If it were possible to get on with the whole business of multivariable control without the whole business of modeling, the implications for process automation would be far-reaching. But little is yet known about what a model-less multivariable controller might actually look like (it looks a lot like a model-based multivariable controller) or how it might work (it's less complicated). This column discusses the circumstances that give rise to the idea of model-less multivariable control, while a later article (Part 2) will provide an engineering look at an actual model-less multivariable controller design.
Manual multivariable control
Multivariable control is usually considered a product of the computer age, but nearly all processes are multivariable, and multivariable control has always been with us. In the pre-computer age, multivariable control was carried out by the operating team, who adjusted controllers and valves manually to keep related process variables within constraint limits and at economic targets. This basic approach to managing the multivariable nature of most processes remains a prominent aspect of plant operations today, whether in lieu of, or in conjunction with, modern automated multivariable controllers.
That's a step on the path to model-less multivariable control – to reflect that it's always been with us, albeit in manual mode. Ergo, automated model-less multivariable control should also be possible, and should bring all the benefits of timeliness and consistency that normally accrue with any automation, even if it doesn't bring 100% of the benefits deriving from the use of models (although that is by no means being conceded at this point).
Model-based multivariable control (MPC)
With the advent of computers in process control, it became possible to automate multivariable control, with (as mentioned) obvious potential to improve the quality of constraint control and optimization. Industry, armed with promising model-based theory and new-found computer power, opted early on for the model-based path. But several associated assumptions have played out unexpectedly (1):
- Nobody foresaw that achieving "model fidelity" (a combination of accuracy and durability) would be so elusive. In retrospect, this can now be seen as having the same root cause that has always plagued single-loop tuning – the process disturbances we seek to control often alter the very models (or tuning parameters) we employ to control them. (In Part 2, we will see a novel control algorithm that is inherently adaptive to dynamic changes in process gain.)
- Nobody had yet realized that "error minimization," industry's de facto control performance criteria, would prove inappropriate for many multivariable control purposes. For high-level constraint control and optimization, less aggressive and more careful control action, with an eye to managing risk and preserving process stability, is usually preferred from an operations standpoint (Figure 1).
- And nobody realized that the "big matrix" design approach would net many inappropriate and ultimately unwanted models that serve to compound these problems and compromise the control solution much more than they strengthen it. MPC engineers have instinctively trimmed matrices over the years, but few have yet realized that the sensible bottom to this trend is to use primarily the variables and models that have historically been employed and are already proven in prior manual operation, i.e. the "small matrix" design approach (2).
The general consequence of this—of industry's commitment to a technology that has not matured as expected—has been stasis. Instead of the cost and complexity of MPC coming down, they remain high. Instead of performance improving, it remains low. And instead of evolving a more affordable and agile tool, suitable to the everyday needs of a plant operating environment, MPC remains an inflexible and taxing technology to own. Meanwhile, countless medium-range multivariable control applications throughout industry remain unfulfilled for lack of such a tool.
Acknowledging a degree of reality in this view—that due to the structural nature of the limitations, MPC is unlikely to soon become cheaper, easier or more effective, and that meanwhile a large share (perhaps the lion's share) of advanced control applications remain out of reach for lack of an appropriate tool—is another step on the path to model-less multivariable control.
Model-less multivariable control
It is this combination of circumstances—the attractive prospect of a common-sense, model-less multivariable control technology deriving from historical manual methods, juxtaposed against the experience of expensive, difficult and often underperforming model-based technology—that compels the end user to consider the former. However tantalizing the prospect of dispensing with models may be, it is the long road of experience, not the shortcut of simplification, that compels one to consider the model-less path.
Upon embarking on this path, one is initially apprehensive that it may become an exercise in pure compromise, as less perfect methods are contrived to overcome the lack of models. But the results of an initial prototype design are encouraging. Taking into account the following design criteria, model-less technology emerges as a potentially viable alternative technology, rather than a compromise technology, standing on many of its own merits:
- Glean the wisdom and experience reflected in historical manual multivariable control methods.
- Find technically sound alternatives to the emerging complex lessons of model-based multivariable control performance (as given above).
- Achieve the theoretical and practical high level of multivariable control and optimization performance as historically promised by model-based technology.
- Provide a more affordable, agile, scalable and robust multivariable control tool to meet the needs of industry.
Figure 2 is a simplified top-level view of a prototype model-less multivariable control algorithm. In place of detailed models, model-less technology uses only gain-direction (the most fundamental and reliable aspect of models). Instead of complex math, it uses logic to determine the direction of variable moves. Move rates, rather than being dynamically calculated, are preselected based on safety, procedures and experience. And a novel technique called "rate-based control" (RBC) is used to complete the move sequence in a manner that lands controlled variables on constraint limits and optimization targets without overshoot or oscillation. This approach captures the above design criteria. For more information, look for Part 2 in the February issue.
- Multivariable Control Performance, InTech, July-August, 2014.
- Small matrix model-less multivariable control, Chemical Engineering, February, 2014.