Adaptive generalized predictive control using a state-space approach

A state-space approach is described for designing adaptive generalized predictive controls for single-input/single-output CARIMA systems having unknown plant parameters. An observer canonical innovation model is used for designing the j-step-ahead output predictors, in which the direct output method, that is based on using the plant output directly, and the output deviation method, that is based on using the deviatory output, are considered. The unknown parameters included in such long-range predictors are identified by employing an extended least-squares method with an exponential weighting. Two particular control systems are developed, based on these principles, and their performances are compared