We hear the term “closed-loop control” all the time—especially in automation, motion control, and servo systems. But what does it actually mean? And more importantly… why do engineers care so much about whether a system is open-loop or closed-loop?
At its core, closed-loop control is a simple idea: measure what happened, compare it to what you wanted, and automatically correct the input to hold a desired setpoint.
That feedback step is what turns a “set it and hope” process into something that can hold a target even when real life gets in the way—changing loads, temperature drift, friction, wear, voltage sag, or airflow shifts.
If you’ve ever watched a thermostat “hunt” around a temperature, or a servo axis land precisely on a commanded coordinate, you’ve seen closed-loop control doing what it does best: correcting reality until it matches the target.
In this article, we’ll break closed-loop control down into plain terms: what it is, how it works, and how it compares to open-loop control. Then we’ll zoom in on the practical side—performance traits, tuning, and how closed-loop feedback is actually implemented in industrial systems like servo drives.
What is a closed-loop control system?
A closed-loop control system is a control system whose action depends on the measured output through a feedback path. This allows the system to automatically regulate a process variable to match a reference input (setpoint).
In a closed loop, a sensor or transducer measures the output (or a function of it). That measurement returns as a feedback signal, and the controller computes an error signal from the difference between the setpoint and the actual output.
The controller then drives the actuator to influence the plant/process and reduce that error. Because the loop continuously corrects itself, closed-loop control is also called feedback control, and it is the default choice when accuracy, repeatability, and disturbance rejection matter more than simplicity.
Why do closed-loop systems matter?
Closed-loop systems matter because feedback lets a controller correct disturbances and drift in real time, keeping performance stable even when the environment isn’t.
Loads change. Temperatures wander. Friction increases. Supply voltage sags. A well-designed closed loop detects these deviations and compensates, making the output repeatable and less sensitive to outside conditions.
That reliability is exactly why closed-loop control is everywhere in modern automation. Digital controllers—whether microcontrollers, PLCs, or the processors inside an ADVANCED Motion Controls servo drive—can read multiple sensors and coordinate outputs faster than any human operator.
Closed-Loop vs. Open-Loop Control
Closed-loop control uses feedback from the output to adjust the control action. Open-loop control does not. That one sentence is the whole difference—but it explains a lot.
An open-loop system follows a command schedule whether or not the output matches the target. For example, a basic heater might run for “10 minutes every hour.” It might work on a mild day, but it won’t adapt when the room is colder or a window is left open. A closed-loop system measures the actual temperature and runs the heater only until the setpoint is reached.
The Industrial Risk of Open-Loop
Zooming out from thermostats to machinery, the difference becomes critical. In open-loop motion control, the controller assumes the commanded move happened. If an axis binds, slips, stalls, or loses steps, the program keeps going anyway because there is no feedback saying, “we didn’t get there.”
This is where open-loop failure becomes a safety issue. The next tool move might be based on a position that only exists in software. This discrepancy can lead to crashed tooling, gouged parts, broken fixtures, and mechanical collisions.
Closed-loop control adds sensors and tuning effort, but it is the standard path to accuracy and robustness. If the load changes or an axis lags, the feedback signal shows the deviation and the controller corrects it—or triggers a fault before damage occurs.
How does a closed-loop control system work?
A closed-loop system works by measuring the output, comparing it to a setpoint, and driving corrective action based on the resulting error.
The key “thinking point” in the loop is the comparison element—often called a summing junction—where the setpoint and the feedback measurement are algebraically combined.
The canonical relationship is:
$$Error = Setpoint – Actual$$
- If the output drops below the setpoint, the error becomes positive, and the controller increases the input.
- If the output rises above the setpoint, the error flips sign, and the controller backs off.
The payoff is disturbance correction. If a disturbance pushes the output away from the target—like a sudden load increase on a motor—the sensor sees the deviation immediately, and the controller compensates until the output returns within bounds.
How is the feedback loop closed inside a servo drive?
In the context of motion control, the servo drive is the “brains + muscle” package. It reads feedback, computes error, and pushes torque until the error shrinks to zero.
At ADVANCED Motion Controls, we design our drives using a Nested Loop architecture. Most servo systems don’t run just one loop—they coordinate three, each focused on a different variable and time scale:
- Current (Torque) Loop (Innermost, Fastest): This loop controls the motor current to produce the commanded torque. It must be extremely fast to handle the electrical dynamics of the motor windings.
- Velocity Loop (Middle): This loop controls speed. It uses a speed estimate (often derived from encoder feedback) to command torque. If the load increases and speed drops, this loop commands more current to compensate.
- Position Loop (Outermost): This loop compares the commanded position to the measured position. It generates velocity commands to eliminate “following error.”
So how does the servo drive “push harder” when the load changes? It adjusts the average motor voltage and current delivered by the power stage, commonly through PWM (Pulse Width Modulation) switching.
If the axis slows under load, the feedback shows the speed drop, the error increases, and the drive responds by commanding more current (more torque) until the target speed is restored. This robustness is the primary advantage of servo control over stepper or open-loop systems.
What is Dual Loop Control?
Standard servo systems use a single feedback device (usually on the motor) for all three loops. However, in high-precision applications, Dual Loop Control offers a significant advantage.
Dual Loop Control uses two measurement points to control one axis:
- A Motor Encoder for the velocity loop (stability).
- A Load-Mounted Linear Scale for the position loop (accuracy).
Why split it?
Because the motor and the load are not always the same thing. Belts stretch, couplings twist, and gears have backlash. A motor encoder can report perfect rotation while the load is actually lagging behind due to mechanical compliance.
With Dual Loop Control, the inner velocity loop stays tight and smooth using the motor feedback, while the outer position loop closes on the linear scale. This ensures the controller keeps driving until the actual load reaches the target, not just the motor shaft.
Tuning a Closed-Loop System
Tuning is the process of selecting controller parameters (like P, I, and D gains) so the loop meets performance targets without going unstable.
- Define targets: Specify tolerances for steady-state error, overshoot, and settling time.
- Identify the plant: Understand what you are controlling (inertia, friction, resonance).
- Set initial gains: Start conservatively. High gains reduce error but increase the risk of oscillation.
- Validate: Test under worst-case loads and disturbances. A loop that is stable in the air might oscillate when coupled to a heavy load.
The biggest engineering risk in closed-loop control is instability. Too much gain or too much delay (latency) can cause the system to self-oscillate. Proper tuning finds the “Goldilocks” zone—stiff enough to reject disturbances, but damped enough to remain stable.
Conclusion
Closed-loop control is fundamentally simple: measure output, compute error, and correct input. Yet, that single idea enables the precision automation we rely on today—from thermal systems to multi-axis robotics.
While it comes with increased complexity in sensors and tuning, the benefits of accuracy, repeatability, and disturbance rejection make it indispensable. Whether you are tuning a PID loop or commissioning a multi-axis servo system, the principle remains the same: trust the feedback, but respect the physics.
