Early and current self-tuning algorithm formulations were generally based on the assumption that the process model under investigation is linear within a certain operating point. A standard Recursive Least-Squares (RLS) estimation algorithm was used to update the model parameters whose data input and output were normally fed through a filter with adequate characteristics. One of the most popular themes belonging to this class of adaptive controllers is that of generalized predictive control (GPC). Classified in the category of long-range predictive controllers (LRPC) its control law stems from the minimization of a cost function over a horizon which spans that used by the RLS algorithm (one step ahead). This paper describes a new approach which derives the same model parameters using extra filtering provided by an identification objective similar to the one used for control derivation. Already successfully applied in real-time to a SISO control system by its original authors, the scheme, known as long-range predictive identification (LRPI), is applied here to a nonlinear multivariable anaesthetic model in combination with generalized predictive control with feedforward (GPCF) and multivariable GPC using a P-canonical form for the process model, and its performance is assessed.