A Generalized Predictive Control for CARIMA Systems Using the State-Space Approach

Abstract

A generalized predictive control (GPC) which can be designed in a state-space framework is proposed for single-input/single-output CARIMA systems. It is first shown that, in designing the steady-state 1-step Kalman predictor by applying the innovation model, there are two ways depending on whether the output value of the plant is directly used or not ; one is called here “direct output method” and another is called “output deviation method”. When solving the 1-step predicted estimate of the original output, the latter method can be further classified into two approaches which differ in respect of the interpretation of the predicted estimate for the output deviation. Thus, three different j-step ahead state-space output predictors can be designed for the GPC strategy. Finally, the performances of three GPCs are compared through some numerical simulations.