Loop pairing is one of the major concerns when designing decentralized control systems for multivariable processes.Most existing pairing tools,such as the relative gain array(RGA) method,have shortcomings both in measuring interaction and in integrity issues.To evaluate the overall interaction among loops,we propose a statistics-based criterion via enumerating all possible combinations of loop statuses.Furthermore,we quantify the traditional concept of integrity to represent the extent of integrity of a decentralized control system.Thus,we propose that a pairing decision should be made by taking both factors into consideration.Two examples are provided to illustrate the effectiveness of the proposed criterion.
We study the subspace identification for the continuous-time errors-in-variables model from sampled data.First,the filtering approach is applied to handle the time-derivative problem inherent in continuous-time identification.The generalized Poisson moment functional is focused.A total least squares equation based on this filtering approach is derived.Inspired by the idea of discrete-time subspace identification based on principal component analysis,we develop two algorithms to deliver consistent estimates for the continuous-time errors-in-variables model by introducing two different instrumental variables.Order determination and other instrumental variables are discussed.The usefulness of the proposed algorithms is illustrated through numerical simulation.
