BICAUSAL REPRESENTATIONS AND MULTIVARIABLE GENERALIZED PREDICTIVE CONTROL
A change of basis from the standard to an eigenvector set has the effect of decomposing a multivariable problem into a set of characteristic scalar sub-problems. Conformal mapping arguments can be constructed to show that satisfactory characteristic subsystem performance is a prerequisite for overal...
| Main Authors: | , |
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| Format: | Journal article |
| Language: | English |
| Published: |
1991
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| Summary: | A change of basis from the standard to an eigenvector set has the effect of decomposing a multivariable problem into a set of characteristic scalar sub-problems. Conformal mapping arguments can be constructed to show that satisfactory characteristic subsystem performance is a prerequisite for overall satisfactory system performance. With this motivation, earlier work proposed a generalization of the Generalized Predictive Control to the multivariable case. The approach was based on causal characteristic representations and this implied some limitations on the accuracy of the method in the case of plants with unstable branch points. In this paper we show that, under some mild conditions, the characteristic subsystems admit a bicausal representation which can be made as accurate as desired. Bicausality does not constitute a problem in predictive control and a suitable multivariable self-tuning algorithm is proposed. The superiority of the derived results is demonstrated by means of a design study. |
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