Which control strategy uses sensor feedback to minimize the difference between the process variable and a desired setpoint?

Which control strategy uses sensor feedback to minimize the difference between the process variable and a desired setpoint?

• A. Feedback control
• B. Feedforward control
• C. Open-loop control
• D. Model predictive control

Answer: (A)

The idea being tested is using real-time measurements to drive corrective action and shrink the gap between what you want (the setpoint) and what is actually happening. In feedback control, a sensor tells you the current process variable, you compare that to the setpoint, and the controller (often a PID-type) produces a corrective signal to reduce the error. This loop runs continuously, so disturbances or changes in the process are counteracted as they occur. A familiar example is a thermostat regulating room temperature: it measures the air temperature, compares it to the desired value, and turns the heater on or off to bring the temperature back to the setpoint. This is different from feedforward control, which tries to preempt changes using disturbances and a model without relying on the actual output measurement; open-loop control has no feedback at all; and model predictive control uses a model to predict future behavior and optimize control actions over a horizon, though it still relies on some feedback to correct predictions.

The idea being tested is using real-time measurements to drive corrective action and shrink the gap between what you want (the setpoint) and what is actually happening. In feedback control, a sensor tells you the current process variable, you compare that to the setpoint, and the controller (often a PID-type) produces a corrective signal to reduce the error. This loop runs continuously, so disturbances or changes in the process are counteracted as they occur. A familiar example is a thermostat regulating room temperature: it measures the air temperature, compares it to the desired value, and turns the heater on or off to bring the temperature back to the setpoint. This is different from feedforward control, which tries to preempt changes using disturbances and a model without relying on the actual output measurement; open-loop control has no feedback at all; and model predictive control uses a model to predict future behavior and optimize control actions over a horizon, though it still relies on some feedback to correct predictions.