Parametrization of multi-output autoregressive-regressive models for self-tuning control

Problem of parameterization of multi-output autoregressive regressive Gaussian model (ARX) is studied in the context of prior design of adaptive controllers. The substantial role of prior distribution of unknown parameters on the parameterization is demonstrated. Among several parameterizations a nontraditional one is advocated which • makes it possible to model the system output entry-wise, thus it is very flexible; • models relations among system outputs in a realistic way; • is computationally cheap; • adds an acceptable amount of redundant parameters comparing to the most general but computationally most demanding parameterization which organizes the unknown regression coefficients in column vector.