© The Institution of Engineering and Technology
This study proposes two new techniques, in the framework of set membership (SM) theory, to derive offline an approximation of a given nonlinear model predictive control (NMPC) law. The obtained approximated control laws satisfy input constraints and guarantee a bounded worstcase approximation error. Such a bound can be tuned to obtain a tradeoff between closedloop performance, online evaluation complexity, offline computational burden and memory usage. The presented techniques are suboptimal, since their worstcase approximation error is not minimal; however, they are able to obtain good accuracy with efficient online computation. Both approaches are based on the prior information given by a finite number ν of nominal control moves, computed offline and stored. The first technique relies on the piecewise linear interpolation of the offline computed data, while the second approach is based on the computation of the (suboptimal) upper and lower bounds of the nominal NMPC law, on the basis of the partial information given by a subset of the whole offline computed data. A numerical example and an automotive case study are presented in order to show the effectiveness of the proposed approaches and to compare their performance.
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