Abstract
A dynamic model of the process is the basic requirement for the design of predictive controllers, and the model is first identified using plant input and output data. Using the process model, predictor matrices are constructed (for example, the dynamic matrix constructed using step response coefficients in DMC [1, 2]), as discussed in Chapter 6. The predictor matrices are used to obtain multistep ahead predictions of the process output(s) and used in the controller design. However these predictor matrices can be directly obtained from the input/output data using subspace matrices, which eliminates the intermediate step of parametric process model identification, providing a means for designing a predictive controller in the generalized predictive controller (GPC) framework (e.g. [16]) or the model predictive control framework, without first identifying a parametric model.
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