In academic circles, there is no specific algorithm formally defined as “advanced control.” Rather, the term broadly refers to any control strategy beyond the conventional PID (Proportional-Integral-Derivative) framework. In industrial practice, however, “advanced control” most commonly refers to Model Predictive Control (MPC).
This article aims to revisit and clarify some common perceptions around advanced control technologies, especially in comparison to PID, and to highlight when and why such methods become truly valuable in real-world industrial applications.
PID vs. Advanced Control: Misunderstood Dichotomy?
It is often argued that PID control performs poorly for processes with large time delays or significant process lags. However, this viewpoint merits closer examination. At its core, PID is not merely a set of three tuning parameters—rather, it represents a philosophical and mathematical approach to feedback control that has been continuously adapted and refined over decades. In many single-variable control systems with strong causal relationships, well-tuned PID controllers can deliver closed-loop performance comparable to, or even exceeding, that of more complex strategies under certain conditions.
That said, industry does not adopt advanced control methods such as MPC simply because they are “new” or “theoretically superior.” The primary motivation is always practical: to improve efficiency, reduce operational costs, and ensure stable, optimized performance in increasingly complex processes.
Key Advantages of Advanced Control in Complex Industrial Systems
In large-scale, multivariable systems—common in petrochemical plants and refineries—the limitations of traditional PID become more apparent. Below are several areas where advanced control technologies provide clear advantages:
1. Multivariable Coordination
Implementing multivariable control with PID requires decomposing the system into many single-loop controllers, which can lead to control conflicts, tuning difficulties, and maintenance challenges. MPC, in contrast, can handle multiple manipulated and controlled variables simultaneously, maintaining coordination through a centralized model and optimization framework.
2. Flexibility and Modifiability
Advanced control systems allow for flexible configuration of control strategies. Different control objectives (e.g., override control, constraint handling, cascade logic) can be implemented by simply modifying the underlying model or optimization weights—without altering physical connections or hardcoded logic in DCS systems. In contrast, modifying PID-based control logic in a live DCS environment often involves significant risk, complexity, and resistance from operations teams.
3. Constraint Management
In practical operations, every manipulated variable (MV) has limits—ranges that may vary under different process conditions. MPC natively incorporates dynamic constraint handling within the optimization process. When one MV hits a limit, the controller automatically reallocates control effort to other MVs, keeping the controlled variables within their desired range. In PID systems, such constraint handling often requires manual intervention or complex custom logic.
4. Smooth Handling of Control Mode Switching
Processes involving control mode switching, such as priority changes between controllers, handover between different actuators, or dynamic blending of control targets, are hard to implement using traditional PID logic. MPC can smoothly handle such transitions using cost-weighting, prioritization rules, or time-varying constraints.
5. Implementing Complex Control Schemes
Advanced control simplifies the implementation of many control strategies that are otherwise difficult with PID:
- Override control
- Split-range control
- Valve position control
- Constraint prioritization
- Ratio and cascade control
All these can be formulated and adjusted using model-based parameters and optimization weights, greatly reducing engineering effort and increasing maintainability.
Advanced Control is a Tool, Not a Magic Bullet
Despite its advantages, advanced control is not a silver bullet. Its effectiveness depends heavily on proper application, process understanding, and maintenance. Ironically, the flexibility and configurability of MPC can lead to poor results if the underlying control objectives and process dynamics are not well-understood.
Many MPC implementations underperform in industry not because the algorithm is flawed, but because the engineers deploying it did not fully grasp the process, the constraints, or the true control objectives.
As a Ph.D. researcher with a focus on Model Predictive Control, I’ve come to realize that technical sophistication alone does not guarantee industrial success. The reason MPC can be widely adopted in large chemical plants lies not in its novelty, but in its ability to solve real, recurring pain points more effectively than traditional methods—when used correctly.
A Word of Caution Against Blind Faith in “Advanced” Tools
The same lessons apply to other modern control technologies, including AI-based control and data-driven optimizers. Tools should empower, not replace, engineering understanding. Misapplying an advanced tool can be worse than using a simple one properly.
In control engineering, there is no universally “better” algorithm—only the most appropriate one for the context. A successful solution must balance:
- Control performance
- Implementation cost
- System complexity
- Operational maintainability
Therefore, we must treat all widely used control methods, whether PID or MPC, with appropriate respect and careful study. Blindly promoting the superiority of an algorithm without acknowledging its limitations, integration costs, or the boundary conditions of its applicability is scientifically unsound and engineering-wise unhelpful.
Conclusion
Advanced control techniques like MPC offer powerful capabilities, especially in complex, multivariable, and constraint-heavy industrial systems. However, these tools must be applied with deep process knowledge, clear understanding of control objectives, and realistic assessments of cost-benefit tradeoffs.
Let us not worship algorithms, but rather strive to understand the problems we are solving. In the end, it is not the complexity of the tool, but the clarity of our thinking that determines the success of a control strategy.