In industrial process control, two types of control strategies dominate real-world applications: PID (Proportional-Integral-Derivative) control and MPC (Model Predictive Control). Despite the rapid development of modern control theory, these two methods continue to prevail in manufacturing plants worldwide. But why?
🔍 1. Process Control Is Not Just About Algorithms—It’s an Engineering Art
Successful implementation of process control systems often relies more on experienced engineers than on theoretical algorithms. Both PID tuning and advanced control system maintenance require practical know-how.
Many sophisticated control methods (such as robust control or μ-synthesis) are rarely deployed on a large scale because:
Few engineers in the industry are trained in these techniques.
There’s no “economy of scale” in implementation.
The engineering effort outweighs the perceived benefit in many cases.
⚙️ 2. Why MPC Gained Ground Over Classic PID
MPC was developed to address limitations of PID in multivariable and constraint-heavy systems. It not only improves performance but also:
Comes with standardized project implementation workflows.
Has commercial software widely available (e.g., AspenTech, Honeywell, Siemens).
Allows for repeatable deployment with lower skill barriers once the workflow is learned.
In many cases, using MPC is simpler, more flexible, and more cost-effective than constructing complex, cascaded PID control schemes.
📌 Key Advantage: MPC reduces uncertainty in system behavior caused by complex interactions, while PID reduces uncertainty from external disturbances.
🧠 3. Why Not Use More Advanced Algorithms?
In theory, we could use advanced algorithms like robust control, adaptive control, or nonlinear optimization-based controllers. However, they rarely make it to the plant floor unless:
The algorithm can operate without the need for manual tuning.
It can be packaged into a black-box controller that doesn’t require understanding by end users.
It solves a critical pain point where PID or MPC clearly fail.
In reality:
Many large equipment vendors already use custom control logic inside their machines.
These algorithms are intentionally hidden and protected as proprietary IP.
If a method requires tuning or third-party integration, it must be simple and intuitive—otherwise, it won’t be adopted.
🧩 4. Engineering Constraints Shape Control Choices
Industrial process systems are inherently nonlinear, uncertain, and noisy, but their control requirements are often moderate. In such environments, the benefits of sophisticated control algorithms rarely justify their cost and complexity.
Therefore, control engineers naturally prefer:
PID for its effectiveness in single-loop control and disturbance rejection.
MPC for handling complex interactions, constraints, and multivariable optimization.
These methods strike a balance between performance, robustness, ease of use, and cost—which is why they continue to dominate.
✅ 5. Conclusion: Control in the Real World Is About Usability and Trust
Process control is about establishing certainty under uncertainty—not about using the most advanced algorithm.
PID handles dynamic uncertainty from disturbances.
MPC manages structural uncertainty in system interactions.
Unless a new control method can be used without specialist tuning, and solves an urgent industrial problem better than PID or MPC, it will struggle to gain adoption.
That’s why PID and MPC aren’t just control algorithms—they’re the languages of industrial reliability.