Model-less multivariable control in practice

Eliminating the model offers the advantages of model-based control without the headaches.

By Allan Kern, P.E.

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A steady stream of articles since 2004 have cast light on the performance and ownership challenges of model-based, multivariable predictive control (MPC) from a practical operations-oriented perspective1. This body of work has led to the idea of model-less multivariable control as a potentially less expensive and more agile alternative approach to multivariable control. This article, for the first time, presents an actual model-less multivariable controller design that's been implemented on an industry-standard DCS platform and is undergoing laboratory and field testing. (For background, see Part 1, "The path to model-less multivariable control," December 2015, page 14.)

In this article, the terms "model-based technology" and "MPC" refer to conventional, model-based constraint control and optimization technology and products. The terms "model-less technology" and "XMC" refer to the model-less multivariable control concepts developed by the author and to the actual prototype controller.

Model-less builds on model-based

Model-less multivariable control technology carries forward many established concepts from model-based technology, such as the matrix, constraint limits and optimization targets. Where concepts are new or have changed, they're often easier to grasp, such as replacing detailed models with simple gain directions, and using pre-selected move rates based on operational performance criteria. Although model-less technology is new, it's usually easier and more intuitive to understand than model-based technology.

A core concept carried forward in model-less technology is the matrix, which is the natural way to illustrate the multivariable nature of processes and to frame the multivariable control problem, regardless of models. Figure 1 is the matrix for a simulated crude oil distillation unit XMC application. The matrix rows are the direct control variables (DCVs), i.e. variables whose setpoints or outputs are directly adjusted by the multivariable controller (similar to MPC manipulated variables). The matrix columns are the indirect control variables (ICVs), i.e. variables that are indirectly controlled by means of the DCVs (similar to MPC controlled variables). Some commercial products reverse the rows and columns, which is arbitrary (and bothersome).

The biggest hurdle to understanding model-less control is often overcoming the past paradigm that model-based control is the only way the problem can be solved. As we saw in Part 1, manual multivariable control has always been an essential aspect of nearly all industrial process operation, even before computers. Model-less multivariable control concepts borrow heavily from historical manual methods, while meeting the overall design criteria shown in Table I (explained further in Part 1).

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