1. Field of the Invention
This invention relates to control systems and, more particularly, to methods of driving dynamic and steady-state behavior of a process toward more optimum operating conditions.
2. Discussion of Related Art
Multivariable predictive control algorithms, such as DMCplus™ from AspenTech® of Aspen Technologies, Inc. or RMPCT from Honeywell International Inc., are a combination of calculations to drive the dynamic and steady-state behavior of a process toward a more optimum operating condition. The steady-state algorithm used in the control scheme is most commonly a linear program (LP), but sometimes is a quadratic program (QP). For small problems, understanding the LP or QP solution is relatively simple. Two-dimensional problems can be visualized on a paper and demonstrated to an operator to gain understanding of the process. With some detailed modeling background, engineers and well trained operators can understand medium-sized problems (less than 10 dimensions). However, larger, more interactive problems, often require offline simulation. This can take a significant amount of time to understand, even qualitatively.
Typically, an operator of a multivariable predictive controller (MPC) can observe current constraints and may have access to an open loop model of the process. However, to fully understand the constraint set relief, the operator would need a detailed understanding of the process model and the ability to trace independent and dependent relationships through the model. For that, an offline simulation or analysis tool is required. Otherwise, the operator cannot know how much to change a constraint or which constraint is the next to become active.
One concept for an offline simulation uses a matrix pivot in which unconstrained manipulated variables (MVs) are swapped with constrained controlled variables (CV). The constraints become “independents,” and the unconstrained variables become “dependents.” The matrix pivot can be symbolized as follows:
      [                                        y            1                                                            y            2                                ]    =                              [                                                    A                                            B                                                                    C                                            D                                              ]                ⁡                  [                                                                      x                  1                                                                                                      x                  2                                                              ]                    ⁢                          [                                                  x              1                                                                          y              2                                          ]        =                  [                                                            A                                  -                  1                                                                                                      -                                      A                                          -                      1                                                                      ⁢                B                                                                                        CA                                  -                  1                                                                                    D                -                                                      CA                                          -                      1                                                        ⁢                  B                                                                    ]            ⁡              [                                                            y                1                                                                                        x                2                                                    ]            
However, this approach does not provide quantitative answers as to how much any operator change will affect the controller solution.
There is a need for a simple utility that can analyze past or current dynamic matrix control (DMC) solutions of any-sized problem, in real-time, to provide an operator meaningful, quantitative instructions for DMC controller constraint relief.