. Feed forward
Block Diagrams
Block diagrams
Sensors
What are some different types of sensors in system? (Accelerometers, cameras,
tactile sensors, pressure sensors). Where can the sensors be placed? (i.e., on a robot
vs. throughout the environment). Sensor errors: Noise, delay/latency, bias, drift. How
this is fixed? Mixture models, Filters (low-pass filter, Kalman filter)
System stability
Stability
Filters
Low-pass, high-pass filters and their uses. Moving average, convolutions, Kalman filter.
PID control
Introduce a few examples of closed loop control systems, e.g., drone
orientation, walking. Introduce idea of error from a target. Brainstorm how you would
use the error to make corrections. Explain basic idea of derivative and integral using
slope (rate of change) and summation.
Model predictive control
MPC