Noise in PID Control – Causes, Effects, and Practical Solutions
Introduction
In process automation and industrial control systems, PID (Proportional-Integral-Derivative) controllers are among the most widely used algorithms for achieving stable and precise control. From flow and pressure loops in refineries to temperature control in batch processes, PID remains a go-to method.
However, one common challenge in PID implementation—often overlooked—is signal noise. Unfiltered or excessive noise can degrade the performance of the PID loop, cause actuator wear, reduce efficiency, and lead to poor product quality.
In this article, we’ll break down the causes, effects, and practical ways to reduce noise in PID control systems—backed by 30 years of real-world experience in industrial instrumentation and automation.
What Is Noise in PID Control?
In control systems, noise refers to unwanted or random fluctuations in a measured signal that do not represent actual process changes. When this noisy signal is used as input to a PID controller, it can lead to incorrect and unstable control actions.
📉 Sources of Noise:
- Electrical interference (EMI, RFI)
- Poorly shielded cables
- Ground loops
- Mechanical vibrations
- Sensor or transmitter limitations
- High-frequency process disturbances
Why Noise Affects PID Performance
Each term in the PID controller reacts differently to noise:
| PID Term | Effect of Noise |
|---|---|
| Proportional (P) | Amplifies noise in real-time; causes fluctuations in output |
| Integral (I) | Slowly accumulates noise, possibly causing offset drift |
| Derivative (D) | Highly sensitive to noise; may produce aggressive spikes |
🔍 The derivative term is the most affected because it responds to the rate of change—and noise, by nature, is a high-frequency signal.
Symptoms of Noise in PID Loops
- Erratic control valve movement
- Frequent small adjustments (also known as “hunting”)
- Output instability despite stable process conditions
- Oscillations in trend data (PV and CV)
- Premature actuator wear or damage
- Poor loop tuning outcomes
Key Strategies to Reduce Noise in PID Control
Let’s explore the most effective methods for minimizing noise without compromising responsiveness or stability.
✅ 1. Use a Low-Pass Filter
Digital filters, especially low-pass filters, can be applied to the Process Variable (PV) to smooth out high-frequency noise.
🔧 How It Works:
- Allows low-frequency (true process) signals to pass
- Blocks or dampens high-frequency (noise) signals
Common implementation:
- First-order lag filter:
PV_filtered = α * PV_current + (1 - α) * PV_previous
Where α is a filter constant (0 < α < 1)
⚠️ Don’t over-filter—excessive filtering introduces additional dead time, which can destabilize the control loop.
✅ 2. Limit or Disable the Derivative Term
Since the derivative (D) term amplifies noise, it should be:
- Used with caution, or
- Disabled entirely in noisy environments
✔ Best Practice:
Use PI (Proportional-Integral) control in cases where:
- Noise levels are high
- The process does not require rapid anticipatory action
Alternatively, apply filtered derivative control (a derivative-on-measurement with built-in filter) if D-action is necessary.
✅ 3. Sensor and Instrumentation Best Practices
Noise is often introduced before it reaches the controller. Optimize the quality and installation of your sensors:
🧰 Tips:
- Use shielded and twisted-pair cables
- Implement proper grounding and bonding practices
- Avoid running sensor cables near power lines or motor drives
- Calibrate sensors and ensure proper scaling
- Upgrade to smart transmitters with internal filtering capabilities
✅ 4. Controller Dead Band or Hysteresis
Many PID controllers offer a dead band (or dead zone) setting—this creates a small input range in which the output doesn’t react.
🔍 Benefit:
Prevents small, rapid output changes caused by noise
🧠 Ideal for valve control systems where slight noise-triggered movements can cause mechanical wear or instability.
✅ 5. Use a Moving Average or Exponential Filter
If your controller allows custom logic or scripting, consider implementing a moving average filter:
PV_avg = (PV1 + PV2 + PV3 + ... + PVn) / n
Or an exponential weighted moving average (EWMA) for more responsive smoothing:
PV_filtered = α * PV_current + (1 - α) * PV_previous
✅ More advanced DCS and PLC systems allow you to configure filter types and parameters for real-time control without introducing unacceptable lag.
✅ 6. Optimize PID Tuning Parameters
If noise is present, aggressive tuning can cause instability. Adjust PID values as follows:
| Parameter | Adjustment |
|---|---|
| Gain (Kp) | Lower the gain |
| Integral (Ki) | Increase the reset time |
| Derivative (Kd) | Reduce or disable |
📌 Use auto-tune cautiously. It may interpret noise as valid process dynamics.
✅ 7. Use Control Valve Positioners with Filtering
Smart valve positioners (e.g., from Emerson, Siemens, or Flowserve) can:
- Ignore small fluctuations in signal
- Filter incoming control signals
- Reduce unnecessary valve movement
This minimizes wear and enhances loop stability.
Example: Flow Control Loop with Signal Noise
🔍 Scenario:
A flow loop controlling cooling water has a fast-responding electromagnetic flowmeter. Due to nearby VFD motors, the signal shows high-frequency noise.
⚠ Symptoms:
- Valve output rapidly oscillates ±2%
- Frequent alarms due to excessive valve travel
- Loop tuning becomes unstable
✅ Resolution:
- Applied a 0.5-second low-pass filter on PV
- Disabled derivative term in PID controller
- Introduced a dead band of 0.3%
- Shielded signal cable rerouted away from motor drives
🎯 Result:
- Stable loop behavior
- 70% reduction in valve movement
- Improved operator confidence and product quality
Tools to Help Manage Noise in PID Control
| Tool | Purpose |
|---|---|
| Signal analyzers | View waveform and frequency of noise |
| Digital oscilloscopes | Identify EMI/RFI interference |
| SCADA/Historian trends | Visualize PV fluctuations and response |
| PID tuning software | Tune loops with filtering built-in |
| Smart positioners | Filter noisy control signals at the actuator |
Conclusion
Noise is an unavoidable reality in control systems, especially in electrically noisy environments like industrial plants. However, understanding its effects and implementing the right strategies can significantly improve PID loop performance and system reliability.
✅ Key Takeaways:
- Noise disrupts PID loops by causing false outputs, hunting, and instability
- The derivative term is the most sensitive and may need to be disabled
- Apply smart filtering techniques and sensor best practices
- Adjust PID tuning to be more robust in noisy environments
- Leverage modern tools and diagnostics to troubleshoot and optimize
🧠 Remember: The goal is not to eliminate noise completely—but to reduce its impact on the control loop.