Set membership approximations of Predictive Control laws: The tradeoff between accuracy and complexity

The paper investigates two new techniques, in the framework of set membership (SM) theory, to derive off-line an approximation of a given Nonlinear Model Predictive Control (NMPC) law. The obtained approximated control laws satisfy input constraints and guarantee a bounded worst-case approximation error (i.e. accuracy). Such a bound can be tuned to obtain a tradeoff between closed-loop performance, on-line evaluation complexity, off-line computational burden and memory usage. A numerical example is employed to show the effectiveness of the proposed approaches and to compare their performance.